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

TL;DR

A collaborative processing scheme in which the APs locally compress and quantize the received sensing signals before forwarding them to the CPU, which effectively reduces the amount of data transmitted from each AP to the CPU while maintaining a high sensing accuracy.

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.

Figures (14)

• Figure 1: Illustration of cooperative ISAC for target sensing in a cell-free MIMO system. The transmit APs send OFDM signals to multiple users for communication, and these signals are reflected by the targets. The reflected sensing signals are collected by the receive APs.
• Figure 2: AoD $\theta$ and AoA $\vartheta$ with respect to (w.r.t.) a point-like target. Two different orientations of the transmit and receive ULAs are shown in (a) and (b). In (a), the transmit and receive ULAs are aligned in opposite directions. In (b), the transmit and receive ULAs are oriented with a phase shift between $90^{\circ}$ and $180^{\circ}$ relative to each other.
• Figure 3: The architecture of the proposed D-VQVAE network. Each receive AP uses an encoder to encode the obtained sensing signals locally, followed by vector quantization based on a codebook. The indices of the selected codewords are forwarded to the CPU. The CPU recovers the discrete latent feature vectors based on the indices received. Finally, the location and velocity of the targets are estimated by a decoder.
• Figure 4: Each AP encodes the reflected sensing signals locally through its encoder, then quantizes the encoded feature using a codebook. The CPU estimates the location and velocity of the targets through a decoder.
• Figure 5: The topology considered in simulations. For different channel realizations, the locations of the APs are fixed, while the users and targets are randomly distributed in the $100\times 100$ m$^2$ area.