Dual-Scale Channel Estimation in Sensing-Assisted Communication Systems: Joint Time Allocation and Beamforming Design

TL;DR

This paper introduces a dual-scale channel estimation framework for sensing-assisted communication systems, optimizing time allocation and beamforming to improve data rates by leveraging sensing data and modeling channel variations.

Contribution

It proposes a novel integrated sensing and communication framework with joint optimization of sensing duration and update timing, addressing dual-scale channel estimation errors.

Findings

Achieves significant performance improvement over benchmarks.

Effectively models the impact of sensing errors on communication.

Optimizes time resources for enhanced data rates.

Abstract

In this paper, we propose a novel integrated sensing and communication (ISAC)-enabled dual-scale channel estimation framework, where large-scale channel estimation benefits from sensing, and the temporal variation of small-scale channel state information is modeled via channel aging. By characterizing the impact of angular sensing error on the communication spatial correlation matrix, we derive a closed form expression for the achievable rate under dual-scale channel estimation errors. Considering the different characteristics in time scales, we design the sensing duration for slow-varying large-scale channel and determine the update timing and frequency for fast-varying small-scale channel information within a given frame structure. We formulate an average achievable rate maximization problem under limited time resources and sensing Cramer-Rao bound (CRB) constraints, and propose a segmented golden based joint optimization algorithm to efficiently solve this nonconvex problem. Simulation results demonstrate that our proposed scheme achieves significant performance improvement compared with the benchmark schemes, which further validate that the system can leverage additional sensing capabilities to enhance communication efficiency.