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Repeater-Assisted Massive MIMO Full-Duplex Communications

Mohammadali Mohammadi, Dhanushka Kudathanthirige, Himal A. Suraweera, Hien Quoc Ngo, Michail Matthaiou

TL;DR

This work tackles repeater-assisted massive MIMO full-duplex networks, formulating a nonconvex optimization to maximize the weighted minimum SE across DL and UL UEs via repeater weight tuning. It employs zero-forcing precoding/combining at the BS and a CCP-based iterative framework with feasible-point pursuit to handle nonconvexity and QoS constraints. The proposed algorithm achieves substantial SE gains over benchmarks, with FD operation and optimized repeater gains yielding up to 4x (DL) and 2.5x (UL) improvements and fairness improvements exceeding an order of magnitude over non-repeater systems. Overall, repeater swarms enable meaningful FD performance gains in RA mMIMO networks, offering practical enhancements for beyond-5G deployments by effectively managing repeater-induced interference and noise.

Abstract

We consider a wireless network comprising multiple singleantenna repeaters that amplify and instantaneously re-transmit received signals in a full-duplex (FD) communication setting. Specifically, we study a massive multiple-input multiple output base station that simultaneously serves multiple uplink (UL) and downlink (DL) user equipment (UE) over the same frequency band. The focus is on the problem of repeater weight optimization at each active repeater to maximize the sum of the weighted minimum spectral efficiencies (SEs) for both UL and DL UEs. The resulting non-convex optimization problem is tackled using a successive convex approximation technique. To demonstrate the effectiveness of the proposed approach, we evaluate its performance against benchmark systems with and without repeater assistance. The optimized FD design achieves SE improvements of up to 4-fold and 2.5-fold compared to its half-duplex counterpart.

Repeater-Assisted Massive MIMO Full-Duplex Communications

TL;DR

This work tackles repeater-assisted massive MIMO full-duplex networks, formulating a nonconvex optimization to maximize the weighted minimum SE across DL and UL UEs via repeater weight tuning. It employs zero-forcing precoding/combining at the BS and a CCP-based iterative framework with feasible-point pursuit to handle nonconvexity and QoS constraints. The proposed algorithm achieves substantial SE gains over benchmarks, with FD operation and optimized repeater gains yielding up to 4x (DL) and 2.5x (UL) improvements and fairness improvements exceeding an order of magnitude over non-repeater systems. Overall, repeater swarms enable meaningful FD performance gains in RA mMIMO networks, offering practical enhancements for beyond-5G deployments by effectively managing repeater-induced interference and noise.

Abstract

We consider a wireless network comprising multiple singleantenna repeaters that amplify and instantaneously re-transmit received signals in a full-duplex (FD) communication setting. Specifically, we study a massive multiple-input multiple output base station that simultaneously serves multiple uplink (UL) and downlink (DL) user equipment (UE) over the same frequency band. The focus is on the problem of repeater weight optimization at each active repeater to maximize the sum of the weighted minimum spectral efficiencies (SEs) for both UL and DL UEs. The resulting non-convex optimization problem is tackled using a successive convex approximation technique. To demonstrate the effectiveness of the proposed approach, we evaluate its performance against benchmark systems with and without repeater assistance. The optimized FD design achieves SE improvements of up to 4-fold and 2.5-fold compared to its half-duplex counterpart.
Paper Structure (9 sections, 11 equations, 3 figures)

This paper contains 9 sections, 11 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. DL Signaling
  5. UL Signaling
  6. ZF Precoding and Combining
  7. System Design Optimization
  8. Numerical Results and Discussion
  9. Conclusion

Figures (3)

  • Figure 1: The CDF of the DL per-UE SE for different system designs.
  • Figure 2: The CDF of the UL per-UE SE for different system designs.
  • Figure 3: The CDF of the objective function in ($\mathcal{P}1$) for different system designs.