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Cell-Free Massive MIMO Surveillance of Multiple Untrusted Communication Links

Zahra Mobini, Hien Quoc Ngo, Michail Matthaiou, Lajos Hanzo

TL;DR

This work studies wireless surveillance of multiple untrusted links using a CF-mMIMO framework with distributed, multi-antenna MNs that can act as observers or jammers. It derives closed-form MSP expressions under imperfect CSI for MR and PZF combining, and provides large-$M$ insights into interference and power scaling. A practical, long-term CSI-based optimization is proposed to jointly design MN mode assignment, power control, and MN-weighting, complemented by a greedy UT grouping strategy. Numerical results show substantial MSP gains over co-located full-duplex baselines, with up to roughly 30–40x improvements depending on the combining scheme and network scale, validating CF-mMIMO surveillance as a scalable approach for wide-area monitoring.

Abstract

A cell-free massive multiple-input multiple-output (CF-mMIMO) system is considered for enhancing the monitoring performance of wireless surveillance, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) proactively monitor multiple distributed untrusted communication links. We consider two types of MNs whose task is to either observe the untrusted transmitters or jam the untrusted receivers. We first analyze the performance of CF-mMIMO surveillance relying on both maximum ratio (MR) and partial zero-forcing (PZF) combining schemes and derive closed-form expressions for the monitoring success probability (MSP) of the MNs. We then propose a joint optimization technique that designs the MN mode assignment, power control, and MN-weighting coefficient control to enhance the MSP based on the long-term statistical channel state information knowledge. This challenging problem is effectively transformed into tractable forms and efficient algorithms are proposed for solving them. Numerical results show that our proposed CF-mMIMO surveillance system considerably improves the monitoring performance with respect to a full-duplex co-located massive MIMO proactive monitoring system. More particularly, when the untrusted pairs are distributed over a wide area and use the MR combining, the proposed solution provides nearly a thirty-fold improvement in the minimum MSP over the co-located massive MIMO baseline, and forty-fold improvement, when the PZF combining is employed.

Cell-Free Massive MIMO Surveillance of Multiple Untrusted Communication Links

TL;DR

This work studies wireless surveillance of multiple untrusted links using a CF-mMIMO framework with distributed, multi-antenna MNs that can act as observers or jammers. It derives closed-form MSP expressions under imperfect CSI for MR and PZF combining, and provides large- insights into interference and power scaling. A practical, long-term CSI-based optimization is proposed to jointly design MN mode assignment, power control, and MN-weighting, complemented by a greedy UT grouping strategy. Numerical results show substantial MSP gains over co-located full-duplex baselines, with up to roughly 30–40x improvements depending on the combining scheme and network scale, validating CF-mMIMO surveillance as a scalable approach for wide-area monitoring.

Abstract

A cell-free massive multiple-input multiple-output (CF-mMIMO) system is considered for enhancing the monitoring performance of wireless surveillance, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) proactively monitor multiple distributed untrusted communication links. We consider two types of MNs whose task is to either observe the untrusted transmitters or jam the untrusted receivers. We first analyze the performance of CF-mMIMO surveillance relying on both maximum ratio (MR) and partial zero-forcing (PZF) combining schemes and derive closed-form expressions for the monitoring success probability (MSP) of the MNs. We then propose a joint optimization technique that designs the MN mode assignment, power control, and MN-weighting coefficient control to enhance the MSP based on the long-term statistical channel state information knowledge. This challenging problem is effectively transformed into tractable forms and efficient algorithms are proposed for solving them. Numerical results show that our proposed CF-mMIMO surveillance system considerably improves the monitoring performance with respect to a full-duplex co-located massive MIMO proactive monitoring system. More particularly, when the untrusted pairs are distributed over a wide area and use the MR combining, the proposed solution provides nearly a thirty-fold improvement in the minimum MSP over the co-located massive MIMO baseline, and forty-fold improvement, when the PZF combining is employed.
Paper Structure (23 sections, 4 theorems, 70 equations, 5 figures, 1 table, 4 algorithms)

This paper contains 23 sections, 4 theorems, 70 equations, 5 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Knowledge Gap and Motivations
  3. Key Contributions
  4. System Model
  5. Combining Schemes
  6. Performance Analysis
  7. Effective SINR of the Untrusted Communication Links
  8. Effective SINR for Observing
  9. Large-$M$ Analysis
  10. Monitoring Success Probability
  11. Max-Min MSP Optimization
  12. Greedy MN Mode Assignment for Fixed Power Control and MN-Weighting Control
  13. Power Control for Fixed MN Mode Assignment and MN-Weighting Control
  14. MN-Weighting Control for Fixed MN Mode Assignment and Power Control
  15. Complexity and Convergence Analysis
  16. ...and 8 more sections

Key Result

Proposition 1

The effective SINR of the untrusted link $k$ can be formulated as where with ${\bf a} \triangleq \{a_m\}$ and $\boldsymbol{\theta}\triangleq \{\theta_{mk}\}$, $\forall m,k$, respectively.

Figures (5)

  • Figure 1: CF-mMIMO surveillance system model with the assigned MNs in observing mode and jamming mode along with the received desired and interference signals at a typical untrusted pair (UT $1$ and UR $1$).
  • Figure 2: Average minimum MSP with mode assignment Algorithm \ref{['alg:Grreedy']} and UT grouping Algorithm \ref{['alg:UTgrouping']}, where $N=12$, and $K=20$.
  • Figure 4: Average minimum MSP versus the size of the area, $D$, where $M=40$, $N=6$, and $K=20$.
  • Figure 5: Average minimum MSP versus number of MNs, $M$, where $N=4$, and $K=20$.
  • Figure 6: Average minimum MSP versus number of untrusted links, $K$, where $M=40$ and $N=6$.

Theorems & Definitions (8)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Proposition 4
  • proof