Joint AP-UE Association and Precoding for SIM-Aided Cell-Free Massive MIMO Systems

TL;DR

This work tackles the high cost and energy demands of dense CF mMIMO by integrating stacked intelligent metasurfaces (SIM) that perform wave-domain precoding. It formulates a joint AP-UE association and precoding problem, and solves it with a two-stage framework: first, AP-UE association via a greedy algorithm based on large-scale CSI, then an alternating optimization that alternates between AP power control (via complex quadratic transform) and SIM phase-shift design (via projection gradient ascent). The approach achieves substantial gains in sum rate (up to about 275% over benchmarks) and reveals that the number of APs and the SIM configuration critically affect performance, with an optimal deployment and SIM-layer configuration under a fixed asset budget. The results highlight SIMs as a promising, energy-efficient alternative to deploying additional APs, enabling scalable, high-capacity CF mMIMO in future networks.

Abstract

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

Figures (10)

• Figure 1: Illustration of SIM-aided CF mMIMO systems.
• Figure 2: Illustration of the transmission protocol.
• Figure 3: Average sum rate against the numbers of iterations ($L = 6$, $U = 2$, $M = 2$, $N = 25$, $K = 4$, ${d_x} = {d_y} = {\lambda \mathord{\left/ {\newline} \right. \nulldelimiterspace} 2}$).
• Figure 4: Sum rate against different numbers of APs ($U = 2$, $M = 2$, $N = 25$, $K = 4$, ${d_x} = {d_y} = {\lambda \mathord{\left/ {\newline} \right. \nulldelimiterspace} 2}$).
• Figure 5: Sum rate against different numbers of APs with the fixed total number of meta-atoms ($U = 2$, $M = 2$, $N_{\rm{total}} = 300$, $K = 4$, ${d_x} = {d_y} = {\lambda \mathord{\left/ {\newline} \right. \nulldelimiterspace} 2}$).

Theorems & Definitions (4)

• Remark 1
• Remark 2
• Remark 3
• Remark 4