A Primer on MIMO Detection Algorithms for 5G Communication Network

In the recent past, demand for large use of mobile data has increased tremendously due to the proliferation of hand held devices which allows millions of people access to video streaming, VOIP and other internet related usage including machine to machine (M2M) communication. One of the anticipated attribute of the fifth generation (5G) network is its ability to meet this humongous data rate requirement in the order of 10s Gbps. A particular promising technology that can provide this desired performance if used in the 5G network is the massive multiple-input, multiple-output otherwise called the Massive MIMO. The use of massive MIMO in 5G cellular network where data rate of the order of 100x that of the current state of the art LTE-A is expected and high spectral efficiency with very low latency and low energy consumption, present a challenge in symbol/signal detection and parameter estimation as a result of the high dimension of the antenna elements required. One of the major bottlenecks in achieving the benefits of such massive MIMO systems is the problem of achieving detectors with realistic low complexity for such huge systems. We therefore review various MIMO detection algorithms aiming for low computational complexity with high performance and that scales well with increase in transmit antennas suitable for massive MIMO systems. We evaluate detection algorithms for small and medium dimension MIMO as well as a combination of some of them in order to achieve our above objectives. The review shows no single one detector can be said to be ideal for massive MIMO and that the low complexity with optimal performance detector suitable for 5G massive MIMO system is still an open research issue. A comprehensive review of such detection algorithms for massive MIMO was not presented in the literature which was achieved in this work

Low-Complexity Lattice Reduction Aided Schnorr Euchner Sphere Decoder Detection Schemes with MMSE and SIC Pre-processing for MIMO Wireless Communication Systems

© 2021, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00045The LRAD-MMSE-SIC-SE-SD (Lattice Reduction Aided Detection - Minimum Mean Squared Error-Successive Interference Cancellation - Schnorr Euchner - Sphere Decoder) detection scheme that introduces a trade-off between performance and computational complexity is proposed for Multiple-Input Multiple-Output (MIMO) in this paper. The Lenstra-Lenstra-Lovász (LLL) algorithm is employed to orthogonalise the channel matrix by transforming the signal space of the received signal into an equivalent reduced signal space. A novel Lattice Reduction aided SE-SD probing for the Closest Lattice Point in the transformed reduced signal space is hereby proposed. Correspondingly, the computational complexity of the proposed LRAD-MMSE-SIC-SE-SD detection scheme is independent of the constellation size while it is polynomial with reference to the number of antennas, and signal-to-noise-ratio (SNR). Performance results of the detection scheme indicate that SD complexity is significantly reduced at only marginal performance penalty

Symbol Detection in 5G and Beyond Networks

Beyond 5G networks are expected to provide excellent quality of service in terms of delay and reliability for users, where they could travel with high mobility (e.g., 500 km/h) and achieve better spectral efficiency. To support these demands, advanced wireless architectures have been proposed, i.e., orthogonal time frequency space (OTFS) modulation and multiple-input multiple-output (MIMO), which are used to handle high mobility communications and increase the network’s spectral efficiency, respectively. Symbol detection in these advanced wireless architectures is essential to satisfy reliability requirements. On the one hand, the optimal maximum likelihood symbol detector is prohibitively complex as its complexity is non-deterministic polynomial-time (NP)-hard. On the other hand, conventional low-complexity symbol detectors pose a significant performance loss compared to the optimal detector. Thus they cannot be used to satisfy high-reliability requirements. One solution to this problem is to develop a low-complexity algorithm that can achieve near-optimal performance in a particular scenario (e.g., M-MIMO). Nevertheless, there are some cases where we cannot design low-complexity algorithms. To alleviate this issue, deep learning networks can be integrated into an existing algorithm and trained using a dataset obtained by simulating a corresponding scenario. In this thesis, we design symbol detectors for advanced wireless architectures (i.e., MIMO and OTFS) to support an excellent quality of service in terms of delay and reliability and better spectral efficiency beyond 5G networks

Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems

This thesis fits into the Multiple-Input Multiple-Output (MIMO) communication systems. Nowadays, these schemes are the most promising technology in the field of wireless communications. The use of this technology allows to increase the rate and the quality of the transmission through the use of multiple antennas at the transmitter and receiver sides. Furthermore, the MIMO technology can also be used in a multiuser scenario, where a Base Station (BS) equipped with several antennas serves several users that share the spatial dimension causing interference. However, employing precoding algorithms the signal of the multiuser interference can be mitigated. For these reasons, the MIMO technology has become an essential key in many new generation communications standards. On the other hand, Massive MIMO technology or Large MIMO, where the BS is equipped with very large number of antennas (hundreds or thousands) serves many users in the same time-frequency resource. Nevertheless, the advantages provided by the MIMO technology entail a substantial increase in the computational cost. Therefore the design of low-complexity receivers is an important issue which is tackled throughout this thesis. To this end, one of the main contributions of this dissertation is the implementation of efficient soft-output detectors and precoding schemes. First, the problem of efficient soft detection with no iteration at the receiver has been addressed. A detailed overview of the most employed soft detectors is provided. Furthermore, the complexity and performance of these methods are evaluated and compared. Additionally, two low-complexity algorithms have been proposed. The first algorithm is based on the efficient Box Optimization Hard Detector (BOHD) algorithm and provides a low-complexity implementation achieving a suitable performance. The second algorithm tries to reduce the computational cost of the Subspace Marginalization with Interference Suppression (SUMIS) algorithm. Second, soft-input soft-output (SISO) detectors, which are included in an iterative receiver structure, have been investigated. An iterative receiver improves the performance with respect to no iteration, achieving a performance close to the channel capacity. In contrast, its computational cost becomes prohibitive. In this context, three algorithms are presented. Two of them achieve max-log performance reducing the complexity of standard SISO detectors. The last one achieves near max-log performance with low complexity. The precoding problem has been addressed in the third part of this thesis. An analysis of some of the most employed precoding techniques has been carried out. The algorithms have been compared in terms of performance and complexity. In this context, the impact of the channel matrix condition number on the performance of the precoders has been analyzed. This impact has been exploited to propose an hybrid precoding scheme that reduces the complexity of the previously proposed precoders. In addition, in Large MIMO systems, an alternative precoder scheme is proposed. In the last part of the thesis, parallel implementations of the SUMIS algorithm are presented. Several strategies for the parallelization of the algorithm are proposed and evaluated on two different platforms: multicore central processing unit (CPU) and graphics processing unit (GPU). The parallel implementations achieve a significant speedup compared to the CPU version. Therefore, these implementations allow to simulate a scalable quasi optimal soft detector in a Large MIMO system much faster than by conventional simuLa presente tesis se enmarca dentro de los sistemas de comunicaciones de múltiples antenas o sistemas MIMO. Hoy en día, estos sistemas presentan una de las tecnologías más prometedoras dentro de los sistemas comunicaciones inalámbricas. A través del uso de múltiples antenas en ambos lados, transmisor y receptor, la tasa de transmisión y la calidad de la misma es aumentada. Por otro lado, la tecnología MIMO puede ser utilizada en un escenario multiusuario, donde una estación base (BS) la cual está equipada con varias antenas, sirve a varios usuarios al mismo tiempo, estos usuarios comparten dimensión espacial causando interferencias multiusuario. Por todas estas razones, la tecnología MIMO ha sido adoptada en muchos de los estándares de comunicaciones de nueva generación. Por otro lado, la tecnología MIMO Masivo, en la cual la estación base está equipada con un gran número de antenas (cientos o miles) que sirve a muchos usuarios en el mismo recurso de tiempo-frecuencia. Sin embargo, las ventajas proporcionadas por los sistemas MIMO implican un aumento en el coste computacional requerido. Por ello, el diseño de receptores de baja complejidad es una cuestión importante en estos sistemas. Para conseguir esta finalidad, las principales contribuciones de la tesis se basan en la implementación de algoritmos de detección soft y esquemas de precodificación eficientes. En primer lugar, el problema de la detección soft eficiente en un sistema receptor sin iteración es abordado. Una descripción detallada sobre los detectores soft más empleados es presentada. Por otro lado, han sido propuestos dos algoritmos de bajo coste. El primer algoritmo está basado en el algoritmo Box Optimization Hard Detector (BOHD) y proporciona una baja complejidad de implementación logrando un buen rendimiento. El segundo de los algoritmos propuestos intenta reducir el coste computacional del conocido algoritmo Subspace Marginalization with Interference Suppression (SUMIS). En segundo lugar, han sido investidados detectores de entrada y salida soft (SISO, soft-input soft-output) los cuales son ejecutados en estructuras de recepción iterativa. El empleo de un receptor iterativo mejora el rendimiento del sistema con respecto a no realizar realimentación, pudiendo lograr la capacidad óptima. Por el contrario, el coste computacional se vuelve prohibitivo. En este contexto, tres algoritmos han sido presentados. Dos de ellos logran un rendimiento óptimo, reduciendo la complejidad de los detectores SISO óptimos que normalmente son empleados. Por el contrario, el otro algoritmo logra un rendimiento casi óptimo a baja complejidad. En la tercera parte, se ha abordado el problema de la precodificación. Se ha llevado a cabo un análisis de algunas de las técnicas de precodificación más usadas. En este contexto, se ha evaluado el impacto que el número de condición de la matriz de canal tiene en el rendimiento de los precodificadores. Además, se ha aprovechado este impacto para proponer un precodificador hibrido. Por otro lado, en MIMO Masivo, se ha propuesto un esquema precodificador. En la última parte de la tesis, la implementación paralela del algoritmo SUMIS es presentada. Varias estrategias sobre la paralelización del algoritmo han sido propuestas y evaluadas en dos plataformas diferentes: Unidad Central de Procesamiento multicore (multicore CPU) y Unidad de Procesamiento Gráfico (GPU). Las implementaciones paralelas consiguen una mejora de speedup. Estas implementaciones permiten simular para MIMO Masivo y de forma más rápida que por simulación convencional, un algoLa present tesi s'emmarca dins dels sistemes de comunicacions de múltiples antenes o sistemes MIMO. Avui dia, aquestos sistemes presenten una de les tecnologies més prometedora dins dels sistemes de comunicacions inalàmbriques. A través de l'ús de múltiples antenes en tots dos costats, transmissor y receptor, es pot augmentar la taxa de transmissió i la qualitat de la mateixa. D'altra banda, la tecnologia MIMO es pot utilitzar en un escenari multiusuari, on una estació base (BS) la qual està equipada amb diverses antenes serveix a diversos usuaris al mateix temps, aquests usuaris comparteixen dimensió espacial causant interferències multiusuari. Per totes aquestes raons, la tecnologia MIMO ha sigut adoptada en molts dels estàndars de comunicacions de nova generació. D'altra banda, la tecnologia MIMO Massiu, en la qual l'estació base està equipada amb un gran nombre d'antenes (centenars o milers) que serveix a molts usuaris en el mateix recurs de temps-freqüència. No obstant això, els avantatges proporcionats pels sistemes MIMO impliquen un augment en el cost computacional requerit. Per això, el disseny de receptors de baixa complexitat és una qüestió important en aquests sistemes. Per tal d'aconseguir esta finalitat, les principals contribucions de la tesi es basen en la implementació d'algoritmes de detecció soft i esquemes de precodificació eficients. En primer lloc, és abordat el problema de la detecció soft eficient en un sistema receptor sense interacció. Una descripció detallada dels detectors soft més emprats és presentada. D'altra banda, han sigut proposats dos algorismes de baix cost. El primer algorisme està basat en l'algorisme Box Optimization Hard Decoder (BOHD) i proporciona una baixa complexitat d'implementació aconseguint un bon resultat. El segon dels algorismes proposats intenta reduir el cost computacional del conegut algoritme Subspace Marginalization with Interference Suppression (SUMIS). En segon lloc, detectors d'entrada i eixidia soft (SISO, soft-input soft-output) els cuals són executats en estructures de recepció iterativa han sigut investigats. L'ocupació d'un receptor iteratiu millora el rendiment del sistema pel que fa a no realitzar realimentació, podent aconseguir la capacitat òptima. Per contra, el cost computacional es torna prohibitiu. En aquest context, tres algorismes han sigut presentats. Dos d'ells aconsegueixen un rendiment òptim, reduint la complexitat dels detectors SISO òptims que normalment són emprats. Per contra, l'altre algorisme aconsegueix un rendiment quasi òptim a baixa complexitat. En la tercera part, s'ha abordat el problema de la precodificació. S'ha dut a terme una anàlisi d'algunes de les tècniques de precodificació més usades, prestant especial atenció al seu rendiment i a la seua complexitat. Dins d'aquest context, l'impacte que el nombre de condició de la matriu de canal té en el rendiment dels precodificadors ha sigut avaluat. A més, aquest impacte ha sigut aprofitat per a proposar un precodificador híbrid , amb la finalitat de reduir la complexitat d'algorismes prèviament proposats. D'altra banda, en MIMO Massiu, un esquema precodificador ha sigut proposat. En l'última part, la implementació paral·lela de l'algorisme SUMIS és presentada. Diverses estratègies sobre la paral·lelizació de l'algorisme han sigut proposades i avaluades en dues plataformes diferents: multicore CPU i GPU. Les implementacions paral·leles aconsegueixen una millora de speedup quan el nombre d'àntenes o l'ordre de la constel·lació incrementen. D'aquesta manera, aquestes implementacions permeten simular per a MIMO Massiu, i de forma més ràpida que la simulació convencional.Simarro Haro, MDLA. (2017). Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86186TESI

Técnicas de equalização para MIMO massivo com amplificação não linear

The dawn of the new generation of mobile communications and the trafic explosion that derives from its implementation pose great challenge. The milimeter wave band and the use of massive number of antennas are technologies which, when combined, allow the transmission of high data rate, functioning in zones of the electromagnetic spectrum that are less explored and with capability of allocation of dozens of GHz of bandwidth. In this dissertation we consider a massive MIMO millimeter wave system employing a hybrid architecture, i.e., the number of transmit and receive antennas are lower than the number of radio frequency chains. As consequence, the precoder and equalizers should be designed in both digital and analog domains. In the literature, most of the proposed hybrid beamforming schemes were evaluated without considering the effects of nonlinear amplifications. However, these systems face non-avoidable nonlinear effects due to power amplifiers functioning in nonlinear regions. The strong nonlinear effects throughout the transmission chain will have a negative impact on the overall system performance and thus its study and the design of equalizers that take into account these effects are of paramount importance. This dissertation proposes a hybrid iterative equalizer for massive MIMO millimeter wave SC-FDMA systems. The user terminals have low complexity, just equipped with analog precoders based on average angle of departure, each with a single radio frequency chain. At the base station it is designed an hybrid analog-digital iterative equalizer with fully connected architecture in order to eliminate both the multi-user interference and the nonlinear distortion caused by signal amplification during the transmission. The equalizer is optimized by minimizing the bit error rate, which is equivalent to minimize the mean square error rate. The impact of the saturation threshold of the amplifiers in the system performance is analysed, and it is demonstrated that the iterative process can efficiently remove the multi-user interference and the distortion, improving the overall system performance.O surgimento de uma nova geração de comunicações móveis e a explosão de tráfego que advém da sua implementação apresenta grandes desafios. A banda de ondas milimétricas e o uso massivo de antenas são tecnologias que, combinadas, permitem atingir elevadas taxas de transmissão, funcionando em zonas do espectro electromagnético menos exploradas e com capacidade de alocação de dezenas de GHz para largura de banda. Nesta dissertação foi considerado um sistema de MIMO massivo de ondas milimétricas usando uma arquitectura híbrida, i.e., o número de antenas para transmissão e recepção é menor que o número de cadeias de radiofrequência. Consequentemente, o pré-codificador e equalizadores devem ser projectados nos domínios digital e analógico. Na literatura, a maioria dos esquemas híbridos de beamforming são avaliados sem ter em conta os efeitos de não linearidade da amplificação do sinal. No entanto, estes sistemas sofrem inevitavelmente de efeitos não lineares devido aos amplificadores de potência operarem em regiões não lineares. Os fortes efeitos das não-linearidades ao longo da cadeia de transmissão têm um efeito nefasto no desempenho do sistema e portanto o seu estudo e projecto de equalizadores que tenham em conta estes efeitos são de extrema importância. Esta dissertação propõe um equalizador híbrido para sistemas baseados em ondas milimétricas para MIMO massivo com modulação SC-FDMA. Os terminais de utilizador possuem baixa complexidade, equipados apenas com pré-codificadores analógicos baseados no ângulo médio de partida, cada um com uma única cadeia de radiofrequência. Na estação base é projectado um equalizador iterativo híbrido analógico-digital com arquitectura completamente conectada de modo a eliminar a interferencia multi-utilizador e a distorção causada pela amplificação do sinal aquando da transmissão. O equalizador é optimizado minimizando a taxa de erro de bit, o que é equivalente a minimizar a taxa de erro quadrático médio. O impacto do limiar de saturação dos amplificadores no desempenho do sistema é analisado, e é demonstrado que o processo iterativo consegue eliminar de modo eficiente a interferência multi-utilizador e a distorção, melhorando o desempenho do sistema.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers

Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference ability. Graph NNs (GNNs) have recently demonstrated outstanding capability in learning enhanced message passing rules and have shown success in overcoming the drawback of inaccurate Gaussian approximation of expectation propagation (EP)-based MIMO detectors. However, the application of the GNN-enhanced EP detector to MIMO turbo receivers is underexplored and non-trivial due to the requirement of extrinsic information for iterative processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo receivers, which realizes the turbo principle of generating extrinsic information from the MIMO detector through a specially designed training procedure. Additionally, an edge pruning strategy is designed to eliminate redundant connections in the original fully connected model of the GNN utilizing the correlation information inherently from the EP algorithm. Edge pruning reduces the computational cost dramatically and enables the network to focus more attention on the weights that are vital for performance. Simulation results and complexity analysis indicate that the proposed MIMO turbo receiver outperforms the EP turbo approaches by over 1 dB at the bit error rate of $10^{-5}$, exhibits performance equivalent to state-of-the-art receivers with 2.5 times shorter running time, and adapts to various scenarios.Comment: 15 pages, 12 figures, 2 tables. This paper has been accepted for publication by the IEEE Transactions on Signal Processing. Copyright may be transferred without notice, after which this version may no longer be accessibl