SCIENCE@home

Joint User Selection and Hybrid Precoder Design for Massive MIMO Systems

Hossein Vaezy,Steven D. Blostein

Published 2025 in IEEE Transactions on Signal Processing

ABSTRACT

Massive multiple-input multiple-output (MIMO) systems are a cornerstone of modern wireless communication, enabling significant improvements in capacity and reliability. However, the joint optimization of user selection and hybrid precoder/decoder design remains challenging due to the complexity introduced by spatial correlation, noisy channel information, and the non-convex nature of the problem. This paper addresses these challenges by considering the downlink of multi-user massive MIMO systems. A noisy version of channel information with spatial correlation between antennas is assumed to be available at the transmitter, and an optimization problem is formulated for joint user selection and hybrid analog/digital precoder design. The total sum rate of the network is considered as a design metric that leads to non-convex and NP-hard mixed-integer optimization. To address the non-convexity, an iterative method is proposed which results in multiple simpler bounding and relaxed convex sub-problems with closed-form solutions for analog precoders/decoders, digital decoders, and user selection. As a by-product, the proposed algorithm also optimizes the number of selected users with perfect or imperfect channel state information (CSI). A generalized user selection metric is also derived for massive MIMO systems with multiple-antenna users under both perfect and imperfect CSI, and is further analyzed for specific scenarios such as ZF, MRT, block diagonalized precoders, and large-scale MIMO settings. Finally, the method is extended to finite-resolution phase shifters and assessed for Rayleigh fading channels. The simulation results show that the proposed method performs favorably compared to other recent joint user selection and precoder designs.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Transactions on Signal Processing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/TSP.2025.3562858
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-47 of 47 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1