Abstract

This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three parts – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustering</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grouping</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scheduling</i> – and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.

Highlights

  • Nowadays, industry and academy intensified the research over Fifth Generation (5G) networks [1]

  • The same set of Space-Division Multiple Access (SDMA) groups is employed in all Scheduling Unit (SU) exploiting the channel hardening in massive Multiple Input Multiple Output (MIMO) systems [22]

  • We show in our results that this approach reduces the intra-cluster interference and, improves the overall system Spectral Efficiency (SE) while satisfying a required number of Mobile Stations (MSs) served in each service

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Summary

INTRODUCTION

Industry and academy intensified the research over Fifth Generation (5G) networks [1]. The proposed framework is used to solve the data rate maximization problem considering QoS requirements and a multi-service scenario, where it firstly clusterizes the MSs by means of a classification algorithm that exploits channel covariance matrices [8]. The same set of SDMA groups is employed in all SUs (whole bandwidth) exploiting the channel hardening in massive MIMO systems [22] Those groups are the input to a scheduling problem aiming at maximizing the total system data rate and satisfying the QoS requirements of the MSs. We show in our results that this approach reduces the intra-cluster interference and, improves the overall system SE while satisfying a required number of MSs served in each service.

SYSTEM MODEL
GROUPING PROBLEM
PROPOSED ALGORITHM FOR GROUPING
11: Put j in the last position in the set Jc
HYBRID BEAMFORMING AND DATA RATE
SCHEDULING PROBLEM
OPTIMAL SOLUTION
LOW COMPLEXITY SOLUTION
Findings
VIII. CONCLUSION
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