Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field.

[1]  W. Yuan,et al.  Multi-Vehicle Tracking and ID Association Based on Integrated Sensing and Communication Signaling , 2022, IEEE Wireless Communications Letters.

[2]  W. Yuan,et al.  Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking , 2022, IEEE Transactions on Wireless Communications.

[3]  Fan Liu,et al.  Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation , 2022, IEEE Transactions on Wireless Communications.

[4]  Giuseppe Caire,et al.  Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets , 2021, IEEE Transactions on Wireless Communications.

[5]  Derrick Wing Kwan Ng,et al.  Integrated Sensing and Communication-Assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks , 2021, IEEE Journal of Selected Topics in Signal Processing.

[6]  Xiaojun Jing,et al.  Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends, and Challenges , 2021, IEEE Network.

[7]  Christos Masouros,et al.  A Tutorial on Joint Radar and Communication Transmission for Vehicular Networks—Part III: Predictive Beamforming Without State Models , 2021, IEEE Communications Letters.

[8]  Derrick Wing Kwan Ng,et al.  Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach , 2020, IEEE Transactions on Wireless Communications.

[9]  Christos Masouros,et al.  Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing , 2020, IEEE Transactions on Wireless Communications.

[10]  Taneli Riihonen,et al.  Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements , 2019, IEEE Transactions on Microwave Theory and Techniques.

[11]  Jaechan Lim,et al.  Beam Tracking Under Highly Nonlinear Mobile Millimeter-Wave Channel , 2019, IEEE Communications Letters.

[12]  Robert W. Heath,et al.  Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing , 2016, IEEE Communications Magazine.

[13]  Jason L. Williams,et al.  Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[14]  W. Yuan,et al.  ISAC-Enabled V2I Networks Based on 5G NR: How Many Overheads Can Be Reduced? , 2023 .

[15]  N. Kato,et al.  The Roadmap of Communication and Networking in 6G for the Metaverse , 2023, IEEE Wireless Communications.

[16]  Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China , 2022 .