Scheduling strategies for multi-antenna communications and dual connectivity in wireless networks

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UNIVERSIDADE FEDERAL DO CEARÁ CENTRO DE TECNOLOGIA

DEPARTAMENTO DE ENGENHARIA DE TELEINFORMÁTICA

PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE TELEINFORMÁTICA

ROBERTO PINTO ANTONIOLI

SCHEDULING STRATEGIES FOR MULTI-ANTENNA COMMUNICATIONS AND DUAL CONNECTIVITY IN WIRELESS NETWORKS

FORTALEZA 2020

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Tese apresentada ao Curso de Doutorado em Engenharia de Teleinformática da Universidade Federal do Ceará, como parte dos requisitos para obtenção do Título de Doutor em Engenharia de Teleinformática. Área de concentração: Sinais e Sistemas

Orientador: Prof. Dr. Tarcisio Ferreira Maciel Coorientador: Prof. Dr. Gábor Fodor

FORTALEZA 2020

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Dados Internacionais de Catalogação na Publicação Universidade Federal do Ceará

Biblioteca Universitária

Gerada automaticamente pelo módulo Catalog, mediante os dados fornecidos pelo(a) autor(a)

A64s Antonioli, Roberto Pinto.

Scheduling Strategies for Multi-Antenna Communications and Dual Connectivity in Wireless Networks / Roberto Pinto Antonioli. – 2020.

120 f. : il. color.

Tese (doutorado) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenharia de Teleinformática, Fortaleza, 2020.

Orientação: Prof. Dr. Tarcisio Ferreira Maciel. Coorientação: Prof. Dr. Gábor Fodor.

1. 5G. 2. MIMO IBC. 3. Conexão dual. 4. Técnicas de escalonamento. I. Título.

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Thesis presented to the Graduate Program in Teleinformatics Engineering of the Federal University of Ceará as a partial requisite to obtain the Ph.D. degree in Teleinformatics Engineering. Concentration Area: Signals and Systems.

Aproved on: 30/04/2020.

EXAMINING COMMITTEE

Prof. Dr. Tarcisio Ferreira Maciel (Advisor) Universidade Federal do Ceará, Fortaleza, Brazil

Prof. Dr. Gábor Fodor (Co-Advisor)

Royal Institute of Technology, Stockholm, Sweden

Prof. Dr. Cecilio José Lins Pimentel

Universidade Federal de Pernambuco, Recife, Brazil

Prof. Dr. Richard Demo Souza

Universidade Federal do Paraná, Florianópolis, Brazil

Prof. Dr. Yuri Carvalho Barbosa Silva Universidade Federal do Ceará, Fortaleza, Brazil

Dr. Victor Farias Monteiro

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ACKNOWLEDGEMENTS

First of all, I would like to thank God for guiding my steps, providing me this opportunity and supporting me with strength, patience and peace.

Special thanks go to Prof. Dr. Emanuel Bezerra Rodrigues for introducing me to GTEL and for supporting me during the beginning of my PhD journey. I also thank Prof. Dr. Francisco Rodrigo Porto Cavalcanti and my advisor Prof. Dr. Tarcisio Ferreira Maciel for giving me the opportunity to be part of GTEL team and for all the guidance and support during my PhD. It has been a pleasure working with you since I was an undergraduate student.

I am also very grateful to my supervisors at Ericsson in Sweden - Jonas Pettersson, Mats Nordberg, Gábor Fodor, Pablo Soldati, Göran Klang - for receiving me at Ericsson and giving the opportunity to temporarily be part of the Ericsson team. Thanks to all of you for the long discussions, share of knowledge, patience and support during my internships. It was a great pleasure to me to have worked with all of you.

I also thank my colleagues and friends from UFC, GTEL and Ericsson for the nice study and work environment as well as for the technical and non-technical discussions. Special thanks go to Diego for the availability throughout all this period for helping me with anything I needed.

I am extremely grateful for all the love, support, prayers and caring from my parents, Egidio and Lenilce, brother, Massimo, and family. Thank you for all the sacrifices you have made. I am also extremely grateful to my beloved wife, Fernanda, for all the love, support, understanding and caring.

Finally, I acknowledge the technical and financial support from FUNCAP and Ericsson Research, Sweden, and Ericsson Innovation Center, Brazil, under UFC.43 and UFC.47 Technical Cooperation Contracts Ericsson/UFC. Also, this study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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RESUMO

As próximas gerações de redes móveis deverão suportar uma ampla variedade de casos de uso, para os quais uma gama de requisitos de qualidade de serviço (QoS) precisam ser atendidos. Para atender esses requisitos de QoS, a quinta geração (5G) e gerações futuras de redes móveis devem contar com, entre outros aspectos, comunicações que utilizam múltiplas antenas e a implantação de células pequenas. Para explorar completamente os potenciais benefícios dessas técnicas, o projeto de técnicas eficientes de escalonamento de enlaces sem fio é de suma importância, uma vez que essas técnicas têm uma grande influência no desempenho geral do sistema. Neste contexto, esta tese lida com a concepção de estratégias eficientes para o escalonamento de enlaces sem fio em sistemas com canal de radiodifusão de múltiplas-entradas e múltiplas-saídas com interferência (do inglês, multiple-input multiple-output interference broadcast channel - MIMO IBC) e sistemas com conexão dual (DC). O MIMO IBC é um modelo geral para comunicação no enlace direto em que vários transmissores com múltiplas antenas querem enviar dados simultaneamente para seus respectivos receptores, os quais também possuem múltiplas antenas. O principal desafio no MIMO IBC é projetar filtros lineares para formatação de feixes nos transmissores e receptores tal que a taxa total do sistema é maximizada ao mesmo tempo em que os requisitos de QoS dos usuários são satisfeitos ou que uma certa justiça entre os usuários seja atingida. Neste contexto, considerando a teoria da otimização, essa tese projeta algoritmos centralizados, semi-distribuídos e distribuídos para maximizar a taxa total do sistema considerando restrições de taxa por usuário no MIMO IBC. Em sistemas com DC, os usuários podem se conectar simultaneamente a mais de uma estação rádio base. Neste contexto, abordamos aspectos de escalonamento relacionados a algoritmos de controle de fluxo de dados que definem como os dados são divididos entre as múltiplas conexões dos usuários. A solução proposta é baseada na teoria da utilidade e foca em maximizar a satisfação dos usuários no sistema. Além disso, propomos uma arquitetura inovadora para o escalonamento de usuários e um algoritmo de escalonamento que podem ser utilizados em redes futuras com múltiplas conexões, onde os usuários podem ter até mesmo mais de duas conexões ao mesmo tempo. As soluções desenvolvidas nesta tese são focadas em aprimorar a provisão da QoS aos usuários e são adequadas para implementações práticas.

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ABSTRACT

Future generations of mobile networks are envisioned to support a wide variety of use cases, to which a plurality of strict quality of service (QoS) requirements need to be fulfilled. In order to meet such requirements, fifth generation (5G) and beyond mobile networks are expected to rely on, among other techniques, multi-antenna communications and the deployment of small cells. To fully exploit the potential benefits of these techniques, the design of efficient scheduling solutions are paramount since these techniques largely influence the overall performance of the system. In this context, this thesis deals with the design of efficient scheduling strategies for the multiple input multiple output (MIMO) interference broadcast channel (IBC) and for dual connectivity (DC) networks. The MIMO IBC is a general model for downlink communication in which a plurality of multi-antenna transmitters wish to simultaneously send data to the respective intended multi-antenna receivers. The main challenge in the MIMO IBC is designing linear transceivers that maximize the system throughput while fulfilling per-user QoS requirements or achieving a level of fairness among the users. In this context, based on optimization theory, this thesis designs centralized, semi-distributed and distributed algorithms for rate-constrained sum-rate maximization in the MIMO IBC. In DC networks, users can be simultaneously connected to more than one base station. In this context, we address scheduling aspects of flow control algorithms that command the data split among the multiple connections of a given user. The proposed solution is based on the utility theory and focuses on maximizing the user satisfaction in the system. Furthermore, we also propose a novel and practical medium access control (MAC) scheduler architecture and corresponding scheduling algorithm for future multi-connectivity networks, where users can even have more than two simultaneous connections. The solutions developed in this thesis are focused on the enhancement of the per-user QoS and are amenable to practical implementations.

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LIST OF FIGURES

Figure 1.1 – Possible configurations for MIMO systems with respect to the number of

transmitters and receivers considering cellular deployments. . . 21

Figure 1.2 – Examples of scenarios expected to occur in 5G and beyond systems which can be categorized into the class of MIMO IBC scenarios. . . 22

Figure 1.3 – Scheduling approaches for MIMO IBC systems with QoS requirements. . . 23

Figure 1.4 – Example of deployment scenario of DC composed of one MN and two SNs. Notice that the UE-1 and UE-2 are in SC with the MN and SN-2, respectively, while UE-3 is in DC with the SN-1 and MN. . . 24

Figure 1.5 – Radio protocol architecture for master, secondary and split bearers. . . 25

Figure 1.6 – Summary of the main tasks performed by each of three functionalities needed in DC scenarios supporting split bearers. . . 26

Figure 1.7 – Block diagram for thesis organization. . . 28

Figure 2.1 – Joint link scheduling and transceiver design schemes. . . 36

Figure 2.2 – Performance for MIMO IC and𝑅_𝑢= 2 bits/s/Hz, ∀𝑢. . . 45

Figure 2.3 – User scheduling and sum-rate performances. . . 45

Figure 3.1 – Illustration of penalty values, i.e., −𝛼_𝑢𝑓(𝑑_𝑢), for different values of user priority𝛼_𝑢and 𝑓(𝑑_𝑢)=𝑑2 𝑢. . . 50

Figure 3.2 – Frame structure for Algorithm 7. . . 57

Figure 3.3 – Signaling exchange illustration for a MIMO IBC. . . 58

Figure 3.4 – Convergence of Algorithm 7 for a case where it is possible to satisfy only 4 out of the 6 initial minimum rate requirements in the system. . . 63

Figure 3.5 – Barchart illustrating achieved rates by all possible alternatives of the proposed solution considering the scenario analyzed in Fig. 3.4. The letter ‘x‘ above a certain user index means that the user is assigned with rate equal to zero. . . 63

Figure 3.6 – Performance comparison of five different algorithms (centralized, distributed in alg. [10], WMMSE, proposed distributed with 1 iteration and proposed distributed with 10 iterations) in terms of total sum-rate (dashed lines) and achieved value of the objective function of problem (3.1) (solid lines). . . . 64

Figure 3.7 – Performance analysis for scenarios where 𝑅_𝑢= 0,∀𝑢. . . 65

Figure 3.8 – Performance analysis for scenarios where 𝑅_𝑢> 0,∀𝑢and a feasible solution is guaranteed to exist. . . 66

Figure 3.9 – Performance analysis for scenarios where𝑅_𝑢> 0,∀𝑢and it is not be possible to satisfy all rate demands, thus scheduling is needed. . . 67

Figure 4.1 – Message diagram illustrating how the proposed flow control algorithm works and the signaling involved. . . 80

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Figure 4.2 – User weight (𝑤𝑏

𝑢), service weight (𝑤 𝑡

𝑢) and RAT weight (𝑤𝑢) employed in the

proposed flow control algorithm. . . 82

Figure 4.3 – Combined utility weight for NR SN and LTE MN for different values ofΨ. The combined weight is obtained by multiplying only the utility weights, i.e., 𝑤𝑏 𝑢·𝑤 𝑡 𝑢·𝑤𝑢. . . 83

Figure 4.4 – Network topology adopted for the simulations. A LTE MN is positioned at the center of a three-sector hexagonal grid and one NR SN is randomly deployed per sector. . . 85

Figure 4.5 – Percentage of satisfied UEs. . . 88

Figure 4.6 – Total system throughput. . . 89

Figure 4.7 – Mean throughput of UEs. . . 90

Figure 4.8 – User satisfaction separating the UEs in DC and in SC. . . 90

Figure 4.9 – 5 %-tile and 90 %-tile of UEs throughput. . . 91

Figure 4.10–50 %-tile and 90 %-tile of UEs throughputs considering imperfection in the backhaul link. . . 92

Figure 5.1 – Functionalities, working flow and possible deployments for the Split Respon-sibility Scheduler. . . 98

Figure 5.2 – Detailed illustration of the main procedure performed in the main loop of the proposed distributed algorithm. . . 102

Figure 5.3 – Detailed description of proposed distributed algorithm for split responsibility scheduling in multi-connectivity networks. . . 103

Figure 5.4 – Scenario deployment adopted for the simulations. The blue hexagonal sites have LTE BSs at their centers, while the green hotspots have NR BSs at their centers. . . 104

Figure 5.5 – Performance analysis for proportional fair objective. . . 106

Figure 5.6 – Performance analysis for max-min rate objective considering minimum rate requirements. . . 107

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LIST OF TABLES

Table 1.1 – KPI values expected for 5G networks. . . 19

Table 2.1 – Simulation parameters for both MIMO IC and MIMO IBC scenarios. . . 44

Table 4.1 – Simulation parameters for LTE and NR. . . 86

Table 4.2 – Common simulation parameters for both RATs. . . 86

Table 5.1 – Comparison between the features of the existing scheduling solutions and the proposed Split Responsibility Scheduler. . . 100

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LIST OF ABBREVIATIONS AND ACRONYMS

3GPP 3rd Generation Partnership Project 4G fourth generation

5G fifth generation BB branch and bound BC broadcast channel BLER block error rate BS base station CBR constant bit rate

CDF cumulative distribution function CFN channel Frobenius norm

CoMP coordinated multi-point CSI channel state information DC dual connectivity

DCP difference of convex functions program

DL downlink

eMBB enhanced mobile broadband EPA equal power allocation FCA flow control algorithm FS fast switching

GLB global lower bound HOL head of line

HWCR highest WCR

HWSR highest weighted sum rate IBC interference broadcast channel IC interference channel

ITU international telecommunication union KKT Karush-Kuhn-Tucker

KPI key performance indicator

LB lower bound

LTE long term evolution MAC medium access control

MCS modulation and coding scheme MILP mixed integer linear programming MIMO multiple input multiple output MISO multiple input single output MMSE minimum mean squared error

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mMTC massive machine-type communications

MN master node

MSE mean squared error

MU multiuser

NP non-polynomial time

NR new radio

NSA non-stand-alone

OFDM orthogonal frequency division multiplexing PDCP packet data convergence protocol

PF proportional fair QoS quality of service RA resource allocator RAT radio access technology RB resource block

REG resource regulator RLC radio link control

RRA radio resource allocation RRH remote radio head

RRM radio resource management RRV rate-relaxation variable RSU road side unit

SC single connectivity

SCA successive convex approximation SINR signal to interference-plus-noise ratio SISO single input single output

SN secondary node

SOCP second-order cone programming SRS split responsibility scheduler TTI transmission time interval UAV unmanned aerial vehicle

UB upper bound

UBA UE-BS association UE user equipment

UL uplink

ULA uniform linear array URA uniform rectangular array

URLLC ultra-reliable and low-latency communications V2X vehicle-to-everything

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WCR weighted common rate

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LIST OF SYMBOLS

|𝑎| Absolute value of𝑎 |A | Cardinality of the set A O (·) Complexity order

kak Euclidean norm of vector a

E [·] Expected value

(·)H Matrix Hermitian operator

I𝑎 Identity matrix with order𝑎

arg max

𝑥∈A

{𝑓(𝑥)} Maximum argument𝑥 ∈ A of a function 𝑓(𝑥)

𝐵 Number of BSs

𝐾_𝑏 Number of RBs of BS𝑏

K Set of RBs

𝐾 Number of RBs

𝐿 Packet arrival rate

𝑀(·) RAT utility function

𝑁_R Number of antennas of the UE 𝑁_T Number of antennas of the BS 𝑃_𝑏 Total available power at BS𝑏 𝑄_𝑢 Instantaneous queue size of UE𝑢

𝑅₀ Weighted common rate

𝑅_ref Reference rate

𝑅_𝑢 Minimum rate requirement of the UE𝑢

𝑆 Number of streams

𝑇_tti Duration of one TTI

𝑇_𝑢_,𝑏 Throughput of UE𝑢at BS𝑏

𝑇_𝑢 Throughput of UE𝑢

𝑈dec

𝑢 (·) Utility decrement function UE𝑢

𝑈inc

𝑢 (·) Utility increment function UE𝑢

𝑈_delay(·) User utility function for delay-based services 𝑈_queue(·) User utility function for queue-based services 𝑈_thr(·) User utility function for throughput-based services

U Set of UEs

𝑈 Number of UEs

𝑈(·) General user utility function 𝑉(·) Service utility function

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Γ𝑢,𝑠 SINR on the stream𝑠of UE𝑢

Λ Parameter that controls the shape of𝑉(·) Ω Parameter that controls the shape of𝑈(·) Ψ Parameter that controls the shape of𝑀(·) Υ𝑢,𝑏 Resource share assigned to UE𝑢at BS𝑏

Ξ Packet size

𝛼_𝑢 Scheduling priority weight of UE𝑢 𝛽𝑢 Relative weight of UE𝑢

𝑏_𝑢 BS serving UE𝑢

𝜒_𝑢_,𝑏 Rate allocated to UE𝑢given the set of RBs K_𝑢_,𝑏of BS𝑏 𝛿 Step size for update of dual variables

𝜖_𝑢_,𝑠 MSE of UE𝑢in data stream𝑠

𝛾_𝑢_,𝑘,𝑏 SINR of the link between BS𝑏and UE𝑢in RB𝑘 ^

𝜒_𝑢_,𝑏 Rate allocated to UE𝑢given the set of RBs K_𝑏of BS𝑏

𝜅 Value of step share

𝜆_𝑢_,𝑠 Dual variable for MSE constraint of UE𝑢and stream𝑠

H𝑏𝑢,𝑢 Channel matrix between the BS𝑏serving UE𝑢and UE𝑢

𝜇 Base for exponential function used for modeling𝑔(·) 𝑆_𝑢 Number of streams from UE𝑢

𝑈_𝑏 Number of UEs of BS𝑏

𝜈_𝑏

𝑢 Dual variable for power constraint of BS𝑏serving UE𝑢

𝜔_𝑢 Source data rate of the service consumed by UE𝑢 𝑄_𝑢 Average queue size of UE𝑢

𝜓 Maximum reduction factor

𝜌_𝑢_,𝑘,𝑏 Binary variable indicating if UE𝑢is scheduled on RB𝑘of BS𝑏

U𝑏 Set of UEs of BS𝑏

𝜎2

𝑢 Thermal noise power at UE𝑢

B𝑢 Set of BSs to which UE𝑢is connected to

B Set of BSs

K𝑏 Set of RBs of BS𝑏

K𝑢,𝑏 Subset of RBs of BS𝑏allocated to UE𝑢

𝜏_𝑢_,𝑘,𝑏 Achievable rate of UE𝑢on RB𝑘of BS𝑏 𝜃_𝑢 Dual variable for RRV constraint of UE𝑢

𝜛 Scalar value obtained by observing that𝑔(·) satisfies the superposition property under logarithmic transformation.

𝜚_𝑢 Dual variable for rate constraint of UE𝑢

𝜗 Amount of step shares

m𝑢,𝑠 Transmission filter to UE𝑢in data stream𝑠

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v𝑢,𝑠 Normalized transmission filter to UE𝑢in data stream𝑠

w𝑢,𝑠 Reception filter of UE𝑢in data stream𝑠

y𝑢,𝑠 Downlink received signal by UE𝑢in data stream𝑠

𝜁_𝑢 Amount of bits that arrived to UE𝑢

𝑎_𝑢_,𝑠 Slope of linear approximation function of the MSE upper-bound function for stream𝑠of UE𝑢

𝑏 BS index

𝑐𝑢,𝑠 Linear coefficient of linear approximation function of the MSE

upper-bound function for stream𝑠of UE𝑢 𝑑hol

𝑢 Delay of the HOL packet of UE𝑢

𝑑_𝑢 RRV of UE𝑢

𝑓_queue Filtering constant for average queue size computation 𝑓_thru Filtering constant for throughput computation

𝑓(·) Penalty function for controlling the values of the RRVs 𝑔(·) Upper-bound function for MSE

ℎ_𝑢_,𝑘,𝑏 Channel coefficient between BS𝑏and UE𝑢in RB𝑘

𝑘 RB index

𝑙(·) Link adaptation function

𝑛 TTI index

𝑜(·) Objective function of a certain optimization problem 𝑝_𝑘_,𝑏 Power allocated to RB𝑘of BS𝑏

𝑝_𝑢_,𝑠 Power allocated to stream𝑠of UE𝑢

𝑞_𝑢_,𝑠 Data symbol transmitted to UE𝑢in data stream𝑠 𝑟cur 𝑢 Current rate of UE𝑢 𝑟dec 𝑢 Decremental rate of UE𝑢 𝑟inc 𝑢 Incremental rate of UE𝑢 𝑠 Stream index

𝑡_𝑢_,𝑠 Point of approximation for stream𝑠of UE𝑢 𝑢?

𝑘,𝑏 Index of UE selected to transmit on RB

𝑘of BS𝑏

𝑢 UE index

𝑤MN

𝑢 Resultant weight of UE𝑢at the MN

𝑤SN

𝑢 Resultant weight of UE𝑢at the SN

𝑤𝑏

𝑢 Utility-based RAT weight associated to UE𝑢of BS𝑏

𝑤𝑡

𝑢 Utility-based service weight associated to UE𝑢and service𝑡

𝑤_𝑢 Utility-based user weight associated to UE𝑢 𝑥req

𝑢 QoS requirement of UE𝑢

𝑥_𝑢 Value of QoS metric of UE𝑢 𝑦_𝑢 Value of service utility of UE𝑢 𝑧_𝑢 Value of user utility of UE𝑢

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SUMMARY

1 INTRODUCTION . . . 18

1.1 Background . . . 20

1.1.1 MIMO Interference Broadcast Channel (IBC) . . . 20

1.1.1.1 Overview about MIMO IBC . . . 20

1.1.1.2 Scheduling for MIMO IBC . . . 21

1.1.2 Dual-Connectivity . . . 22

1.1.2.1 Overview about Dual-Connectivity . . . 23

1.1.2.2 Scheduling for Dual-Connectivity . . . 25

1.2 Objectives and Thesis Structure . . . 28

1.3 Scientific Contributions . . . 29

2 USER SCHEDULING VIA BINARY VARIABLES FOR THE MIMO IBC . . . 32

2.1 Introduction . . . 32

2.2 System Model . . . 35

2.3 Problem Formulation . . . 35

2.4 Centralized Solution . . . 37

2.4.1 Centralized Algorithm Using Branch-and-Bound . . . 37

2.4.2 Feasibility Problem . . . 37

2.4.3 Lower and Upper Bounds Computation . . . 39

2.4.4 Complexity Analysis of Centralized Solution . . . 40

2.5 Hybrid Solution . . . 40

2.5.1 CFN-based Link Scheduling . . . 41

2.5.2 HWCR-based Link Scheduling . . . 41

2.5.3 HWSR-based Link Scheduling . . . 42

2.5.4 Complexity Analysis of Hybrid Solutions . . . 42

2.6 Numerical Results . . . 44

2.7 Chapter Summary . . . 46

3 USER SCHEDULING VIA RATE-RELAXATION VARIABLES FOR THE MIMO IBC . . . 47

3.3 Centralized Solution . . . 51

3.3.1 Reformulated Problem . . . 51

3.3.2 Centralized Solution via Approximated Problem . . . 52

3.4 Decentralized Solutions and Practical Signaling Aspects . . . 54

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SUMMARY 17

3.4.2 Signaling Aspects . . . 56

3.4.3 Possible Alternatives for the Proposed Solution . . . 58

3.5 Convergence Analysis . . . 59

3.6 Numerical Results . . . 61

3.6.1 Simulation setup . . . 62

3.6.2 Convergence and Main Features of the Proposed Solution . . . 62

3.6.3 Performance Comparison with the Centralized Solution . . . 64

3.6.4 Rate-Unconstrained Problem (𝑅_𝑢= 0,∀𝑢) . . . 65

3.6.5 Rate-Constrained Sum-Rate Maximization (𝑅𝑢> 0,∀𝑢) . . . 65

3.6.5.1 Evaluation with Feasible Problem Instances . . . 65

3.6.5.2 Evaluation with Infeasible Problem Instances. . . 66

4 ADAPTIVE BEARER SPLIT CONTROL FOR DUAL CONNECTIVITY 69 4.1 Introduction . . . 69

4.2 System Model . . . 71

4.4 Suboptimum Solution . . . 73

4.5 Reinterpretation of Suboptimum Solution . . . 77

4.5.1 Utility Functions . . . 80

4.5.2 Pseudo Code of Proposed Solution . . . 83

4.5.3 Practical Considerations . . . 84

4.6 Performance Evaluation . . . 84

4.6.1 Simulation Assumptions . . . 85

4.6.2 Simulation Results . . . 87

5 SPLIT RESPONSIBILITY SCHEDULER FOR MULTI CONNECTIV-ITY . . . 94

5.2 Split Responsibility Scheduler . . . 97

5.3 Scheduling Algorithm for the SRS . . . 99

5.4 Performance Evaluation . . . 104

5.4.1 Simulation Assumptions . . . 104

5.4.2 Case Study I: PF without Rate Requirements . . . 105

5.4.3 Case Study II: MMR . . . 106

6 CONCLUSIONS AND FUTURE WORKS . . . 108

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1 INTRODUCTION

The explosion in mobile traffic volume, the enriched service scope and the ever increasing number of connected devices led industry and academia to develop the conceptual vision and engineering requirements of the next generation of mobile networks. The fifth generation (5G) of cellular networks are expected to support a wide range of applications/services with diverse requirements, which pose new challenges on the current network technologies and in terms of designing efficient and flexible scheduling techniques to meet the diversified demands. The applications envisioned to be present in the 5G era have been categorized in three different use cases by the international telecommunication union (ITU) [1]:

• enhanced mobile broadband (eMBB): which includes services that will require seamless multi-connectivity across different radio access technologys (RATs) operating over a wide range of frequency bands, and require very high throughput and large bandwidth, such as 4K and 3D video, virtual and augmented reality, etc. Therefore, this category is focused on meeting people’s demand for an ever increasing digital lifestyle;

• ultra-reliable and low-latency communications (URLLC): that comprises ve-hicular communication (e.g., to support autonomous cars) and remote control (e.g., remote robotics, medical surgery or tactile Internet), which are applications related to the digital industry and demand very low latency, and very high reliability and availability;

• massive machine-type communications (mMTC): that involves applications for a further developed digital society with a large number of connected devices trans-mitting small amounts of data such as in smart cities and smart homes/buildings, which are characterized by requiring low bandwidth, high connection density, enhanced coverage, and low energy consumption.

In order to support the rigorous requirements of the upcoming eMBB, URLLC and mMTC applications, the ITU and 3rd Generation Partnership Project (3GPP) proposed more strict values for some key performance indicators (KPIs) for the downlink (DL) and uplink (UL) of 5G systems [1, 2]. Some of the most important KPIs are shown in Table 1.1.

Since the 5G networks must be able to support these diversified requirements im-posed by these envisioned 5G services/applications, there have been many attempts to find new communication technologies and techniques that further improve the end-user experience and system performance of cellular networks. A promising alternative to meet the envisioned demands for 5G and beyond networks is by means of network densification, which consists of increasing the number of antennas per site and deploying more access points per unit area [3, 4]. In either of these two ways, novel scheduling techniques are required to enhance the per-user quality of service (QoS).

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Chapter 1. Introduction 19

Table 1.1 – KPI values expected for 5G networks.

KPI Use case Target value Ref.’s

Peak data ratea eMBB 10 Gbps for UL [1, 2]

eMBB 20 Gbps for DL [1, 2]

User plane latency eMBB 4 ms for UL and DL [1, 2]

URLLC 1 ms for UL and DL [1]

User experienced data rateb

eMBB (dense urban) 50 Mbps for UL [1] eMBB (dense urban) 100 Mbps for DL [1]

Mobilityc

High speed vehicular 120 to 500 km/h [1, 2]

Vehicular 10 to 120 km/h [1]

Pedestrian 0 to 10 km/h [1]

Stationary 0 km/h [1]

Mobility

interruption time eMBB and URLLC 0 ms [1, 2]

Battery life mMTC 10 to 15 years [2]

a _{The peak data rate is defined as the maximum data rate under ideal conditions (in} bps), i.e., under error-free conditions and when all assignable radio resources for the corresponding link direction are utilized.

b _{The user experienced data rate is the 5% point of the CDF of the user throughput.} c _{The maximum user speed (in km/h) at which a defined QoS can be achieved.} Source: Created by the author.

User scheduling is one of the fundamental aspects of access coordination in modern cellular networks with a shared medium. The main responsibility of scheduling algorithms is to decide which users should transmit on each time slot as well as how much bandwidth should be assigned to each scheduled user. Such decisions are often driven by the optimization of a key performance indicator, such as maximizing the total rate or minimizing the power expenditure in the system. Scheduling techniques are at the heart of many radio communication technologies, such as in the 3GPP 5G cellular systems. In such systems, scheduling strategies are not standardized, giving the freedom for each vendor to develop its own scheduling strategies in different ways depending on the scenario and user demands [5]. Efficient scheduling techniques are paramount because, to a large extent, they determine the overall performance of the system. Therefore, the research and industry communities are always seeking to improve existing scheduling algorithms or to propose novel scheduling mechanisms for current and future wireless systems.

In this thesis, we deal with the design of scheduling solutions for 5G and beyond systems. More specifically, we propose scheduling algorithms for wireless telecommunication systems in which network densification has been adopted, where the proposed solutions seek to enhance the per-user QoS metrics. Considering the context of increasing the number of antennas per site, we design scheduling solutions for multiple input multiple output (MIMO) interference broadcast channel (IBC) systems, which basically consist of the downlink of multi-cell multi-user

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multi-antenna systems. Regarding the deployment of more access points per unit area, in which the deployment of smaller and smaller cells is included, we propose scheduling algorithms for systems with dual connectivity (DC), which is one of the solutions standardized by 3GPP for small cell enhancements.

The next section provides more details about MIMO IBC and DC-enabled systems along with the scheduling aspects involved in such systems. Then, Section 1.2 presents the objectives and the structure of this thesis. Finally, Section 1.3 details the scientific contributions of this thesis.

1.1 Background

1.1.1 MIMO Interference Broadcast Channel (IBC)

This section initially presents a brief overview about the MIMO IBC systems. After that, we describe the scheduling aspects addressed in this thesis for MIMO IBC systems.

1.1.1.1 Overview about MIMO IBC

The term MIMO IBC refers to a general model for downlink communication in which a plurality of multi-antenna transmitters wish to simultaneously send data to the respective intended multi-antenna receivers [6]. A MIMO IBC system is illustrated in Fig. 1.1c, where a cellular deployment is used. There are two degenerated cases of the MIMO IBC, namely the MIMO broadcast channel (BC) [7] and the MIMO interference channel (IC) [8]. In the MIMO BC, which is also referred to in the literature as multiuser (MU)-MIMO, a single multi-antenna transmitter wishes to send data to multiple multi-antenna receivers, as illustrated in Fig. 1.1a. Meanwhile, in MIMO IC systems, multiple multi-antenna transmitters wish to simultaneously send data to their single intended multi-antenna receiver, as illustrated in Fig. 1.1b. Consequently, in studies regarding MIMO BC systems, the inter-cell interference is not taken into account, while in studies considering MIMO IC systems, the intra-cell interference is not taken into account. On the other hand, MIMO IBC systems represent a more general model where both inter-cell and intra-cell interferences are considered. Therefore, the solutions designed for the MIMO IBC can also be applied to the MIMO BC and the MIMO IC as special cases, while the opposite is not necessarily true.

Even though we used cellular deployments in Fig. 1.1 to illustrate the concept of MIMO IBC systems, there are several other scenarios which can also be classified as MIMO IBC systems.

As mentioned before, future wireless networks are expected to support a wide variety of scenarios, including eMBB, URLLC and mMTC. The QoS demands imposed by such applications range from very strict latency and high energy efficiency to ultra-high reliability and per user throughput. Consequently, 5G and beyond systems are expected to exploit several communication technologies and different topologies to support those unprecedented demands.

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Figure 1.1 – Possible configurations for MIMO systems with respect to the number of transmit-ters and receivers considering cellular deployments.

(a) MIMO BC. (b) MIMO IC.

(c) MIMO IBC.

Source: Created by the author.

Several scenarios expected to occur in 5G and beyond systems are comprised of numerous multi-antenna transmitters competing to grant access to a shared medium to send data to many multi-antenna intended receivers. This situation leads to interference-limited scenarios [9], some of which are illustrated in Fig. 1.2. One example of such scenarios is the vehicle-to-everything (V2X), where vehicles can exchange information among themselves using the sidelink and can also communicate with a cellular base station (BS) or a road side unit (RSU) for safety and non-safety purposes. Additionally, cloud-based remote radio heads (RRHs) or unmanned aerial vehicle (UAV)-based scenarios also present the characteristic of having multiple interfering transmitters wishing to simultaneously send some information. Furthermore, as the distinct types of deployments are not necessarily geographically isolated, they might also interfere with each other, as exemplified in Fig. 1.2 using the case of small cells, wireless relay and coordinated multi-point (CoMP). Given these characteristics, those scenarios fall into the class of MIMO IBC systems.

1.1.1.2 Scheduling for MIMO IBC

In the context of MIMO IBC systems with per-user QoS requirements, the main goal of user scheduling is to simultaneously accommodate multiple users within the same resource by means of spatial-division multiplexing while fulfilling the individual QoS demands. A common approach found in the literature is to assume, for each channel realization, a pre-selected set of users for which the QoS constraints can be simultaneously met and focus on the transceiver design [10, 11, 12]. As such, the scheduling aspect is separately considered.

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Figure 1.2 – Examples of scenarios expected to occur in 5G and beyond systems which can be categorized into the class of MIMO IBC scenarios.

CoMP Small Cells V2X UAV eMBB Wireless Relay Cloud RSU RRH RRH

However, in practical MIMO scenarios, it is often the case that many users compete for the same time-frequency resource such that it is no longer possible to meet their per-user QoS demands. To fully optimize the system performance, this situation requires joint transceiver design and user scheduling. In this case, two possibilities for performing the scheduling task by relaxing the initial conditions are [7]:

1. Reducing the user set by applying either admission control [13, 14] or user removal techniques [15] so that the scheduled users can have their QoS constraints fulfilled, as illustrated in Fig. 1.3a.

2. Relaxing the QoS constraints to find a minimum QoS requirement feasible for all users, e.g., the maximum minimum weighted common rate [16] or signal to interference-plus-noise ratio (SINR) [17], as illustrated in Fig. 1.3b.

While reducing the user set is a widely-used approach to deal with infeasible QoS demands, relaxing some of the QoS constraints without reducing the set of served users is advan-tageous in several situations. This can be the case when several users are watching high-definition video employing 1080 pixels (1080p) technology, and the aggregated bit rate requirement be-comes infeasible. In this case, rather than dropping problematic users, a scheduling mechanism can compute a useful rate allocation that allows some users to watch the video in a lower resolution (e.g., 720p) while the remaining users continue watching the video in 1080p.

1.1.2 Dual-Connectivity

This section firstly presents a brief overview about the DC technology. Then, we describe some of the scheduling aspects involved in the DC systems.

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Figure 1.3 – Scheduling approaches for MIMO IBC systems with QoS requirements. (a) Reducing the user set, where user-2 is not scheduled for reception while the remaining users simultaneously meet their QoS demands.

x UE Index 1 2 3 4 5 x 6 Minimum SINR Requirement User-5 User-6 User-4 User-3 User-2 User-1 SINR (dB) 5 10 15 20 0

(b) Relaxing the QoS constraints, where a new reduced minimum QoS re-quirement is computed so that every user can meet the new QoS rere-quirement.

6 Initial Minimum SINR Requirement SINR (dB) User Index 5 10 15 20 1 2 3 4 5 0 User-5 User-6 User-4 User-3 User-2 User-1 New Minimum SINR Requirement

1.1.2.1 Overview about Dual-Connectivity

As aforementioned, one of the most promising alternatives to achieve the ultra-high per-user throughput demands is to increase the cell densification by deploying small cells [3]. In these heterogeneous networks, the macro cells are responsible for providing a wide and reliable coverage region, while the small cells can offer improved capacity in hotspot areas and offload some traffic from the macro cell [18]. However, the deployment of small cells has the disadvantage that, due to the smaller cell coverage area and the larger number of cell boundaries, mobility-related issues may arise, such as an increase in the number of cell (re)selections and handovers.

In this context, the DC technology has been proposed in the long term evolution (LTE) Release 12 specifications by 3GPP as one of the most relevant technologies to accomplish even higher per-user throughput, mobility robustness, and load balancing [19, 20]. This technology was standardized under the umbrella work of small cell enhancements in the 3GPP LTE Release 12, where user equipments (UEs) with DC capabilities can be simultaneously connected to two LTE BSs operating in different carriers.

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Figure 1.4 – Example of deployment scenario of DC composed of one MN and two SNs. Notice that the UE-1 and UE-2 are in SC with the MN and SN-2, respectively, while UE-3 is in DC with the SN-1 and MN.

SN-1 MN SN-2 UE-3 UE-1 UE-2 Non-Ideal Backhaul

two BSs: a master node (MN) and a secondary node (SN), which operate on different carrier frequencies and are interconnected by traditional backhaul links, known as X2 (LTE standard) or Xn (new radio (NR) standard) interface1 in accordance with the 3GPP terminology. These X2/Xn-based backhauls are non-ideal in practice, being characterized by a certain latency and limited capacity [21].

In Fig. 1.4, an example of a DC scenario is illustrated, which is composed of an MN connected to two SNs via non-ideal backhaul links and three UEs. UE-1 and UE-2 are in single connectivity (SC) with MN and SN-2, respectively, while UE-3 is in DC with the SN-1 and MN. Therefore, the throughput of UE-3 would be increased by utilizing radio resources from different BSs.

Differently from the DC scenario presented in [19, 20], where a heterogeneous network composed of LTE BSs operating on different frequencies was considered, another possible solution for DC that has been exploited in the literature is a scenario with the integration between multiple RATs, where the MN belongs to one RAT and the SN to another. In this context, some works have considered as a possible solution for DC a tight integration between the upcoming 5G RAT, named NR, and the legacy fourth generation (4G) RAT, namely LTE [22, 23]. In fact, this option has been standardized by 3GPP in Release 15 [24].

More specifically, in one possible architecture of the DC technology based on the LTE-NR integration, which is the one considered in this thesis, the legacy LTE MN provides a larger coverage region to the SNs that use the NR technology [23, 24]. Therefore, for simplicity, in the remaining of this work, MN refers to LTE MN and SN refers to NR SN. This integration targets the fulfillment of the 5G requirements by means of allowing simultaneous multi-RAT connectivity in order to provide faster mobility and by enhancing the per-user QoS due to the

1 _{The X2/Xn interface connects two neighboring BSs in a peer to peer fashion to assist handover and provide a}

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use of resources across multiple RATs.

Concerning the user plane, there are three types of bearer, namely master, secondary and split bearer, as illustrated in Fig. 1.5. The master bearers are sent independently through the protocol stack of the MN, while the secondary bearers are sent independently through the protocol stack of the SN. Meanwhile, a common packet data convergence protocol (PDCP) layer is required to support split bearers, while the radio link control (RLC) and medium access control (MAC) layers from the respective MN and SN are used. Note that an NR PDCP is always used for split bearers [24], which comes from the fact that the common PDCP layer needs to process data units from both LTE and NR RATs.

Figure 1.5 – Radio protocol architecture for master, sec-ondary and split bearers.

MN PDCP MN RLC MN MAC SN PDCP SN RLC SN MAC MN RLC NR PDCP SN RLC Master Bearer Secondary Bearer Split Bearer UE

Source: Created by the author, based on [24].

1.1.2.2 Scheduling for Dual-Connectivity

In order to fully harvest the gains provided by DC scenarios supporting split bearers, it is necessary to design three main functionalities: (i) mechanisms for determining the UE-BS association (UBA), (ii) a flow control algorithm (FCA) for splitting the data traffic between BSs, and (iii) radio resource management (RRM) techniques, such as power control and radio resource allocation (RRA) [25]. These functionalities should work targeting a common global objective in order to exploit all benefits provided by the DC technology and enhance the QoS experienced by the UEs. Fig. 1.6 shows the most important aspects of each main functionality, which are also detailed in the following.

UE-BS Association

This functionality involves the design of mechanisms to select the best connection solution for the UE, which can be DC, SC or fast switching (FS). The SC is the traditional solution where the UE connects to only one BS at a time. On the other hand, a UE in DC connects simultaneously to two BSs on both user and control planes. Differently from DC, a UE in FS is connected to two BSs only in terms of control plane, while the user plane can switch very fast

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Figure 1.6 – Summary of the main tasks performed by each of three functionalities needed in DC scenarios supporting split bearers.

UE-BS Association

• Determine best UE connection solution among: single connectivity, dual connectivity and fast switching • Decision based on, for instance, signal

quality and/or QoS perceived by UE

Flow Control Algorithm

• Compute the percentage of data traffic going through 4G and 5G BSs without overloading or underutilizing their resources

• Data split performed relying on metrics from 4G and 5G BSs such as traffic load, transmission capacity, buffering time, user satisfaction or any other user QoS related metric

Radio Resource Management

• Responsible for resource assignment and power allocation, as well as beam management • Procedures conducted based on, for example,

service type, packet delay, user throughput, fairness, channel quality measurement, etc

Time Fr equ ency Time Fr equ ency Single Connectivity Dual

Connectivity _SwitchingFast

Core Network

MN

SN

between BSs [26]. The FS was initially proposed in [23], where the authors presented it as a fast user plane switching.

Several works have studied the problem of UBA in heterogeneous and multi-RAT scenarios. A survey regarding some studies is presented in [27], where the authors discuss multiple mathematical formulations and tools to model the UBA, but without considering DC. The authors in [28] proposed a user-centric scheme based on an optimization problem with DC considerations, where the UEs perform the BS selection according to several criteria; however, no evaluation was conducted in DC-enabled scenarios.

In [26], the authors compared different channel measurement metrics for providing QoS improvements by exploiting FS and DC; it was concluded therein that when the system load increases, less UEs should remain in DC to limit the interference and increase the system performance. This behavior was observed in [26] by using signal quality instead of signal strength metrics. Another approach found in the literature is to combine QoS and signal quality metrics [29], which allows users to detect low QoS levels even when the signal quality is acceptable.

Radio Resource Management

Another functionality highlighted here concerns RRM techniques, which include tasks such as RRA, power control and beam management. The last task has fundamental importance to overcome the problem of intrinsic challenging propagation characteristics (e.g., lower diffraction and higher path loss) present in high frequencies, which are expected to be used for some use cases in 5G networks. Furthermore, new degrees of freedom arise to be exploited by the upcoming RRM techniques for LTE-NR scenarios with DC, such as different bandwidths

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and transmission time interval (TTI) durations, as well as different link capacities from distinct RAT connections of DC UEs.

RRM techniques have been extensively studied in the literature. As examples, [7, 30] present extensive surveys on RRM schemes; however, the algorithms discussed therein consider a single RAT network such that they cannot be directly applied in DC scenarios. In [31], the authors proposed a modified version of the traditional proportional fair (PF) scheduling algorithm for DC scenarios, which was modified to consider the total throughput of DC UEs, i.e., the throughput considering the data received from both BSs. To accomplish this, the BSs use the backhaul connection to exchange the throughput information of DC UEs, which is a rather low demanding signaling. A heuristic RRA algorithm for maximizing the minimum user rate was proposed in [32], where the authors also relied on the backhaul connection to exchange the throughput information of DC UEs.

Flow Control Algorithm

In DC scenarios supporting split bearers where a LTE BS acts as the MN, an FCA at the PDCP layer in the LTE BS is responsible for processing, routing and splitting the data traffic of UEs in DC. Therefore, the FCA orchestrates the bearer split ratios such that the correct amount of data is transmitted to LTE and NR BSs. Notice that a well-designed FCA not only benefits UEs in DC, but also UEs in SC and FS since the traffic of DC UEs should be forwarded to where there is more transmission capacity available (i.e., less competition for radio resources), which in its turn reduces the QoS damage caused to UEs with only one active connection.

In summary, the FCA for LTE-NR DC networks should be designed to neither overload the transmission buffer of either LTE or NR BSs, nor underutilize their available radio resources. Additionally, the FCA must enhance the QoS provision in order to meet unprecedented requirements predicted for the 5G era. To accomplish these goals, during the conception of an FCA, it is necessary to consider diverse system parameters (e.g., buffer status and backhaul characteristics) as well as QoS metrics such as throughput and packet delay.

The simplest FCA for bearer splitting is performed by sending fixed percentages of data to the NR and LTE BSs [33]. A dynamic mechanism is proposed in [25] by means of a request-and-forward algorithm, where the NR BS would send data requests to the master LTE based on the users’ throughput, and its target buffering time and transmission capacity. An FCA algorithm that analyzes the resource utilization and sends each packet on the link with shorter estimated delay is proposed in [34]. In [35], a joint FCA and traffic scheduling is proposed aiming at maximizing the network throughput, where the authors only discussed an optimal solution for their optimization problem, i.e., no low-complexity algorithm was proposed therein.

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1.2 Objectives and Thesis Structure

Considering the overview about scheduling presented in the previous sections of this chapter, the main objective of this thesis is to design scheduling algorithms for MIMO IBC systems and DC-enabled systems.

A block diagram illustrating the thesis structure is presented in Fig. 1.7. Observe that the thesis is divided into two independent parts. In the first part, we address scheduling aspects related to MIMO IBC systems, which are discussed in Chapter 2 and Chapter 3. In the second part, we address scheduling aspects related to DC-enabled systems, which are discussed in Chapter 4 and Chapter 5.

Figure 1.7 – Block diagram for thesis organization. Scheduling Strategies for

Wireless Networks

Dual Connectivity

Chapter 1 – Background on scheduling for dual connectivity

Chapter 4 – Flow control algorithm for bearer split

Chapter 5 – MAC scheduler architecture and algorithm MIMO IBC

Chapter 1 – Background on scheduling for the MIMO IBC

Chapter 2 – User scheduling via combinatorial variables

Chapter 3 – User scheduling via QoS-relaxation variables

More specifically, Chapter 2 proposes a user scheduling approach for rate-constrained sum-rate maximization in the MIMO IBC. To this end, we modify the classical transceiver design problem by introducing binary variables, which are responsible for selecting a subset of users for which the rate demands can be simultaneously met. Then, we design a centralized solution using the branch and bound (BB) method and a hybrid solution, where the user scheduling is conducted in a centralized unit while the transceiver design is executed in a decentralized fashion. Numerical results show the effectiveness of the proposed user scheduling solutions compared to existing solutions.

Chapter 3 proposes a novel approach for user scheduling in the MIMO IBC. Instead of using binary variables as in Chapter 2, we use QoS relaxation variables in a novel formulation of the rate-constrained sum-utility maximization problem, which allows to either deactivate users or minimize the QoS degradation for the scheduled users when the set of candidate users is not feasible with respect to their rate demands. We propose centralized and decentralized

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solutions, where the decentralized solutions focus on a practical design and low signaling overhead. Simulations show the advantages of the proposed solution in a variety of MIMO IBC scenarios.

Considering the bearer split configuration for DC, Chapter 4 proposes a flow control algorithm that targets to maximize the user satisfaction by adaptively controlling the bearer split ratios. The adaptation is conducted attempting to meet a predefined network operator goal, such as maintaining a certain QoS level at a given network node. Furthermore, the proposed solution tracks the channel and QoS metrics variations such that the best split ratios are employed. Simulations in 5G multi-RAT scenarios show that the proposed solution effectively maximizes the user satisfaction as well as enhances the users and system throughput compared to existing algorithms.

Chapter 5 proposes a novel MAC scheduler, which splits the existing scheduling tasks into two simpler scheduling entities, thus it is named split responsibility scheduler (SRS). The SRS presents degrees of flexibility in terms of easy deployment of centralized or distributed scheduler architectures as well as in terms of adding and testing new scheduling behaviors. Besides the new architecture, we also propose a scheduling algorithm that can be executed in centralized or distributed SRS architectures without any modification. Network operators can modify the scheduling behavior of the proposed scheduling algorithm by only changing simple utility functions. Simulations show that the proposed algorithm has close to optimal performance in 5G multi-connectivity scenarios, when running in both centralized and distributed architectures, while outperforming existing solutions.

Finally, Chapter 6 draws the main conclusions taken from the solutions and results presented in this thesis along with some directions for possible future works.

1.3 Scientific Contributions

Currently, the content of this thesis is published or is under review with the following bibliographic information:

Journal Papers

[J1] ANTONIOLI, R. P.; PARENTE, G. C.; SILVA, C. F. M. e; SOUSA, D. A.; RODRIGUES, E. B.; MACIEL, T. F.; CAVALCANTI, F. R. P. Dual Connec-tivity for LTE-NR Cellular Networks: Challenges and Open Issues. Journal of Communication and Information Systems, v. 33, n. 1, Aug. 2018. DOI: 10.14209/jcis.2018.28 (Invited Paper)

[J2] ANTONIOLI, R. P.; RODRIGUES, E. B.; SOUSA, D. A.; GUERREIRO, I. M.; SILVA, C. F. M. e; CAVALCANTI, F. R. P. Adaptive bearer split control for 5G multi-RAT scenarios with dual connectivity. Computer Networks, Elsevier, v. 161, p. 183–196, Oct. 2019. DOI: 10.1016/j.comnet.2019.07.005

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Scheduler for Multi-Connectivity in 5G Cellular Networks. IEEE Netw., 2020 (First Round Review)

[J4] ANTONIOLI, R. P.; FODOR, G.; SOLDATI, P.; MACIEL, T. F. User Scheduling for Sum-Rate Maximization Under Minimum Rate Constraints for the MIMO IBC. IEEE Wireless Commun. Lett., v. 8, n. 6, p. 1591–1595, Dec. 2019. ISSN 2162-2345. DOI: 10.1109/LWC.2019.2930255

[J5] ANTONIOLI, R. P.; FODOR, G.; SOLDATI, P.; MACIEL, T. F. Decentralized User Scheduling for Rate-Constrained Sum-Rate Maximization in the MIMO IBC. IEEE Trans. Commun., 2020 (Second Round Review)

[J6] ANTONIOLI, R. P.; SOUSA, D. A.; RODRIGUES, E. B.; GUERREIRO, I. M.; SILVA, C. F. M. e; MACIEL, T. F.; CAVALCANTI, F. R. P. Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks. Journal of Communication and Information Systems - Letters, 2020 (Submitted)

Patents

[P1] ANTONIOLI, R. P.; PETTERSSON, J. Technique for Controlling Radio Re-source Allocation. Mar. 2019. PCT/EP2019/05763. Patent Application

[P2] ANTONIOLI, R. P.; RODRIGUES, E. B.; GUERREIRO, I. M. Adaptive Flow Control For Bearer Split in 5G Systems. Apr. 2019. PCT/SE2019/050370. Patent Application

[P3] ANTONIOLI, R. P.; FODOR, G.; SOLDATI, P. Scheduling and QoS-control for Vehicle Communications. Sept. 2019. Provisional Application

It is worth mentioning that this thesis was developed under the context of the following Ericsson/UFC technical cooperation projects:

• UFC.43 - 5G Radio Access Network (5GRAN), August/2017 - October/2018, • UFC.47 - Network Assisted Intelligent Vehicle-to-Everything communications

(NAIVE), November/2018 - April/2020,

in which a number of six technical reports, three in the first project and three in the second project, have been delivered. Furthermore, due to this partnership, two Ph.D. internships occurred during this Ph.D.:

• Feb/2018-Jun/2018: Ph.D. internship at Ericsson Research in Luleå-Sweden; • Sep/2018Aug/2019: Ph.D. internship at Ericsson Research in Stockholm/Kista

-Sweden.

Also in the context of these projects, the author collaborated in the following scientific works:

Journal Papers

[J7] ANTONIOLI, R. P.; RODRIGUES, E. B.; MACIEL, T. F.; SOUSA, D. A.; CAV-ALCANTI, F. R. P. Adaptive resource allocation framework for user satisfaction

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maximization in multi-service wireless networks. Telecommunication Systems, Springer, v. 68, n. 2, p. 259–275, June 2018. DOI: 10.1007/s11235-017-0391-3

[J8] ANTONIOLI, R. P.; GUERREIRO, I. M.; SOUSA, D. A.; RODRIGUES, E. B.; SILVA, C. F. M. e; MACIEL, T. F.; CAVALCANTI, F. R. P. User-assisted Bearer Split Control for Dual Connectivity in Multi-RAT 5G Networks. Wireless Net-works, v. 26, n. 5, p. 3675–3685, July 2020. ISSN 1572-8196. DOI: 10.1007/ s11276-020-02283-6

Patents

[P4] ANTONIOLI, R. P.; GUERREIRO, I. M.; RODRIGUES, E. B. Methods, Data Split Unit and Data Collector Unit for Controlling Data Transmission Over Two Connections. Sept. 2018. PCT/SE2018/05094. Patent Application

[P5] ANTONIOLI, R. P.; BRAGA JR., I. M.; FODOR, G. Power control for bidi-rectional sidelink. May 2020. Provisional Application

Conference Papers

[C1] SOUSA, D. A.; MAURÍCIO, W. V. F.; ANTONIOLI, R. P.; MACIEL, T. F.; LIMA, F. R. M. Improved Joint Resource and Power Allocation Algorithm with QoS Provisioning. In: SIMPóSIO BRASILEIRO DE TELECOMUNICAÇõES E PROCESSAMENTO DE SINAIS, Sept. 2018, Campina Grande, Brazil. Anais [...] Campina Grande, Brazil: SBrT, Sept. 2018. p. 1–5

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2 USER SCHEDULING VIA BINARY VARIABLES FOR THE MIMO IBC

In the present chapter and in the next chapter, we deal with the problem of user scheduling in the MIMO IBC. More specifically, in this chapter, the user scheduling problem is tackled using the approach of reducing the user set (cf. Section 1.1.1.2) by means of binary variables.

In this context, we consider a sum-rate maximization problem with per-link min-imum rate constraints for the MIMO IBC. The key idea is scheduling a suitable subset of the communication links for simultaneous transmissions, such that a minimum rate for each scheduled link can be ensured. To this end, we pose the sum-rate maximization problem as a combinatorial optimization problem, in which we introduce binary variables to the classical transceiver design problem. We propose a centralized solution based on branch-and-bound and a hybrid semi-distributed scheme, in which a centralized unit is responsible for scheduling decisions, while the transceiver computations are distributed. Simulations show that the proposed solutions handle the user scheduling effectively, while the proposed hybrid scheme performs closely to the centralized scheme.

The remainder of this chapter is organized as follows. Section 2.1 introduces the topic of user scheduling in the MIMO IBC, discusses some related works and highlights the main contributions of the chapter. Section 2.2 discusses the system model adopted in this chapter. Then, Section 2.3 formulates the proposed optimization problem. Section 2.4 proposes a centralized solution for the considered optimization, while Section 2.5 proposes the hybrid solution. The results and related discussions are presented in Section 2.6, while Section 2.7 draws the conclusions.

2.1 Introduction

Scheduling techniques are at the heart of many radio communication technologies, such as the 3GPP 4G and 5G systems. The task of these techniques is to grant access to a shared medium to multiple communication links competing for resources in a fair and efficient manner. More specifically, the allocation of resources to users via scheduling typically follows from optimizing a key performance indicator, e.g., maximizing the system sum-rate, minimizing the total transmit power, maximizing the amount of active links, minimizing the age of information, etc. In this context, scheduling is also crucial to fulfill the QoS demands of individual users expressed, e.g., in terms of minimum data rate or SINR, tolerable latency, etc.

User scheduling and resource allocation in radio networks are typically formulated as combinatorial problems, wherein the optimization space spans both scheduling decision variables and variables representing parameters specific to the underlying radio technology. On the one hand, in single input single output (SISO) systems, the optimization variables’ space is

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Chapter 2. User Scheduling via Binary Variables for the MIMO IBC 33

basically comprised of the transmit power allocated to users and the scheduling variables. On the other hand, in multiple input single output (MISO) or MIMO systems, in addition to scheduling decision variables, the optimization space may extend to transmit/receive beamforming vectors, transmit powers, etc. [36].

Several works in the literature proposed scheduling schemes for SISO systems. Scheduling solutions considering the conventional objective of maximizing the number of active links subject to SINR constraints (also referred as the link activation problem) were proposed in [37, 38]. Parallel and successive interference cancellation were introduced in the conventional link activation problem in [39], while in [40] the authors also considered cooperative transmission along with interference cancellation for the link activation problem. Another extension of the link activation problem was considered in [41, 42, 43, 44, 45], where the authors proposed scheduling solutions with the objective of emptying the transmission buffers in the minimum amount of time, which is a problem also known as the minimum time schedule that basically consists of sequential link activation problems on different time-slots. The minimum time schedule problem along with packet deadlines was studied in [46]. More recently, scheduling solutions for minimizing the age of information were developed with and without packet deadlines in [47] and [48], respectively. Nevertheless, besides the fact that these works mostly consider centralized solutions, only single antenna transmissions are considered. However, modern wireless networks usually rely on multi-antenna architectures, in which such SISO solutions cannot be directly applied.

A wide body of research was also developed regarding multi-antenna systems. Centralized and distributed solutions for sum-rate maximization in MIMO systems that exploit an iterative weighted minimum mean squared error (WMMSE) approach were proposed in [6, 49]. The iterative WMMSE solution was then combined with a user grouping algorithm in [50]. Sum-rate maximization solutions based on the BB method for MISO systems were proposed in [51] and [52]. Centralized iterative algorithms for sum-rate maximization based on geometric programming were studied in [53, 54]. Several other studies also proposed sum-rate maximization algorithms based on a variety of mathematical approaches in [36, 55, 56, 57, 58, 59]. Algorithms aiming at a proportionally fair allocation or focusing on minimizing the norm of the transmission queue deviation were proposed in [60] and [61], respectively. However, these works only impose power constraints on the transmitter side, and there is no minimum QoS consideration in their optimization problems. Consequently, the scheduling aspect focusing on guaranteeing some per-user minimum QoS is not considered in those works.

On another line of research, there are also some works in the literature that consid-ered the problem of providing some minimum QoS in MIMO systems. Most works along this line considered sum-power minimization subject to SINR constraints. For example, a centralized algorithm based on minimum mean squared error (MMSE) and uplink-downlink duality was proposed in [11], where the authors also provided a thorough convergence analysis. A sum-power minimization solution based on an iterative algorithm using second-order cone programming

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(SOCP) was proposed in [62]. Centralized and distributed algorithms for sum-power minimiza-tion focusing on reduced backhaul signaling were proposed in [63]. Using difference of convex functions program (DCP), successive convex approximation (SCA) and SOCP, the authors in [12] proposed centralized and distributed solutions for two different signaling strategies. Algorithms based on other objective functions were also proposed. The sum-energy efficiency maximization subject to minimum SINR constraints was studied in [64], where the authors used the alternating direction method of multipliers tool to propose centralized and distributed algorithms. Centralized and distributed algorithms for sum-rate maximization subject to min-imum rate constraints were proposed in [10, 65], where the authors relied on SCA, DCP and Lagrangian relaxation to propose their solutions. Even though these works considered some minimum QoS assurance, they largely neglected the user scheduling aspect assuming that a feasible set of users had been previously selected. Therefore, these works still did not exploit joint transceiver design and user scheduling solutions for enhancing the performance of MIMO systems.

A few works in the literature considered some sort of user scheduling with QoS as-surance in MIMO scenarios. For MISO single-resource and multi-resource systems, respectively, [15] and [13] proposed user scheduling solutions for sum-power minimization under minimum SINR constraints. The authors in [14] proposed a scheduling algorithm for MISO systems based on a one-step reformulation of a sum-power minimization subject to SINR constraints optimization problem where the scheduling was modeled using binary variables. Considering the strategy of scheduling by relaxing the QoS constraints, the authors in [17] proposed to balance the per-user SINR requirements to a common SINR requirement in a MISO scenario, while in [16] the authors proposed to maximize a minimum weighted common rate such that feasible rate requirements are found for a MIMO scenario. Some of these works (e.g., [13, 14, 15, 17]) do not benefit from multi-antenna capabilities at the user side, thus not fully exploiting the gains provided by the multi-antenna technology. Furthermore, the existing works have not considered the rate-constrained sum-rate maximization problem. Therefore, scheduling for maximizing the sum-rate of the MIMO IBC under minimum rate constraints remains an open problem.

This chapter considers the joint link scheduling and transceiver design for sum-rate maximization in the MIMO IBC with per-link minimum rate constraints. We pose the problem as a combinatorial optimization problem, where we introduce binary variables to the classical transceiver design problem to determine a feasible subset of links that meet the respective rate requirements. The combinatorial nature of the scheduler design makes the overall problem more complex and challenging than the classical transceiver design problem.

In this context, we propose a centralized solution based on the BB method, which jointly computes the link scheduling and transceiver design in a central entity and maximizes the system sum-rate. Furthermore, we propose a hybrid approach, in which a centralized unit is responsible for scheduling decisions, while the link-level adjustments of the MIMO transceivers are computed in a decentralized fashion using the algorithm from [10]. Three different policies

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for the centralized scheduling are proposed. Numerical results show that the proposed solutions effectively handle the user scheduling problem, and the hybrid solution performs close to that provided by the centralized scheme. The numerical results also illustrate that the novel joint design of the link-level scheduler and the MIMO transceivers can be used to ensure a predetermined minimum rate for the scheduled users both for the MIMO IBC and for its degenerated version, the MIMO IC.

2.2 System Model

We consider a MIMO IBC system, where 𝐵BSs equipped with 𝑁_Tantennas serve in total𝑈 multi-antenna user terminals, each one equipped with𝑁_Rantennas. The set of users associated to BS 𝑏 is denoted by U_𝑏 with𝑈_𝑏= |U_𝑏|, where each user𝑢 is served by a single BS 𝑏_𝑢, i.e., U_𝑏∩ U_˜

𝑏= ∅, 𝑏= 1, . . . , 𝐵, 𝑏, ˜𝑏. All BSs are assumed to operate over a common

frequency channel and serve the respective users with linear transmit beamforming. We let𝑆_𝑢 denote a fixed number of spatial streams allocated to user𝑢, and indicate the𝑠th stream of user𝑢 by the index pair (𝑢, 𝑠). The total number of streams in the system is𝑆= Í𝑈

𝑢=1𝑆𝑢.

The downlink signal received by user𝑢over the spatial stream𝑠can be expressed as

y𝑢,𝑠= H𝑏𝑢,𝑢m𝑢,𝑠 𝑞_𝑢_,𝑠+ 𝑈 Õ 𝑖=1 𝑆𝑖 Õ 𝑗=1, (𝑖,𝑗),(𝑢,𝑠) H𝑏𝑖,𝑢m𝑖,𝑗 𝑞_𝑖_,𝑗+ n_𝑢, (2.1) where H𝑏𝑖,𝑢∈ C 𝑁_R×𝑁_T

is the channel matrix between user𝑢and BS𝑏serving user𝑖, m𝑢,𝑠∈ C 𝑁_T

is the precoder vector of the corresponding data stream,𝑞_𝑢_,𝑠is the mutually independent transmitted data symbol withEh𝑞𝑢,𝑠

2_{i = 1 and n} 𝑢∈ C 𝑁_R ∼𝐶 𝑁(0, 𝜎2

𝑢) is the noise at user𝑢. User𝑢decodes

the signal y𝑢,𝑠via a unit norm receive beamformer w𝑢,𝑠∈ C 𝑁_R

. The SINR for stream𝑠of user𝑢is given by

Γ𝑢,𝑠= wH𝑢,𝑠H𝑏𝑢,𝑢m𝑢,𝑠 2 𝑈 Í 𝑖=1 𝑆𝑖 Í 𝑗=1, (𝑖,𝑗),(𝑢,𝑠) wH𝑢,𝑠H𝑏𝑖,𝑢m𝑖,𝑗 2 +𝜎_𝑢2 w𝑢,𝑠 2 . (2.2)

We assume the availability of perfect channel state information (CSI) at the trans-mitters and receivers, similarly to the assumptions used in [6, 10, 12, 49]. The analysis under imperfect CSI conditions is left for future work.

2.3 Problem Formulation

We consider a rate-constrained weighted sum-rate maximization problem with max-imum power constraints per BS. Differently from [6], which strictly maximizes the sum-rate, and [10], which does not consider the link scheduling for the rate-constrained sum-rate maxi-mization, we deal with the case in which it is not possible to meet the rate constraints for every