Queue-Aware Opportunistic Interference Alignment in Downlink MU-MIMO Cellular Systems

A. M. Benaya, Maha Elsabrouty, Osamu Muta

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, attention has been paid to the integration of opportunistic communications, whether based on opportunistic user selection (OUS) or opportunistic antenna selection, with interference alignment (IA) in order to improve the performance of wireless networks. In OUS, users that have the best operational conditions are usually selected. However, fairness among users is another important aspect that should be considered in scheduling users. In this paper, a queue-aware two-stage opportunistic IA (OIA) algorithm is proposed for the downlink multicell multiuser multiple-input-multiple-output system. In the first stage, inter-cell interference is eliminated using one pair of precoding/postcoding matrices. Whereas in the second stage, two user selection polices are proposed namely, capacity-based selection (CBS) and queue-based scheduling (QBS), to select a group of users and minimize the inter-user interference among them using another pair of precoding/postcoding matrices. In the QBS-OIA case, a joint scheduling, resource allocation, and IA optimization problems are formulated, and a low complexity heuristic is proposed to solve it. Comparisons are conducted with other OIA algorithms in terms of achieved sum rate, achieved degrees-of-freedom (DoFs), number of served users, queue overflow probability, and computational complexity. Simulations show that the two proposed CBS-OIA and QBS-OIA algorithms outperform other schemes in terms of sum rate and DoFs. Moreover, the proposed QBS-OIA is capable of serving more users, in some cases, and achieves lower overflow probability with much reduced complexity on the expense of achieving a bit lower sum rate than the CBS-OIA in some cases.

Original languageEnglish
Article number8458427
Pages (from-to)50860-50874
Number of pages15
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - Sep 8 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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