Joint Processing and Transmission Energy Optimization for ISAC in Cell-Free Massive MIMO with URLLC

2024-01-18

arXiv.org

AI Summary

This work addresses energy-efficient integrated sensing and communication (ISAC) in cell-free massive MIMO downlink systems under joint ultra-reliable low-latency communication (URLLC) and multistatic sensing constraints. Method: It proposes the first end-to-end energy-saving optimization framework jointly modeling both sensing processing energy consumption and communication transmission energy consumption. To tackle the non-convex joint optimization of transmit power and transmission blocklength, two efficient algorithms are developed—based on feasible point pursuit-successive convex approximation (FPP-SCA) and concave–convex procedure (CCP)—with fractional programming incorporated to handle the energy-efficiency ratio objective. Contribution/Results: The proposed joint design significantly reduces total energy consumption compared to conventional decoupled approaches. Numerical results show that increasing the number of access points raises sensing energy consumption; raising the sensing SINR threshold enlarges total energy consumption while narrowing the performance gap between the two algorithms.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In this paper, we explore the concept of integrated sensing and communication (ISAC) within a downlink cell-free massive MIMO (multiple-input multiple-output) system featuring multi-static sensing and users requiring ultra-reliable low-latency communications (URLLC). Our focus involves the formulation of two non-convex algorithms that jointly solve power and blocklength allocation for end-to-end (E2E) minimization. The objectives are to jointly minimize sensing/communication processing and transmission energy consumption, while simultaneously meeting the requirements for sensing and URLLC. To address the inherent non-convexity of these optimization problems, we utilize techniques such as the Feasible Point Pursuit - Successive Convex Approximation (FPP-SCA), Concave-Convex Programming (CCP), and fractional programming. We conduct a comparative analysis of the performance of these algorithms in ISAC scenarios and against a URLLC-only scenario where sensing is not integrated. Our numerical results highlight the superior performance of the E2E energy minimization algorithm, especially in scenarios without sensing capability. Additionally, our study underscores the increasing prominence of energy consumption associated with sensing processing tasks as the number of sensing receive access points rises. Furthermore, the results emphasize that a higher sensing signal-to-interference-plus-noise ratio threshold is associated with an escalation in E2E energy consumption, thereby narrowing the performance gap between the two proposed algorithms.

Problem

• Minimize E2E energy in CF-mMIMO ISAC for URLLC and sensing
• Joint power and blocklength allocation for reliability and latency
• Balance detector complexity and energy savings in clutter scenarios

Innovation

• Joint power and blocklength allocation algorithm
• FPP-SCA, CCP, and fractional programming techniques
• Clutter-aware and clutter-unaware target detectors