Massive MIMO

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Massive MIMO

Massive MIMO

operações muitas de inversão de matrizes, reduzindo deste modo a complexidade da implementação, numa escala considerável. Para os receptores no domínio das frequências, em transmissões uplink, os esquemas SC-FDE tronam-se adequados para combater a elevada dispersão temporal devida ao canal assim como também oferecem uma reduzida envolvente de flutuação nos sinais transmitidos quando comparado com sinais OFDM [12], [9]. Isto traduz-se numa eficiente amplifi- cação de potência. Contudo, os igualizadores FDE convencionais são lineares e não oferecem tão elevados desempenhos como os igualizadores não lineares. Uma solução seria utilizar igualizadores Decision Feedback Equalizer (DFE), que apre- sentam um desempenho superior comparativamente aos igualizadores lineares. O problema deste e outros igualizadores que operam no domínio do tempo é que a sua complexidade aumenta drasticamente quando a Resposta Impulsiva do canal (CIR) é muito longa. Em contrapartida, a complexidade dos igualizadores FDE é independente da duração da CIR, daí a motivação para usar esquemas SC-FDE com OFDM. No entanto, não deixa de ser desejável ter-se igualizadores FDE não lineares (do tipo DFE) para esquemas SC-FDE. Os receptores iterativos tais como o IB-DFE surgem como uma solução a esta necessidade e oferecem, por sua vez, melhorias no seu desempenho, aproximando-o muito do MFB [13], que constitui a marca de referência de desempenho ótimo. O receptor IB-DFE utiliza DFE para SC-FDE implementado no domínio da frequência que trabalha ao nível do bloco iterativo e daí o seu bom desempenho. O único inconveniente deste tipo de igualizadores é que a sua complexidade cresce exponencialmente com o número de antenas (ou utilizadores) devido as operações de inversão de matrizes presentes na sua constituição. O que não é nada favorável para ambientes massive MIMO com centenas de antenas a comunicar simultaneamente.
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Hybrid Iterative Space-Time Equalization for Multi-User mmW Massive MIMO Systems

Hybrid Iterative Space-Time Equalization for Multi-User mmW Massive MIMO Systems

assuming perfect knowledge of the channel sate information (CSI) and single antenna user terminals (UTs). A downlink hybrid beamforming scheme based on a weighted sum mean square error (WSMSE) optimization problem was proposed for massive MIMO systems [27]. It was shown that the performance degradation is severe when the number of RF chains is smaller than half of the number of antennas. Hybrid approaches explicitly designed for mmW communications were considered in [28] and [29]. Li et al. [28] designed an uplink receive beamforming that handles the multiuser interference at both analog and digital stages. However, only single antenna UTs were assumed. A limited feedback hybrid two-stage precoding/combining algorithm was proposed in [29] for the downlink. In the first stage transmit and receive analog beamforming are jointly computed for each base-station (BS) – user terminal pair assuming interference free links. In the second stage the interference is explicitly mitigated, in the digital domain, by employing a conventional linear zero forcing (ZF) precoding.
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Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

We designed a new hybrid iterative multiuser equalizer for a subconnected mmWave massive MIMO architecture. We considered low complexity UTs employing randomly analog- only precoding and a single RF chain. First, it was verified that the proposed hybrid iterative multiuser equalizer converges and that it achieves a performance close to the digital and fully connected counterparts, requiring very few iterations. This structure efficiently manages multiuser interference; the dictionary approximation and the sequential optimization are quite precise. We observed that when we reduce the number of receiving antennas per RF chain, that is, 𝑅 = 𝑁 rx /𝑁 RF
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Fast matrix inversion updates for massive MIMO detection and precoding

Fast matrix inversion updates for massive MIMO detection and precoding

Multiple-input multiple-output (MIMO) and its extension to very large arrays have been a trending topic of research in the past few years. The theoretical advantages of massive MIMO systems are clear: increased spectral capacity while attaining high energy efficiencies [1]. However, with the increase of the number of dimensions, using conventional MIMO algorithms may not be suitable any more in terms of computational efficiency and new methods must emerge.

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Spectral Efficiency Analysis in Massive MIMO using FBMC-OQAM Modulation

Spectral Efficiency Analysis in Massive MIMO using FBMC-OQAM Modulation

Abstract — This article covers the potential of Filter Bank Multicarrier (FBMC) modulation as an alternative to be used in the future 5G wireless networks in which Massive Multiple-Input Multiple-Output (MIMO) will be deployed. The study compares orthogonal frequency division multiplexing (OFDM) with FBMC. The former is the multiplexing technique in 4G communications and the latter is one of the strongest candidates to replace OFDM in 5G networks. This comparison evaluates the spectral efficiency (SE) of a Massive MIMO (MM) system uplink under a single-cell environment. The diversity in MM permits a self-equalization of the channel, which the FBMC further benefits from, due to the confinement of the subcarrier in an assigned range. Due to the absence of the cyclic prefix, the FBMC has better SE than the OFDM for increasing signal-to-noise-ratio (SNR). One may find a scarce literature covering the FBMC in a large-scale multiuser MIMO scenario, which considers a large number of antennas at the base station (BS). Various scenarios are considered by varying the number of antennas, users and different cell radius. Moreover, the subcarrier modulations are simulated, and not considered Gaussian distributed, as in Shannon limit theory. In some cases, the FBMC allows doubling the cell radius for the same SE value of 3.8 bits/s/Hz/user. For a fixed cell radius of 750m and a SE of 3.5 bits/s/Hz/user, the OFDM requires three times more antennas than FBMC when both modulations are under the same conditions.
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Analytical performance evaluation of massive MIMO techniques for SC-FDE modulations

Analytical performance evaluation of massive MIMO techniques for SC-FDE modulations

Abstract: In the Fifth Generation of telecommunications networks (5G), it is possible to use massive Multiple Input Multiple Output (MIMO) systems, which require efficient receivers capable of reaching good performance values. MIMO systems can also be extended to massive MIMO (mMIMO) systems, while maintaining their, sometimes exceptional, performance. However, we must be aware that this implies an increase in the receiver complexity. Therefore, the use of mMIMO in 5G and future generations of mobile receivers will only be feasible if they use very efficient algorithms, so as to maintain their excellent performance, while coping with increasing and critical user demands. Having this in mind, this paper presents and compares three types of receivers used in MIMO systems, for further use with mMIMO systems, which use Single-Carrier with Frequency-Domain Equalization (SC-FDE), Iterative Block Decision Feedback Equalization (IB-DFE) and Maximum Ratio Combining (MRC) techniques. This paper presents and compares the theoretical and simulated performance values for these receivers in terms of their Bit Error Rate (BER) and correlation factor. While one of the receivers studied in this paper achieves a BER performance nearly matching the Matched Filter Bound (MFB), the other receivers (IB-DFE and MRC) are more than 1 dB away from MFB. The results obtained in this paper can help the development of ongoing research involving hybrid analog/digital receivers for 5G and future generations of mobile communications.
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Iterative receiver combining IB-DFE with MRC for massive MIMO schemes

Iterative receiver combining IB-DFE with MRC for massive MIMO schemes

Once we are moving to the 5G system it is imperative to reduce the complexity of massive MIMO (Multiple-Input, Multiple Output) receivers. This paper considers the uplink transmission using massive MIMO combined with SC-FDE (Single-Carrier with Frequency-Domain Equalization). We propose an iterative frequency-domain receiver merging IB-DFE (Iterative Block Decision-Feedback Equalizer) with MRC (Maximal Ratio Combining). We propose a novel approach to reduce the complexity of the receiver by avoiding matrix inversions while maintaining a level of performance very close to the Matched Filter Bound (MFB), which makes it an excellent option for 5G systems.
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Implicit Pilots for an Efficient Channel Estimation in Simplified Massive MIMO Schemes with Precoding

Implicit Pilots for an Efficient Channel Estimation in Simplified Massive MIMO Schemes with Precoding

Abstract—This paper proposes a new channel estimation scheme based on implicit pilots, optimized for a simplified massive multiple input, multiple output (MIMO), implemented with precoding, combined with single-carrier with frequency- domain equalization (SC-FDE) modulations. We propose an iterative receiver that considers an iterative detection with interference cancellation and channel estimation. The channel estimates are usually obtained with the help of pilot symbols and/or training sequences multiplexed with data symbols. Since the required overheads in massive MIMO schemes can be too high, leading to spectral degradation, the use of superimposed pilots (i.e., pilots added to data) is an efficient alternative. Three different types of pre-processing algorithms are considered in this paper: Zero Forcing Transmitter (ZFT), Maximum Ratio Transmitter (MRT), and Equal Gain Transmitter (EGT). The main advantage MRT and EGT is that they do not require matrix inversions. Nevertheless, some level of interference is generated in the decoding process. Such interference is mitigated by employing an optimized iterative receiver. By employing the proposed implicit pilots, the performance of MRT and EGT are very close to the matched filter bound just after a few iterations, even when the number of transmit or receiver antennas is not much higher than the number of data streams.
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Channel estimation techniques applied to massive MIMO systems using sparsity and statistics approaches tese dcaraújo

Channel estimation techniques applied to massive MIMO systems using sparsity and statistics approaches tese dcaraújo

Massive MIMO has the potential of greatly increasing the system spectral efficiency by employ- ing many individually steerable antenna elements at the base station (BS). This potential can only be achieved if the BS has sufficient channel state information (CSI) knowledge. The way of acquiring it depends on the duplexing mode employed by the communication system. Currently, frequency division duplexing (FDD) is the most used in the wireless communication system. However, the amount of overhead necessary to estimate the channel scales with the number of antennas which poses a big challenge in implementing massive MIMO systems with FDD protocol. To enable both operating together, this thesis tackles the channel estimation problem by proposing methods that exploit a compressed version of the massive MIMO channel. There are mainly two approaches used to achieve such a compression: sparsity and second order statistics. To derive sparsity-based techniques, this thesis uses a compressive sensing (CS) framework to extract a sparse-representation of the channel. This is investigated initially in a flat channel and afterwards in a frequency-selective one. In the former, we show that the Cramer-Rao lower bound (CRLB) for the problem is a function of pilot sequences that lead to a Grassmannian matrix. In the frequency-selective case, a novel estimator which combines CS and tensor analysis is derived. This new method uses the measurements obtained of the pilot subcarriers to esti- mate a sparse tensor channel representation. Assuming a Tucker3 model, the proposed solution maps the estimated sparse tensor to a full one which describes the spatial-frequency channel response. Furthermore, this thesis investigates the problem of updating the sparse basis that arises when the user is moving. In this study, an algorithm is proposed to track the arrival and departure directions using very few pilots. Besides the sparsity-based techniques, this thesis investigates the channel estimation performance using a statistical approach. In such a case, a new hybrid beamforming (HB) architecture is proposed to spatially multiplex the pilot sequences and to reduce the overhead. More specifically, the new solution creates a set of beams that is jointly calculated with the channel estimator and the pilot power allocation using the minimum mean square error (MMSE) criterion. We show that this provides enhanced performance for the estimation process in low signal-noise ratio (SNR) scenarios.
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Repositório Institucional UFC: Transceiver design for massive MIMO systems: approaches based on matrix completion, beam selection and random pilots

Repositório Institucional UFC: Transceiver design for massive MIMO systems: approaches based on matrix completion, beam selection and random pilots

In massive MIMO, the spatial focusing of energy into ever-smaller regions of space potentially brings huge improvements in throughput and radiated energy efficiency. Other benefits could also include the extensive use of inexpensive low-power components, low latency communication, simplification of the media access control (MAC) layer, and robustness to intentional jamming [4]. To fully utilize the benefits of such a promising technology, an accurate knowledge of the CSI at the BS is essential to apply linear precoders such as a simple MRT or a ZFBF. Henceforth, we focus on FDD operation. In this context, one well-known problem is that the channel feedback overhead grows linearly with the number of antennas [8, 50, 51, 52]. Then, for practical feedback channels with limited transmission rate, the overhead to obtain full CSI becomes prohibitively large due to the massive number of antenna elements. Thus, relying on CSI to design the downlink transmission emerges as a bottleneck in FDD systems. Furthermore, for practical feedback channels, the rate is limited and it is acceptable not to assume the transmission of full CSI to the BS. In this context, the availability of CSI at the transmitter to design the downlink transmission is a bottleneck in FDD systems. It is worth mentioning that in this thesis, we consider that the uplink feedback channel is error-free. Although this assumption may not hold in practice, good channel coding is usually applied to add robustness on the feedback channel against channel induced errors.
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Channel estimation in massive MIMO systems

Channel estimation in massive MIMO systems

5G is recognized to be the groundbreaking future of cellular networking. From 5G, massive MIMO is an innovative concept to revolutionize wireless communication systems, and it is intended to be implemented in the near future. This new concept, is capable of hundredfold growths in spectral efficiency and overall system’s performance by deploying a large-scale antenna arrays at the base stations. In this work, some problems are analyzed that arise with massive MIMO, more specifically, the increased complexity of channel estimation of large channel matrices and inter-cell interference caused by the reuse of training sequences in adjacent cells. As far as complexity concerns, matrix inversions and factorizations are the main problem of channel estimations techniques. A channel estimation technique with Zadoff-Chu sequences was introduced in order to replace channel estimation based on matrix inversions, such as the MMSE estimator. Moreover, pilot contamination was studied and three channel estimation techniques were proposed to achieve the best compromise between complexity, spectral efficiency and system performance.
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Massive MIMO transmission techniques

Massive MIMO transmission techniques

Diversas tecnologias diferentes estão a ser propostas para o 5G mas um consenso perante as principais tecnologias a adotar começa a emergir. Muito provavelmente, sistemas massive MIMO (Multiple Input Multiple Output) com esquemas de diretividade a operar no espectro de ondas milimétricas formarão a base tecnológica do 5G. À medida que as dificuldades de propagação das ondas milimétricas são superadas, todo o potencial de estruturas massive MIMO pode ser aproveitado. A presente dissertação propõe um novo sistema de transmissão com vectores bi-dimensionais de antenas que operam em frequências de ondas milimétricas, onde as múltiplas configurações de antenas podem ser utilizadas para obter ganhos muito elevados e transmissões directivas em comunicações ponto a ponto. Além destas vantagens, é proposta uma combinação de directividade na transmissão e na informação, permitindo maior isolamento entre utilizadores e proteção contra interceção e espionagem, assegurando também simultaneamente a amplificação eficiente de constelações multinível.
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Efficient frequency-domain detection for massive MIMO systems

Efficient frequency-domain detection for massive MIMO systems

This section presents a set of performance results concern- ing the receiver design proposed in this paper. We consider the uplink of a massive MIMO system with T = 4 single-antenna transmitters unless otherwise stated, each one employing an SC-FDE modulation and a receiver with R antennas. The blocks have N = 256 data symbols, each one selected from a QSPK constellation, plus an appropriate CP. The channel has 100 slots, symbol-spaced, equal-power multipath components. Similar conclusions could be drawn for other rich multipath propagation conditions. We consider uncorrelated Ryeligh fading for different multipath components and different links between transmit and receive antennas. We assume perfect synchronization and channel estimation. For the sake of com- parison, we also plot the MFB, which can be regarded as a lower bound on the optimum performance [16].
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Low complexity detection for SC-FDE massive MIMO systems

Low complexity detection for SC-FDE massive MIMO systems

The most important and motivating application for the discussion here is receivers for multiple-antennas such as massive MIMO systems, where several transmit antennas can simultaneously send multiple data streams. Essentially the same problem occurs in systems where the channel itself introduces time or fre- quency dispersion, in multiuser detection, and in cancellation of crosstalk. When the signal is a combination of several waves, the total signal amplitude may ex- perience deep fades over the time or space. The most popular way to combat this effect is to use some form of diversity combining. The presence of diversity also poses other interesting problem: how do we effectively use the information from all the antennas to demodulate the data?
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A novel massive MIMO for 5G systems

A novel massive MIMO for 5G systems

• Implementing the MRT/MRC and EGT/EGC algorithms for m-MIMO with mm- Wave, associated to the interference cancellation, we avoid the computation of the pseudo-inverse matrix, and therefore simplify the processing (either pre or post-processing), while achieving a performance very close to the MFB, especially with 4 iterations of the interference canceller.

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Optimization of Spectrum Management in Massive Array Antenna Systems with MIMO

Optimization of Spectrum Management in Massive Array Antenna Systems with MIMO

lines and Simulink-based project creation. The proposed project was implemented in code lines, thus having a somewhat more complex hardware complexity. USRP hardware supports not only Single Input Single Output (SISO) but also Multiplexed Multiple Input Multiple Output (MIMO) up to 2x2 array. Taking into account that several USRP can be used, together, we can get a massive MIMO (mMIMO) system, which considerably improves the transmitter and receiver bit rate. The programming of the USRP with in Matlab is initiated from base parameters such as gain, carrier frequency, clockRate, which is responsible for the amount of operations that can be performed at a given time. The corresponding interpolation Factor can increase the sampling rate by a factor multiplication in relation to the amount of sampling (nBloscks). There is a difference between the of transmitter (TX) at which the Sampling Frequency (FS) and is considered in the Digital Analog Conversion (DAC), and the in receiver where the Sampling Frequency (FS) and in the one from the Analog Digital Conversion (ADC). After configuring the interfaces, we request the USRP connection a by applying the ’findsdru’ command, which is responsible for synchronizing the USRP with Matlab. The connection made by the system generates equipment IP data, connection status, and the identification of the equipment and port to which it was connected. In order to be able to identify the behavior of the synchronism between a transmitter and a receiver, the major greatest difficulty is the implementation of multiplexing into the USRP. It was decided to start TX and RX code by a low complexity communication, in Matlab, and precisely the synchronization, the code allows for generating a signal with a fixed gain and fixed frequency. The transmitter/receiver has a dependence on the digital modulation. The receiver has the possibility of recognizing the signal emitted and which can be received in the most adequate work, taking into account that there is path loss plus noise/interference. The configuration of the modules is as shown in Figure 4.4. It implements SISO, with only one transmitter and one receiver.
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Pedro Alexandre Velez Fernandes

Pedro Alexandre Velez Fernandes

Wireless systems are one of the most used and more important communi- cation systems in the current days. In the past years, they have become the elec- tion choice for millions of users around the world, having democratize commu- nications. Such development allowed the world to be more connected than ever, connecting the most remote zones of the world to the rest of the world. Massive MIMO have the capability of expanding even more its influence, if they can in- crease the quality and capacity. If such objectives are obtained the wireless sys- tems will be able to become the most important communication system in use, substituting even optical fiber cable in some cases.
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End-to-end 5G testbed characterisation and improvements at 60GHz

End-to-end 5G testbed characterisation and improvements at 60GHz

In the scope of evaluating the system in a real-world user case, in the following section, the feasibility of this OFDM system in enabling an AR wireless transmission in a typical classroom environment, as seen in gure 5.1, is studied. This scenario intents to deliver multi-Gigabit/s of data transmission to multiple users (students) in LOS. As such, to guarantee the LOS between the transmitting antenna and the User Equipment (UE) and therefore mitigate the signal path loss associated with 60 GHz signals, a massive MIMO beamforming antenna array is employed. This array is connected to a server that is responsible for generating the virtual objects in the multiple OHMDs. In the receiver, an OHMD with a FPGA chip is responsible for the decoding, displaying and retransmission of inputs back to the server. FPGA chips are capable of providing the data processing requirements in an AR setting, while consuming up to 7x less power than modern Graphics Processing Units (GPUs) [2, 60]
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Design and Implementation of a FPGA and DSP Based MIMO Radar Imaging System

Design and Implementation of a FPGA and DSP Based MIMO Radar Imaging System

Accompanied by the emergence of a large number of theoretical results in MIMO radar [12]-[14], the research of engineering application is also in progress. Some MIMO radar systems have been developed for radar system perfor- mance analysis and algorithm verification[7]-[11], [15]. In [7], a 77-Ghz FMCW MIMO radar is implemented for 2D target localization using an SiGe single-chip transceiver. In [8], a 2D-MUSIC algorithm for joint estimation of angular and range target locations is applied to the MIMO radar sys- tem. As well as imaging resolution and measurement accu- racy, real-time performance is also a significant index for an imaging radar. In [9], a through-wall MIMO imaging radar system, which produces real-time imagery of targets at a frame rate of 0.5 Hz, is presented. The system controller and signal processing modules are implemented in LabView [16] running on a PC.
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Tensorbased MIMO relaying communication systems

Tensorbased MIMO relaying communication systems

a cette tˆache, l’utilisation de la d´ecomposition tensorielle PARAFAC pour mod´eliser les signaux re¸cus ne permet pas l’estimation conjointe des symboles et des canaux de communi- cation. Afin d’´eviter l’utilisation de symboles pilotes qui limite l’efficacit´e spectrale du fait de l’utilisation d’une partie de la largeur de bande pour l’estimation de canal, l’objectif de cette th`ese est de fournir de nouvelles approches tensorielles, en termes de syst`emes de transmis- sion et de r´ecepteurs semi-aveugles, pour des syst`emes de communication MIMO avec relai mono-directionnels, ` a deux sauts. Deux syst`emes de transmission sont propos´es en utilisant un codage spatio-temporel du type Khatri-Rao et deux strat´egies de traitement Amplify- and-Forward (AF) au relai. Pour ces syst`emes, appel´es PT2-AF et NP-AF, les signaux re¸cus au niveau de la destination satisfont respectivement des mod`eles tensoriels du type PARATUCK2 et nested PARAFAC. En exploitant les propri´et´es d’unicit´e de ces mod`eles tensoriels ´etablies dans la th`ese, plusieurs r´ecepteurs semi-aveugles sont d´eriv´es. Certains de ces r´ecepteurs sont du type ALS, tandis que d’autres sont des solutions non it´eratives bas´ees sur des factorisations de produits de Khatri-Rao. Des r´esultats de simulation sont pr´esent´es pour illustrer les performances des r´ecepteurs propos´es qui sont compar´es ` a des estimateurs supervis´es.
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