Problem
Research questions and friction points this paper is trying to address.
cell-free massive MIMO
energy efficiency
wireless fronthaul
adaptive quantization
integrated access and fronthaul
Energy-Efficient Integrated Access and Fronthaul for Cell-Free Massive MIMO with Adaptive Quantization Resolution
🤖 AI Summary
This work addresses the low energy efficiency in cell-free massive MIMO uplink systems for 6G, where access and fronthaul links share spectrum and fronthaul rates are constrained by quantization resolution. To tackle this, the paper presents the first unified framework that jointly optimizes access point (AP) selection, adaptive quantization bit allocation, fronthaul power control, and access/fronthaul resource allocation, supporting both time-division and frequency-division multiplexing modes while incorporating an AP sleep mechanism. Leveraging mixed-integer nonconvex fractional programming, an alternating optimization algorithm with block-wise optimality guarantees is developed, integrating closed-form time splitting, bandwidth bisection search, per-AP bit allocation, and end-to-end rate bounds derived via Lloyd–Max quantizers and Bussgang decomposition. Simulations demonstrate that the proposed framework significantly enhances energy efficiency under practical quantization and effectively handles diverse multiplexing modes.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
cell-free massive MIMO
adaptive quantization
energy efficiency
integrated access and fronthaul
mixed-integer optimization
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