2024/03/28 by Minje Kim, Kim, Minje, In-Soo Kim +3 · 2 citations
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #FOS: Electrical engineering #Signal Processing (eess.SP) #Wireless Communication Networks Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.19132
openalex publication_date 2024/03/28 · openalex created_date 2024/03/30 · openalex updated_date 2026/07/28
Limited capacity of fronthaul links in a cell-free massive multiple-input multiple-output (MIMO) system can cause quantization errors at a central processing unit (CPU) during data transmission, complicating the centralized rate optimization problem. Addressing this challenge, we propose a harmony search (HS)-based algorithm that renders the combinatorial non-convex problem tractable. One of the distinctive features of our algorithm is its hierarchical structure: it first allocates resources at the access point (AP) level and subsequently optimizes for user equipment (UE), ensuring a more efficient and structured approach to resource allocation. Our proposed algorithm deals with rigorous conditions, such as asymmetric fronthaul bit allocation and distinct quantization error levels at each AP, which were not considered in previous works. We derive a closed-form expression of signal-to-interference-plusnoise ratio (SINR), in which additive quantization noise model (AQNM) based distortion error is taken into account, to define the mathematical expression of spectral efficiency (SE) for each UE. Also, we provide analyses on computational complexity and convergence to investigate the practicality of proposed algorithm. By leveraging various performance metrics such as total SE and max-min fairness, we demonstrate that the proposed algorithm can adaptively optimize the fronthaul bit allocation depending on system requirements. Finally, simulation results show that the proposed algorithm can achieve satisfactory performance while maintaining low computational complexity, as compared to the exhaustive search method