This paper presents an energy-efficient downlink cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) network that serves ultra-reliable low-latency communication (URLLC) users while simultaneously detecting a target. We propose a load-balancing algorithm that minimizes the total network power consumption; including transmit power, fixed static power, and traffic-dependent fronthaul power at the access points (APs) without degrading system performance. To this end, we formulate a mixed-integer non-convex optimization problem and introduce an iterative joint power allocation and AP load balancing (JPALB) algorithm. The algorithm aims to reduce total power usage while meeting both the communication quality-of-service (QoS) requirements of URLLC users and the sensing QoS needed for target detection. Proposed JPALB algorithm for ISAC systems was simulated with maximum-ratio transmission (MRT) and regularized zero-forcing (RZF) precoders. Simulation results show approximately 33% reduction in power consumption, using JPALB algorithm compared to a baseline with no load balancing, without compromising communication and sensing QoS requirements.

翻译：本文提出了一种面向超可靠低时延通信(URLLC)用户并同时进行目标检测的高能效下行链路无小区大规模多输入多输出集成感知与通信网络。我们提出了一种负载均衡算法，该算法在保证系统性能不降低的前提下，最小化包括接入点发射功率、固定静态功率和流量相关前传功率在内的总网络功耗。为此，我们构建了一个混合整数非凸优化问题，并提出了一种迭代的联合功率分配与接入点负载均衡算法。该算法旨在降低总功耗，同时满足URLLC用户的通信服务质量要求和目标检测所需的感知服务质量要求。所提出的面向ISAC系统的JPALB算法采用最大比传输和正则化迫零预编码器进行了仿真。仿真结果表明，与无负载均衡的基线方案相比，采用JPALB算法可在不损害通信与感知服务质量要求的前提下，实现约33%的功耗降低。