A network energy-efficiency (EE) maximization framework for the uplink of acell-free massive MIMO with wireless fronthaul, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division and frequency-division operating modes in a unified manner.
Abstract
Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees---a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection---that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd--Max quantizers and a Bussgang decomposition-based achievable-rate bound.
A joint access and fronthaul resource allocation algorithm is proposed that maximizes the minimum user equipment (UE) spectral efficiency while satisfying fronthaul load constraints and shows that severe fronthaul limitations not only reduce UE rates but also introduce spatial performance disparities depending on the cloud location.
Ozan Alp Topal, Ozlem Tuugfe Demir, Emil Björnson et al.· arXiv.org· 0 citations
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.· IEEE Transactions on Wireles...· 0 citations
A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Z. Behdad, Ozlem Tuugfe Demir, Ki Won Sung et al.· arXiv.org· 0 citations
The proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions, indicating that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations
The proposed methods consistently outperform state-of-the-art greedy benchmarks, delivering noticeable improvements in energy efficiency for both conjugate beamforming and minimum mean square error (MMSE) processing, while simultaneously enhancing the energy–spectral efficiency tradeoff, which is typically difficult to improve without incurring penalties elsewhere.
Jan Garc'ia-Morales, A. de la Fuente, D. Gualda et al.· IEEE Open Journal of the Com...· 0 citations
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