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Zih-Yi Liu

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2026

Wireless Fronthauls in Full-Duplex Cell-Free Massive MIMO Systems

In this paper, we investigate a full-duplex (FD) cell-free massive multiple-input multiple-output (CF mMIMO) architecture with millimeter wave (mmWave) fronthaul, where uplink (UL) and downlink (DL) payload data and control signaling must be simultaneously supported. We first revisit the fronthaul requirements of representative wired low physical layer functional splits and show that the FD operation further aggravates the wired fronthaul bottleneck. To improve scalability beyond purely wired deployments, we propose a wireless fronthaul architecture in which the fronthaul links between access points (APs) and the central processing unit (CPU) operate over mmWave bands that are spectrally disjoint from the sub-6 GHz access links. Then, instead of forwarding antenna-domain baseband samples, we exploit low-dimensional sufficient statistics and develop a wireless fronthaul transmission framework. For the DL, Gram-regularized zero-forcing (Gram-RZF) and Gram-weighted minimum mean-square error (Gram-WMMSE) beamforming methods are designed using user-domain Gram matrices, thereby avoiding the transport of instantaneous channel state information. For the UL, the remaining two phases convey local UL signal estimates and slow-timescale second-order moments, enabling centralized large-scale fading decoding (LSFD) at the CPU. All UL information is delivered through subspace-domain wireless fronthaul transmission, together with a receive-subspace demultiplexing mechanism at the CPU for reliable packet recovery. Numerical results validate the proposed framework, demonstrating remarkable improvements over conventional half-duplex CF mMIMO.

Zhilong Liu, Jiayi Zhang, Enyu Shi et al. · 0 citations
2026

Low-Overhead Distributed Power Control for Cell-Free Massive MIMO With Limited Fronthaul Capacity

Cell-free massive multiple-input multiple-output (CF mMIMO) requires effective power control, but centralized processing relies on global instantaneous channel state information (CSI) and creates heavy fronthaul load. This letter focuses on low-overhead distributed power control under limited fronthaul capacity. We propose an information bottleneck (IB)-based policy that exchanges compact latent messages instead of raw local observations, and we train it using cluster-based federated learning to keep raw CSI local. The IB penalty provides an explicit information-rate proxy for online coordination, while robustness under imperfect CSI and data locality are evaluated as supporting effects rather than formal guarantees. Simulations show that the proposed method approaches a centralized benchmark with much lower effective overhead and stable behavior under channel estimation errors.

Yukun Ma, Jiayi Zhang, Zih-Yi Liu et al. · 0 citations

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