Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems

TL;DR

This paper explores vector quantization methods for optimizing access point placement in cell-free massive MIMO systems, proposing scalable solutions that improve throughput and fairness.

Contribution

It introduces and compares three VQ-based approaches for AP placement, highlighting PDFVQ as a scalable method with favorable performance trade-offs.

Findings

VQ methods yield good sum and minimum rate performance.

PDFVQ offers a scalable, low-complexity placement solution.

VQ solutions serve as effective starting points for further optimization.

Abstract

We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput. In this regard, we formulate two main placement problems, namely the sum rate and minimum rate maximization problems, and discuss the challenges associated with solving the underlying optimization problem with the help of some simple scenarios. As a practical solution to the AP placement problem, we suggest a vector quantization (VQ) approach. The suitability of the VQ approach to cell-free AP placement is investigated by examining three VQ-based solutions. First, the standard VQ approach, that is the Lloyd algorithm (using the squared error distortion function) is described. Second, the tree-structured VQ (TSVQ), which performs successive partitioning of the distribution space is applied. Third, a probability density function optimized VQ (PDFVQ) procedure is outlined, which enables efficient, low complexity, and scalable placement, and is aimed at a massive distributed multiple-input-multiple-output (MIMO) scenario. While the VQ-based solutions do not solve the cell-free AP placement problems explicitly, numerical experiments show that their sum and minimum rate performances are good enough, and offer a good starting point for gradient-based optimization methods. Among the VQ solutions, PDFVQ, with advantages over the other VQ methods, offers a good trade-off between sum and minimum rates.