Leveraging PRS and PDSCH for Integrated Sensing and Communication Systems

TL;DR

This paper explores how existing 5G NR signals, specifically PRS and PDSCH, can be used for integrated sensing and communication, proposing new methods to eliminate ghost targets and optimize resource allocation.

Contribution

It introduces novel approaches for PRS-based sensing in 5G NR, leveraging PDSCH's DMRS to remove ghost targets and formulates a resource allocation framework for ISAC.

Findings

Effective ghost target removal using DMRS and PRS

Achieving Pareto optimality in resource allocation

Simulation confirms improved sensing and communication performance

Abstract

From the industrial standpoint on integrated sensing and communication (ISAC), the preference lies in augmenting existing infrastructure with sensing services while minimizing network changes and leveraging available resources. This paper investigates the potential of utilizing the existing infrastructure of fifth-generation (5G) new radio (NR) signals as defined by the 3rd generation partnership project (3GPP), particularly focusing on pilot signals for sensing within the ISAC framework. We propose to take advantage of the existing positioning reference signal (PRS) for sensing and the physical downlink shared channel (PDSCH) for communication, both readily available in 5G NR. However, the use of PRS for sensing poses challenges, leading to the appearance of ghost targets. To overcome this obstacle, we propose two innovative approaches for different PRS comb sizes within the ISAC framework, leveraging the demodulation reference signal (DMRS) within PDSCH to eliminate ghost targets. Subsequently, we formulate a resource allocation problem between PRS and PDSCH and determine the Pareto optimal point between communication and sensing without ghost targets. Through comprehensive simulation and analysis, we demonstrate that the joint exploitation of DMRS and PRS offers a promising solution for ghost target removal, while effective time and frequency resource allocation enables the achievement of Pareto optimality in ISAC.