Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network

TL;DR

This paper presents a joint optimization framework for radar sensing, location estimation, and resource allocation in 6G networks, improving system performance through spectrum sharing and advanced algorithms.

Contribution

It introduces a novel joint subcarrier and power optimization approach based on precise UE location sensing, considering multipath and Doppler effects, with a two-phase resource allocation process.

Findings

Location sensing accuracy significantly improved over benchmarks.

Energy consumption reduced through joint resource optimization.

System performance enhanced compared to baseline schemes.

Abstract

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE's location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.