Joint Transmission and Compression Optimization for Networked Sensing with Limited-Capacity Fronthaul Links

TL;DR

This paper develops a joint transmission and compression optimization framework for networked sensing in cellular networks with limited fronthaul capacity, improving target localization accuracy through novel algorithms and strategies.

Contribution

It introduces a new joint design approach for transmission and compression strategies based on PCRB minimization, with efficient algorithms and a novel estimate-then-beamform-then-compress strategy for massive antenna scenarios.

Findings

The proposed algorithms effectively minimize the PCRB under power and fronthaul constraints.

The estimate-then-beamform-then-compress strategy enhances localization in massive antenna setups.

Numerical results demonstrate the superiority of the proposed methods over baseline approaches.

Abstract

This paper considers networked sensing in cellular network, where multiple base stations (BSs) first compress their received echo signals from multiple targets and then forward the quantized signals to the central unit (CU) via limited-capacity fronthaul links, such that the CU can leverage all useful echo signals to perform high-resolution localization. Under this setup, we manage to characterize the posterior Cramer-Rao Bound (PCRB) for localizing all the targets with random positions as a function of the transmit covariance matrix and the compression noise covariance matrix of each BS. Then, a PCRB minimization problem subject to the transmit power constraints and the fronthaul capacity constraints is formulated to jointly design the BSs' transmission and compression strategies. We propose an efficient algorithm to solve this problem based on the alternating optimization technique. Specifically, it is shown that when either the transmit covariance matrices or the compression noise covariance matrices are fixed, the successive convex approximation (SCA) technique can be leveraged to optimize the other type of covariance matrices locally optimally. Moreover, we also propose a novel estimate-then-beamform-then-compress strategy for the massive receive antenna scenario, under which each BS first estimates targets' angle-of-arrivals (AOAs) locally, then beamforms its high-dimension received signals into low-dimension ones based on the estimated AOAs, and last compresses the beamformed signals for fronthaul transmission. An efficient beamforming and compression design method is devised under this strategy. Numerical results are provided to verify the effectiveness of our proposed algorithms.