Joint Scheduling and Power Allocation with Per-User Rate Constraints for Uplink MU-MIMO OFDMA Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple access (OFDMA) systems. The objective is to minimize the number of occupied resource blocks (RBs) subject to per-user rate constraints. The problem is a mixed integer and non-convex programming problem. We first propose a hierarchical algorithm to find a solution, where in the outer layer the number of RBs are reduced in a greedy manner while in the inner layer the power allocation and scheduling of users are optimized to determine which RB should be unoccupied. The inner problem is non-convex high-complexity problem. To reduce the complexity, we further employ a deep neural network to learn the solution of the inner problem. Simulation results show that compared to two baseline methods, the proposed method can effectively reduce the occupied RBs with much lower complexity.

Original languageEnglish
Title of host publication2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350311143
DOIs
StatePublished - 2023
Event97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy
Duration: 20 Jun 202323 Jun 2023

Publication series

NameIEEE Vehicular Technology Conference
Volume2023-June
ISSN (Print)1550-2252

Conference

Conference97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/TerritoryItaly
CityFlorence
Period20/06/2323/06/23

Keywords

  • MU-MIMO OFDMA
  • deep learning
  • power allocation
  • scheduling

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