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 next generation WLANs: filling the gap between offered and observed data rates

In wireless networks, opportunistic scheduling is used to increase system throughput by exploiting multi-user diversity. Although recent advances have increased physical layer data rates supported in wireless local area networks (WLANs), actual throughput realized are significantly lower due to overhead. Accordingly, the frame aggregation concept is used in next generation WLANs to improve efficiency. However, with frame aggregation, traditional opportunistic schemes are no longer optimal. In this paper, we propose schedulers that take queue and channel conditions into account jointly, to maximize throughput observed at the users for next generation WLANs. We also extend this work to design two schedulers that perform block scheduling for maximizing network throughput over multiple transmission sequences. For these schedulers, which make decisions over long time durations, we model the system using queueing theory and determine users' temporal access proportions according to this model. Through detailed simulations, we show that all our proposed algorithms offer significant throughput improvement, better fairness, and much lower delay compared with traditional opportunistic schedulers, facilitating the practical use of the evolving standard for next generation wireless networks

Proportional Fair MU-MIMO in 802.11 WLANs

We consider the proportional fair rate allocation in an 802.11 WLAN that supports multi-user MIMO (MU-MIMO) transmission by one or more stations. We characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities. While a number of features carry over from the case without MU-MIMO, in general neither flows nor stations need to be allocated equal airtime when MU-MIMO is available

A Genetic Algorithm-based Beamforming Approach for Delay-constrained Networks

In this paper, we study the performance of initial access beamforming schemes in the cases with large but finite number of transmit antennas and users. Particularly, we develop an efficient beamforming scheme using genetic algorithms. Moreover, taking the millimeter wave communication characteristics and different metrics into account, we investigate the effect of various parameters such as number of antennas/receivers, beamforming resolution as well as hardware impairments on the system performance. As shown, our proposed algorithm is generic in the sense that it can be effectively applied with different channel models, metrics and beamforming methods. Also, our results indicate that the proposed scheme can reach (almost) the same end-to-end throughput as the exhaustive search-based optimal approach with considerably less implementation complexity

Coordinated Multi-Point MIMO Processing for 4G

The concept of cooperative Multiple-Input-Multiple-Output (MIMO), also referred to as network MIMO, or as Coordinated Multi-Point Transmission (CoMP), was standardized in 3GPP Release 11. The goal of CoMP is to improve the coverage of high data rates and cell-edge throughput, and also to increase system throughput. In this paper we analyze only the latter scenario, using system level simulations in accordance with 3GPP guidelines. It is shown that the use of joint coordinated multipoint transmission achieves additional throughput gains. However, the gains depend on the scheduling type. This paper also indicates that the criterion of fairness is an important parameter when the number of users is high

An open source multi-slice cell capacity framework

Número especial con los mejores papers de 2021.5G is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. Network Slicing is a key paradigm in 5G, and future 6G networks will inherit it for the concurrent provisioning of diverse quality of service. As scheduling is always a delicate vendor topic and there are few free and complete simulation tools to support all 5G features, in this paper, we present Py5cheSim. This is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network level. It offers an environment that allows the development of new scheduling algorithms in a researcher-friendly way without the need of detailed knowledge of the core of the tool. The present work describes its design and implementation choices, the validation process, the results and different use cases.Proyecto: FVF-2021-128– DICYT. Fondo Carlos Vaz Ferreira, Convocatoria 2021, Dirección Nacional de Innovación, Ciencia y Tecnología, Ministerio de Educación y Cultura, UruguayProyecto: FMV_1_2019_1_155700 "Inteligencia Artificial aplicada a redes 5G", Agencia Nacional de Investigación e Innovación, Urugua