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Multiple-Target Detection in Cell-Free Massive MIMO-Assisted ISAC

Mohamed Elfiatoure, Mohammadali Mohammadi, Hien Quoc Ngo, Hyundong Shin, Michail Matthaiou

TL;DR

This work tackles joint downlink communication and multi-target sensing in a cell-free mMIMO ISAC system by introducing distributed APs that dynamically switch between communication and sensing modes. It combines PZF and MRT precoding, derives closed-form SE and MASR expressions, and analyzes asymptotic performance under two scaling regimes, revealing power-scaling laws that preserve performance as the network grows. A mixed-integer joint AP mode selection and power-control problem is formulated and solved via successive convex approximation, with a low-complexity greedy variant (JAP-OPA) that alternates AP mode assignment and power allocation. Numerical results show that the proposed JAP-OPA achieves 100% sensing success in tested setups and significantly improves UE SE fairness compared to baselines, highlighting the practicality and energy efficiency of distributed CF-mMIMO ISAC for multi-target sensing. The framework opens pathways to scalable, secure ISAC deployments with distributed sensing zones and robust performance in dense networks, driven by long-term CSI and convex optimization techniques.

Abstract

We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations' requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via successive convex approximation approach. We further propose a low-complexity design, where AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of $100\%$ for different network setups with a satisfactory fairness among all UEs.

Multiple-Target Detection in Cell-Free Massive MIMO-Assisted ISAC

TL;DR

This work tackles joint downlink communication and multi-target sensing in a cell-free mMIMO ISAC system by introducing distributed APs that dynamically switch between communication and sensing modes. It combines PZF and MRT precoding, derives closed-form SE and MASR expressions, and analyzes asymptotic performance under two scaling regimes, revealing power-scaling laws that preserve performance as the network grows. A mixed-integer joint AP mode selection and power-control problem is formulated and solved via successive convex approximation, with a low-complexity greedy variant (JAP-OPA) that alternates AP mode assignment and power allocation. Numerical results show that the proposed JAP-OPA achieves 100% sensing success in tested setups and significantly improves UE SE fairness compared to baselines, highlighting the practicality and energy efficiency of distributed CF-mMIMO ISAC for multi-target sensing. The framework opens pathways to scalable, secure ISAC deployments with distributed sensing zones and robust performance in dense networks, driven by long-term CSI and convex optimization techniques.

Abstract

We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations' requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via successive convex approximation approach. We further propose a low-complexity design, where AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of for different network setups with a satisfactory fairness among all UEs.
Paper Structure (23 sections, 2 theorems, 73 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 23 sections, 2 theorems, 73 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions
  4. Paper Organization and Notation
  5. System Model
  6. Channel Model and Uplink Training
  7. Probing and Data Signal Transmission
  8. Downlink Communication and SE
  9. Sensing Operation and MASR
  10. Asymptotic Analysis
  11. Case I ($M_s, M_c\to\infty$ and $N$ is fixed)
  12. Case II ($N\to\infty$ and $M_c$ and $M_s$ are fixed)
  13. Proposed Design Problems and Solution
  14. Joint AP Mode Selection and Power Allocation Design
  15. Low-Complexity Design
  16. ...and 8 more sections

Key Result

Proposition 1

The SE achieved by the PZF scheme is represented by eq:SE, where $\mathrm{SINR}_k$ is given in eq:dLSNIRf at the top of the next page.

Figures (7)

  • Figure 1: Illustration of the CF-mMIMO ISAC system.
  • Figure 2: Convergence behavior of Algorithm \ref{['alg1']} ($MN=480$, $K=4$, $L=2$, $\lambda=10$).
  • Figure 3: CDF of the per-UE SE and sensing success rate ($\kappa=8$ dB, $M=30$, $N=16$, $K=4$, and $L=2$).
  • Figure 4: CDF of the per-UE minimum SE for different schemes and for different number of sensing zones. The dashed lines depict results for $L=3$ while the solid lines show results for $L=2$ ($\kappa=6$ dB, $M=30$, $N=16$, $K=4$).
  • Figure 5: Average of the per-UE minimum SE and sensing success rate versus the number of APs ($\kappa=8$ dB, $MN=480$, $K=4$, and $L=2$).
  • ...and 2 more figures

Theorems & Definitions (6)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Lemma 1
  • proof