Problem
Research questions and friction points this paper is trying to address.
Full-duplex massive MIMO
Integrated sensing communication and computing (ISCC)
Resource allocation
Task offloading
End-to-end latency
Performance Analysis and Sensing-Aware Resource Allocation for Full-Duplex Massive MIMO ISCC Networks
🤖 AI Summary
This study addresses the coupled interference between radar sensing and uplink communication, alongside stringent low-latency requirements for edge computing, in full-duplex massive MIMO integrated sensing, communication, and computation (ISCC) networks. To minimize end-to-end latency, this work jointly optimizes task offloading, transmit power, and computational resource allocation. By deriving the achievable communication rate bounds and the radar Cramér–Rao lower bound (CRLB) under zero-forcing reception, a successive convex approximation-based algorithm is proposed to solve the resulting non-convex resource allocation problem. Numerical results demonstrate that, while satisfying prescribed sensing accuracy constraints, the proposed scheme reduces radar transmit power by 86% and significantly outperforms both fixed-allocation and half-duplex baseline schemes.
📝 Abstract
Emerging edge applications must sense their environment and process data within tight latency budgets. Full-duplex (FD) integrated sensing, communication, and computing (ISCC) supports these functions concurrently, but radar probing interferes with uplink reception while user transmissions degrade target estimation. These coupled effects call for joint sensing and offloading decisions. We investigate a FD massive multiple-input multiple-output (mMIMO) ISCC network in which users partially offload tasks to an edge server while an access point probes a target. For zero-forcing uplink reception with imperfect channel estimates, we derive a tractable rate bound and target-angle Cramer-Rao lower bounds (CRLBs) under the stated interference and beam-alignment assumptions. We then jointly optimize offloading fractions, user and radar transmit powers, and edge computing resources to minimize average end-to-end task latency subject to sensing-accuracy, communication-rate, energy, deadline, and resource constraints. A successive convex approximation algorithm is developed to efficiently address the resulting nonconvex problem. The analysis shows that larger arrays improve sensing and uplink performance, but residual radar interference creates a finite rate ceiling when user powers scale down with array size. Simulation results show joint allocation reduces radar transmit power by approximately 86% relative to equal communication-user power at a -40 dB CRLB threshold and lowers latency compared with fixed-allocation and half-duplex schemes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Full-Duplex ISCC
Massive MIMO
Joint Resource Allocation
Successive Convex Approximation
Cramer-Rao Lower Bound
🔎 Similar Papers
No similar papers found.
