A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks

We study the performance of antenna selection-based multiple-input-multiple-output networks with large-but-finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users' data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GAs).As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information, and channel models. Our results show that the proposed GA-based algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity

On the Throughput of Large-but-Finite MIMO Networks using Schedulers

This paper studies the sum throughput of the {multi-user} multiple-input-single-output (MISO) networks in the cases with large but finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closed-form expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly

SCHEDULING FOR MASSIVE MIMO USING CHANNEL AIGING UNDER QOS CONSTRAINTS

Massive multiple-input multiple-output (MIMO) networks support QoS (Quality of Service) by adding a new sublayer Service Data Adaption Protocol on the top of Packet Data Convergence Protocol layer to map between QoS flows and data radio bearers. In downlink for Guaranteed Bit Rate (GBR) flows, the gNB guarantees the Guaranteed Flow Bit Rate (GFBR) that defines the minimum bit rate the QoS flow can provide. So, one of the most important requirements is the minimum rate. The channel aiging helps to improve the sum-rate of Massive MIMO systems by serving more users to increase the spatial multiplexing gain without incurring additional pilot overhead. In this paper, a novel scheduler, termed QoS-Aware scheduling, is designed and proposed for Massive MIMO to use the channel aiging to increase the sum-rate but guarantee the minimum bit rate per user to support QoS. We investigate how many users are enough to serve to maximize the sum-rate while keeping the data rate per user meeting a given threshold. Through the numerical analysis we confirmed that QoS-Aware scheduling can guarantee a minimum rate per user and get a higher useful through-put (goodput) than conventional channel aiging schedulers

Multiuser Optimal Transmit Beamforming: Performance Studies, Antennas Selection, A Genetic Algorithm Approach

RÉSUMÉ La formation de faisceaux est une technique très prometteuse utilisant un grand nombre d'antennes pour transmettre un signal vers un ou plusieurs utilisateurs. L'objectif est d'augmenter la puissance du signal chez l'utilisateur souhaité et de réduire la puissance d'interférence chez les utilisateurs non visés. Étant donné que la transmission de la formation de faisceaux augmente la puissance dans une direction spécifique, cela permet à un accès multiple par division spatiale de servir plusieurs utilisateurs simultanément. Cependant, le problème est de garder un équilibre entre maximiser la puissance du signal et minimiser la puissance d'interférence dans les systèmes multi-utilisateurs. Cette thèse décrit une structure simple qui fournit une base théorique pour un système de formation de faisceau optimal. Dans cette thèse, nous étudions les propriétés des systèmes linéaires et optimaux dans différents scénarios, tels que les rapports des signaux faibles et élevés au bruit, des nombres multiple d'antennes, le canal à évanouissement de Rayleigh et les retards multiples. Nous analysons les scénarios lorsque la formation de faisceaux linéaires fonctionnent comme une formation de faisceau optimale. Ensuite, nous proposons une méthode simple pour sélectionner le nombre minimum d'antennes suffisantes pour satisfaire aux exigences de qualité de service des utilisateurs. Lorsque le nombre d’antennes à la station de base est très grand, il ne sera peut-être pas nécessaire d’utiliser toutes les antennes pour desservir seulement quelques utilisateurs. Cette situation incite à choisir un nombre d’antennes limité. Cependant, le nombre choisi peut ne pas suffire à satisfaire les exigences de qualité de service des utilisateurs en raison de fortes interférences, de conditions de canal et du nombre d'utilisateurs. Pour résoudre ce problème NP-difficile, il faut faire une recherche exhaustive ou une recherche heuristique des méthodes itératives avec un coût de complexité informatique acceptable. Ainsi, nous proposons un cadre simple pour sélectionner un ensemble d'antennes suffisantes pour satisfaire les besoins de l'utilisateur. Enfin, nous proposons un algorithme génétique pour une formation de faisceaux optimale avec une complexité d'implémentation faible. Considérant l'algorithme de réduction de branche comme une référence, nous comparons la performance de l'algorithme proposé dans différents scénarios.---------- ABSTRACT Transmit beamforming is a very promising technique to transmit the signal from a large array of antennas to one or multiple users. The goal is to increase the signal power at the desired user and reduce the interference power at the non-intended users. Since transmit beamforming increases the power to a specific direction, it allows for space division multiple access to serve multiple users simultaneously. However, the problem is to keep the balance between maximizing the signal power and minimizing the interference power in multi-user systems. This thesis describes a simple structure that provides a theoretical foundation for optimal beamforming scheme. In this thesis, we study the properties of linear and optimal beamforming schemes in different scenarios such as low to high signal to noise ratio ranges, multiple number of antennas, simple Rayleigh fading channel, Rayleigh fading channel with Doppler effects. We analyze the scenarios when linear beamforming performs as an optimal beamforming. Next, we propose a simple method to select the minimum number of antennas that is enough to satisfy the quality of service requirements of the users. In case of massive number of antennas at base station, it may not be necessary to use all antennas to serve only few users. That situation motivates the selection of a set of limited number of antennas. However, the number of chosen antennas may not be enough to satisfy the quality of service requirements of the users due to strong interference, channel conditions and number of users. To solve this NP-hard problem, it requires an exhaustive search or heuristic search, iterative methods with a cost of computational complexity. Thus, we propose a simple framework to select a set of antennas that is enough to satisfy the user’s requirements. Finally, we propose a genetic algorithm for optimal beamforming with low implementation complexity. Considering the branch reduce and bound algorithm as a benchmark, we compare the performance of the proposed algorithm in different scenarios