[Paper Review] Design and Performance Evaluation of Joint Sensing and Communication Integrated System for 5G MmWave Enabled CAVs

Qixun Zhang, Xinna Wang+2 more|arXiv (Cornell University)|Aug 24, 2021

Wireless Body Area Networks38 references133 citations

TL;DR

This paper proposes a joint sensing and communication integrated system (JSCIS) for 5G mmWave-enabled connected automated vehicles (CAVs), using a time-division frame structure based on 5G New Radio to dynamically allocate time between radar sensing and high-rate data communication. By modeling the system as an M/M/1 queue with Age of Information (AoI) and formulating resource allocation as a non-cooperative game, the proposed CTRA algorithm achieves a 26% improvement in radar mutual information and enables stable 2.8 Gbps communication with ±0.25 m ranging accuracy at 28 GHz.

ABSTRACT

The safety of connected automated vehicles (CAVs) relies on the reliable and efficient raw data sharing from multiple types of sensors. The 5G millimeter wave (mmWave) communication technology can enhance the environment sensing ability of different isolated vehicles. In this paper, a joint sensing and communication integrated system (JSCIS) is designed to support the dynamic frame structure configuration for sensing and communication dual functions based on the 5G New Radio protocol in the mmWave frequency band, which can solve the low latency and high data rate problems of raw sensing data sharing among CAVs. To evaluate the timeliness of raw sensing data transmission, the best time duration allocation ratio of sensing and communication dual functions for one vehicle is achieved by modeling the M/M/1 queuing problem using the age of information (AoI) in this paper. Furthermore, the resource allocation optimization problem among multiple CAVs is formulated as a non-cooperative game using the radar mutual information as a key indicator. And the feasibility and existence of pure strategy Nash equilibrium (NE) are proved theoretically, and a centralized time resource allocation (CTRA) algorithm is proposed to achieve the best feasible pure strategy NE. Finally, both simulation and hardware testbed are designed, and the results show that the proposed CTRA algorithm can improve the radar total mutual information by 26%, and the feasibility of the proposed JSCIS is achieved with an acceptable radar ranging accuracy within 0.25 m, as well as a stable data rate of 2.8 Gbps using the 28 GHz mmWave frequency band.

Motivation & Objective

• To address the challenge of low-latency, high-rate raw sensing data sharing among CAVs in dynamic vehicular environments.
• To integrate radar sensing and 5G mmWave communication functions within a single system to reduce hardware and signaling overhead.
• To optimize time resource allocation between sensing and communication for minimal age of information (AoI) and maximum radar mutual information.
• To design a feasible and stable hardware testbed for real-time validation of the integrated system at 28 GHz.

Proposed method

• Designs a time-division duplex (TDD) frame structure based on 5G NR, allocating subframes to radar sensing and communication via configurable slot allocation (SA1 and SA2).
• Models the sensing-to-communication data transmission link as an M/M/1 queuing system to analyze age of information (AoI) and derive optimal time allocation ratios.
• Formulates the multi-CAV resource allocation problem as a non-cooperative game using radar mutual information as the utility function.
• Proves the existence and feasibility of a pure strategy Nash equilibrium (NE) in the game-theoretic framework.
• Proposes a centralized time resource allocation (CTRA) algorithm to achieve the optimal feasible pure strategy NE.
• Develops a hardware testbed using two NI 5G mmWave platforms, 64-element phased array antennas, and horn antennas at 28 GHz to validate system performance.

Experimental results

Research questions

• RQ1 What is the optimal time duration allocation ratio between sensing and communication for a single CAV to minimize information staleness, as measured by Age of Information (AoI)?
• RQ2 How can resource allocation among multiple CAVs be optimized to balance sensing and communication performance?
• RQ3 Does a pure strategy Nash equilibrium exist in the non-cooperative game formulation of the joint sensing and communication system?
• RQ4 What is the achievable radar ranging accuracy and communication data rate in a real-world hardware implementation of the JSCIS at 28 GHz?
• RQ5 How does the proposed CTRA algorithm compare to other resource allocation strategies in terms of radar mutual information and system convergence?

Key findings

• The CTRA algorithm improves total radar mutual information by 26% compared to baseline strategies, demonstrating superior optimization performance.
• The hardware testbed achieves a stable data rate of 2.8 Gbps over the 28 GHz mmWave band, confirming high-throughput communication capability.
• Ranging accuracy of the integrated system is within ±0.25 m, with a measurement error of only ±0.044 m at 2 m distance.
• The system maintains a 10 ms radar update rate with 3-second averaging, ensuring real-time data freshness.
• The CTRA algorithm converges to the optimal time allocation strategy and achieves the best feasible pure strategy Nash equilibrium.
• The proposed JSCIS framework is feasible and effective, as validated by both simulation and real-world hardware testing.