# Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems

arXiv: https://arxiv.org/abs/2403.19132 · HTML: https://mlsanity.com/paper/2403.19132/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Five Disruptive Technology Directions for 5G](https://mlsanity.com/paper/1312.0229/) (2013)
- [Asymptotic Task-Based Quantization with Application to Massive MIMO](https://mlsanity.com/paper/1811.10077/) (2018)
- [Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO with Stochastic Geometry](https://mlsanity.com/paper/1710.00395/) (2017)
- [Cell-Free MmWave Massive MIMO Systems with Low-Capacity Fronthaul Links and Low-Resolution ADC/DACs](https://mlsanity.com/paper/2205.07598/) (2022)

## Built on by (most cited first, 2 of 2)

- [Mixed-Precision Quantization: Make the Best Use of Bits Where They Matter Most](https://mlsanity.com/paper/2412.03101/) (2024)
- [Joint Quantized Precoding and Bit Allocation for Fronthaul-Constrained Cell-Free Massive MIMO](https://mlsanity.com/paper/2609.26149/) (2026)

## Abstract (arXiv)

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