Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

By Fan Liu, Weijie Yuan, Christos Masouros, Jinhong Yuan. IEEE Transactions on Wireless Communications, 2020

569 citations recorded by the source corpus.

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/26019a33cf6e46602f54e78e97e1559f4837cab4

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Citations in the corpus

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges (2022, background): “future wireless networks, which in turn can potentially enhance their communication performance [14].”
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges (2023, methodology): “A typical example can be found in vehicle-to-infrastructure (V2I) communication systems [14], where the roadside unit (RSU) can exploit the echo signals to predict and track the vehicular target, so as to assist the downlink communication and to reduce the pilot overhead.”
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, methodology): “More importantly, the wireless sensing functionality equipped on the RSU provides an alternative way to acquire vehicles’ states in a fast and inexpensive manner, which in turn facilitates the Vehicle-to-Infrastructure (V2I) communications and platooning by significantly reducing the beam training o”
Waveform Design and Performance Analysis for Full-Duplex Integrated Sensing and Communication (2021, background): “For example, the sensed information like the angle, range, and location of the user equipment (UE) or even scatterers can be used for communication performance enhancement, such as sensing-assisted beamforming [14], [15] and environment-aware resource allocation and beam alignment [16], [17].”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, background): “UE [35], without the need of transmitting separate radar signals.”
Integrated Sensing and Communication-Assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks (2021, methodology): “…time instant can be expressed as Moreover, we are able to predict angle θ i of the i -th vehicle relative to the RSU, given by In this paper, we assume that the speeds of vehicles do not change in the relatively short time duration, i.e., ¯ s i = ˆ s i , as commonly adopted in the literature [29].”
Cramér-Rao Bound Optimization for Joint Radar-Communication Beamforming (2021, methodology): “Together with the use of mmWave and hybrid analog-digital beamforming techniques, the radar sensing functionality can be incorporated into the massive MIMO base station (BS) [16], [23], [24], which may be deployed in the 5G/B5G vehicular network as a road side unit (RSU) for both vehicular communica”
On the Fundamental Tradeoff of Integrated Sensing and Communications Under Gaussian Channels (2022, unknown): “Nonetheless, the distinct physical roles of X and H in S&C and the resulting performance measures lead to a number of unique performance tradeoffs and design criteria [7], [8], [9], [10] to the ISAC signal processing, particularly for simultaneously realizing S&C functionalities via a unified wavefo”
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing (2021, unknown): “With both models (20) and (21) above, the RSU can predict and estimate the vehicle’s state at each epoch via various approaches, e.g., Kalman ﬁltering and factor graph based message passing algorithms [48]–[50].”
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “Joint signal design and optimization, such as beamforming optimization [41] and sensing assisted communications [89], [90] (To be detailed in Section VI-B and VI-I ).”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “In particular, radar sensing provides a promising way to predict the vehicular links [214] and holds the potential to provide real-time model aggregation via predictive beamforming in the model aggregation procedure.”
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, unknown): “The semi-definite relaxation (SDR) and successive convex approximation (SCA) methods are applied to provide high-quality solutions with acceptable computation costs [75], [76], [77].”
Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach (2020, methodology): “Compared to the EKF method in [24], the proposed approach is expected to achieve a better estimation performance.”