We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations' requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via successive convex approximation approach. We further propose a low-complexity design, where AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of $100\%$ for different network setups with a satisfactory fairness among all UEs.

翻译：针对基于无小区大规模多输入多输出（CF-mMIMO）架构的通信与感知一体化（ISAC）系统，我们提出了一种分布式实现方案。分布式多天线接入点（AP）同时为通信用户（UE）提供服务，并向多个指定区域发射探测信号以实现感知功能。AP可在通信与感知模式间切换，并根据网络配置及感知与通信操作的实时需求调整发射功率。通过分别在AP处采用面向通信的局部迫零预编码与面向感知的最大比传输预编码，我们首先推导出用户频谱效率（SE）和感知区域主瓣平均旁瓣比（MASR）的闭式表达式。随后，构建联合操作模式选择与功率控制优化问题，以在保证感知区域特定MASR水平的前提下最大化用户间的SE公平性。该复杂混合整数问题通过逐次凸近似方法进行松弛求解。进一步地，我们提出一种低复杂度设计方案：通过贪心算法确定AP模式选择，再基于选定模式设计功率控制策略。研究结果表明，所提方案在不同网络配置下均能稳定实现$100\%$的感知成功率，同时保持所有用户间良好的公平性。