Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays

This work analyzes the performance of the implementable detectors for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system condi- tions, including antenna correlation and array configuration. Time-domain channel model has been used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order and antenna arrays configurations. A bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high performance combined with high capacity systems and manageable computational complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.Comment: 26 pgs, 16 figures and 5 table

A universal space-time architecture for multiple-antenna aided systems

In this tutorial, we first review the family of conventional multiple-antenna techniques, and then we provide a general overview of the recent concept of the powerful Multiple-Input Multiple-Output (MIMO) family based on a universal Space-Time Shift Keying (STSK) philosophy. When appropriately configured, the proposed STSK scheme has the potential of outperforming conventional MIMO arrangements

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Quantum search algorithms, quantum wireless, and a low-complexity maximum likelihood iterative quantum multi-user detector design

The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a function’s evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUD’s computational complexity is significantly lower

Self-diagnosing and optimization of low coverage and high interference in 3G/4G: radio access networks

Mestrado em Engenharia Electrónica e Telecomunicações - DissertaçãoSelf-Organizing Networks (SON) solutions have been developed and implemented in the last years as a Mobile Network Operator (MNO) strategy to deal with the complexity of current networks. This research work, focuses on the self-optimization branch of SON solutions. It aims to empower a network with automatic capabilities for detecting and optimizing poor Radio Frequency (RF) performance scenarios. The detection and optimization of those scenarios, is based on Drive Test (DT) data. This leads to the development of a DT classi cation model to assert the quality of data collected through DT for a given cell, as it supports all decision making in terms of detection and optimization of poor RF situations. The DT model was calibrated with subjective testing in the form of inquiries made to fty Radio Access Network (RAN) engineers. Three algorithms were implemented for detection of low coverage and high interference scenarios. Besides identifying and dividing into clusters the DT data that denotes each problem, harshness metrics at cell and cluster level allow to identify the most severe situations. Moreover, an antenna physical parameter optimization algorithm, based on a Particle Swarm Optimization (PSO) algorithm, is able to purpose new Electrical Downtilt (EDT), Mechanical Downtilt (MDT) or the antenna orientation to improve or x the detected RF problems. All algorithms were tested with real MNO DT data and network topology, mainly on urban scenarios, where the detection and optimization is more critical for MNO. Regarding the detection algorithms, in urban scenario, it was established that the situations of high interference were more prevailing than the low coverage. The antenna self-optimization algorithm achieved an average gain of 78% on the tested cases.As redes SON têm sido, cada vez mais, uma das fortes apostas por parte das operadoras móveis para fazer face a crescente complexidade das redes móveis. Este trabalho de pesquisa foca-se no ramo, das redes SON, de optimização automática. O objectivo e dotar uma rede móvel de capacidades de detecção e optimização de situações de má performance rádio. Tendo em conta que toda a detecção e optimização e baseada em dados recolhidos por DT, surgiu a necessidade de desenvolver um modelo de qualidade para DT. Este modelo e usado como referência em termos de qualidade de dados disponíveis, para cada célula analisada. O modelo de qualidade de DT foi calibrado através de questionários subjectivos, realizados a cinquenta engenheiros rádio. Foram implementados três algoritmos para detecção de situações de má cobertura e interferência. Além de identificar e dividir em clusters os dados de DT com cada um dos problemas mencionados, as métricas de gravidade ao nível do cluster e da célula, permitem identificar os cenários mais graves. Em termos de optimizaçãoo, foi desenvolvido e implementado um algoritmo de optimização de tilts eléctrico e mecânico ou a orientação da antena, com base num algoritmo PSO. Todos os algoritmos foram testados com dados reais de DT e de topologia de rede, principalmente em cenários urbanos. No que diz respeito aos algoritmos de detecção, em cenário urbano, foi concluído que as situações de excesso de interferência são mais abundantes do que as situações de má cobertura. O algoritmo de optimização dos parâmetros físicos de antenas, para os casos testados, obteve um ganho médio de 78%.N/

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multi-output (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radio-frequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users, and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm