title: Network planning and optimization in vehicle-to-everything communications
Creators: name: Akinsanya, Akinsola Sunday
Publisher: name: University of Kent
Year: 2024

description: The fifth generation (5G) New Radio (NR) opened the door to a wide range of novel use cases, in particular, vehicular communications. Besides the enhanced Mobile Broadband (eMBB), the 5G is the enabler towards network planning to achieve higher coverage and reliability communications in the fast growing internet of vehicle (IoV). Millimeter-wave (mmWave) communications will play a key role in enhancing the coverage and reliability of next generation vehicular communication systems. Considering time-variant vehicular channel in accommodating some IoV applications, the coverage and reliability requirements for vehicle-to-infrastructure demand extensive link budgeting optimisation.
The first part of the thesis focuses on obtaining statistical expression of Time-variant mmWave V2I Beamforming Channel (TVBC) between a fixed remote radio head (RRH) and a high mobility vehicular unit under the effect of Doppler shift associated with received multipath echoes to realize spatial diversity gain. To the best of the author's knowledge, this work presents the first attempt in developing a complete expression for the time-variant channel impulse response (CIR) by superpositioning all contributions such as (i) simplified path gain, (ii) antenna co-phasing, (iii) Doppler effects, (iv) link budgeting analysis and (v) effect of beam-width.
The second part of the study focuses on the resource allocation to achieve high system performance. In vehicular communications, maximizing the number of served vehicles is crucial for building up vehicle-aided emergency communications to ensure system reliability, coverage and safety service support at all the available vehicles. The parameters, such as the number of vehicles, number of beams, dynamic channel properties and vehicle position uncertainty, are solved in a switched-beam based multiple-input multiple-output (MIMO) vehicle-to-infrastructure (V2I) system (SBB-MIMO-V2I). SBB-MIMO-V2I system can provide better system reliability as the signal-to-interference-plus-noise ratio (SINR) can be significantly improved. In this part, for the purpose of performance comparison, the conventional resource allocation approach, suboptimal vehicle-beam allocation (VBA), which is an SINR optimization algorithm is presented. Then, an optimal resource allocation (Optimal-RA) scheme for the V2I communication is proposed to maximize the SINR. The Optimal-RA algorithm is designed as a joint multi-vehicle selection and SINR-aware link optimization with water-filling-based power allocation algorithm. This handles power reallocation for each beam and the energy efficiency of the system after a successful optimal beam allocation. Numerical simulations were carried out to compare the performance of suboptimal and Optimal-RA algorithms in terms of outage probability, average rate and energy efficiency. It is demonstrated that the Optimal-RA solution outperforms the suboptimal approach in terms of average data rate, service ratio, energy efficiency and reliability. It is demonstrated that the outage probability and rates are also directly dependent on received SINR thresholds. Based on the above learnings, the third part of the thesis proposes a novel RRH cooperation and inter-beam interference cancellation scheme to improve the channel capacity of the SBB-MIMO-V2I system. An inter-beam interference cancellation technique and RRH cooperation is exploited to improve the channel capacity of the SBB-MIMO-V2I system. The research takes into account the feedback transmission from multiple vehicular users through multiple beams under two scenarios: (i) inter-beam interference cancellation, and (ii) RRH cooperation. A Distributed Trellis-Powered Vehicular Architecture (D-TVA) is proposed to achieve an optimal quality of service. D-TVA is a low-complexity adaptive resource allocation with an interference mitigation solution between directional beams. The problem of power assignment is solved by applying the Lagrangian duality optimization method to maximize the channel capacity of a vehicular network. Finally, our simulation results show that it is beneficial to embrace the cooperation concept, where beams exchange information resulting to improved signal capacity and throughput.