# Performance Analysis and Fronthaul Quantization Bits Allocation in Cell-free ISAC System

**Authors:** Zhu, Deng, Xin, Xia, Han, Wang, Wang
**Year:** 2025
**DOI:** 10.1109/ICCC65529.2025.11148976
**URL:** https://doi.org/10.1109/ICCC65529.2025.11148976

## Abstract

In cell-free integrated sensing and communication system, the substantial information transmission need from access points to central processing unit for centralized communication decoding and sensing data fusion places a heavy burden on the fronthaul links and the finite fronthaul capacity in practice may cause severe performance degradation. To address this, we first analyze sensing and communication performance under fronthaul constraint and then formulate a multi-objective optimization problem to maximize both sensing and communication performance by allocating the fronthaul quantization bits. Considering about the non-convex nature of the bit allocation problem, we adopt a meta-heuristic algorithm, the non-dominated sorting genetic algorithm III to solve it. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the bit allocation algorithm. Also, the results highlight the tradeoffs between sensing and communication under finite fronthaul capacity and suggest optimal sensing strategies for varying fronthaul capacities.

## Citation Details

Performance Analysis and Fronthaul Quantization Bits Allocation in Cell-free ISAC System
**Authors**: Zhu, Deng, Xin, Xia, Han, Wang, Wang
**Year**: 2025
**DOI**: 10.1109/ICCC65529.2025.11148976
**URL**: https://doi.org/10.1109/ICCC65529.2025.11148976

**Abstract**: In cell-free integrated sensing and communication system, the substantial information transmission need from access points to central processing unit for centralized communication decoding and sensing data fusion places a heavy burden on the fronthaul links and the finite fronthaul capacity in practice may cause severe performance degradation. To address this, we first analyze sensing and communication performance under fronthaul constraint and then formulate a multi-objective optimization problem to maximize both sensing and communication performance by allocating the fronthaul quantization bits. Considering about the non-convex nature of the bit allocation problem, we adopt a meta-heuristic algorithm, the non-dominated sorting genetic algorithm III to solve it. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the bit allocation algorithm. Also, the results highlight the tradeoffs between sensing and communication under finite fronthaul capacity and suggest optimal sensing strategies for varying fronthaul capacities.  # Truncate if very long

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