BS-UE association and power allocation in heterogeneous massive MIMO systems

Hangsong Yan, I. Tai Lu

Research output: Contribution to journalArticlepeer-review

Abstract

We consider a downlink Heterogeneous Massive Multiple-Input Multiple-Output (HM-MIMO) system with the macro-base station (BS) having hundreds of antennas and each of the micro-BSs having tens of antennas. Two lower bounds and one approximation of the achievable per-user equipment (UE) rate in closed forms under this system are derived and compared in terms of network utility. Using the derived approximation, three BS-UE association approaches are proposed. Firstly, a simple, sub-optimal, and heuristic multiple BS-UE association approach is designed, which achieves around 25% utility performance improvement compared with the conventional maximum received signal strength (Max-RSS) approach. Secondly, based on the results given by this heuristic approach, a learning approach for BS-UE association using convolutional neural network (CNN) is introduced. After being fully trained, the CNN can take any new BS-UE configuration as input and provide a sub-optimal BS-UE association for that configuration directly. It has only a small performance degradation compared with the proposed heuristic approach. Thirdly, realizing that the BS-UE connection probability in the proposed CNN architecture can be considered as a power allocation ratio, a combined power allocation and association approach is proposed. Its performance achieves as high as 60% utility improvement compared with the Max-RSS association and is also comparable to that achieved by the max-min power allocation approach which requires more than 10000× running time. It is remarkable that by using the gradients of the derived achievable per-UE rate approximation with respect to the power control coefficients, accurate target data is in fact not required for training in this approach.

Original languageEnglish (US)
Pages (from-to)184045-184060
Number of pages16
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Achievable Per-UE rate
  • BS-UE association
  • HM-MIMO
  • Network utility
  • Neural network
  • Power allocation

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'BS-UE association and power allocation in heterogeneous massive MIMO systems'. Together they form a unique fingerprint.

Cite this