Cooperative ISAC for Joint Localization and Velocity Estimation in Cell-Free MIMO Systems

TL;DR

This paper proposes a cooperative ISAC framework in cell-free MIMO systems that uses distributed autoencoders to accurately estimate target location and velocity while significantly reducing fronthaul signaling overhead.

Contribution

It introduces a novel distributed vector-quantized variational autoencoder (D-VQVAE) for efficient joint localization and velocity estimation with minimal data transmission.

Findings

D-VQVAE outperforms baseline schemes in sensing accuracy.

The proposed method reduces fronthaul signaling overhead by 99%.

End-to-end training enhances overall system performance.

Abstract

In this paper, we explore a cooperative integrated sensing and communication (ISAC) framework that utilizes orthogonal frequency division multiplexing (OFDM) waveforms. Under the control of a central processing unit (CPU), multiple access points (APs) collaboratively perform multistatic sensing while providing communication service in a cell-free multiple-input multiple-output (MIMO) system. Achieving high sensing accuracy requires the collection of global sensing information at the CPU, which can lead to significant fronthaul signaling overhead due to the feedback of the sensing signals from each AP. To tackle this issue, we propose a collaborative processing scheme in which the APs locally compress and quantize the received sensing signals before forwarding them to the CPU. The CPU then aggregates the information from all APs to estimate the location and velocity of the targets. We develop a distributed vector-quantized variational autoencoder (D-VQVAE) to enable an end-to-end implementation of this scheme. D-VQVAE consists of distributed encoders at the APs to locally encode the received sensing signals, codebooks for quantizing the encoded results, and a decoder at the CPU for location and velocity estimation. It effectively reduces the amount of data transmitted from each AP to the CPU while maintaining a high sensing accuracy. We employ a collaborative learning-assisted scheme to train D-VQVAE in an end-to-end manner. Simulation results show that the proposed D-VQVAE network outperforms the baseline schemes in sensing accuracy and reduces fronthaul signaling overhead by 99% when compared with the centralized sensing approach.