Resource Optimization for Communication and Radar Sensing in Vehicular Networks

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With a great increasing volume of vehicles and population, transportation systems are facing many challenges such as congestion, air pollution, crashes and noise. Vehicular communication and radar sensing technology is promising for realizing intelligent, faster, safer transportation. The rapidly increasing amount and mobility of vehicles require frequent resource allocation, which can cause network congestion, large signalling and processing delay. In addition, due to the limited available bandwidth, wide deployment of radar sensors on automotive vehicles can potentially lead to a severe interference problem. Therefore, resource optimization for vehicular communication and automotive radars becomes a key issue in future autonomous vehicular networks, to meet their performance requirements and improve the spectral efficiency. In this thesis, we investigate the resource optimization algorithm for communication and radar sensing in vehicular networks, addressing the following three issues: 1. The resource allocation and optimization scheme for vehicular communications based on traffic prediction, considering both critical latency requirement and spectral efficiency; 2. The mode selection scheme for vehicle-to-everything (V2X) communications to optimize energy consumption, considering resource reusing between vehicular users and conventional users; 3. The power optimization and interference characterization for automotive radars, including the modeling of vehicle distribution, the consideration of different types of radars and the assumption of radar antenna directivity. Regarding the first issue, we propose a novel semi-persistent resource allocation scheme based on a two-tier heterogeneous network architecture including a central macro base station (MBS) and multiple roadside units (RSU). Considering the predictability of vehicular flows, we combine the traffic prediction with this resource allocation scheme. In the proposed semi-persistent scheme, the MBS pre-allocates persistent resources to RSUs based on predicted traffic, and then allocates dynamic resources upon real-time requests from RSUs while vehicles simultaneously communicate using the pre-allocated resources. Based on this scheme, we mainly study two classes of optimization problems: 1) minimizing the relative latency with the constraint of total bandwidth; 2) minimizing the total bandwidth with the constraint of transmission latency. Different algorithms are developed to address the problems. Towards the second issue, we investigate a two-tier heterogeneous cellular network where the macro tier and small cell tier operate according to a dynamic time-division duplex (TDD). Based on dynamic TDD which can adjust UL and DL time configurations to accommodate to the traffic asymmetry, we propose a vehicular device-to-device (V-D2D) mode selection scheme jointing time allocation, power control to minimize the energy consumption of the vehicles and the whole network. The problem is formulated as a convex optimization problem, and a geometrical interpretation is provided. For the third issue, we firstly study the mean power of effective echo signals and interference, by considering both front- and side- mounted automotive radars equipped with directional antennas. We employ the stochastic geometry method to characterize the randomness of vehicular location and hence radars in both two-lane and multi-lane scenarios, and derive closed-form expressions for the mean interference by approximating the radiation pattern by Gaussian waveforms. Based on the interference analysis, we aim to minimize the total transmission power of each vehicle with constraints on the required signal to interference and noise ratio. An optimal solution is obtained based on linear programming techniques.
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