Literature Database Entry

sahin2023resource


Taylan Şahin, "Resource Allocation for Vehicle-to-Vehicle Communications under Intermittent Cellular Coverage," PhD Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), May 2023. (Advisor: Adam Wolisz; Referees: Adam Wolisz, Falko Dressler, Klaus Moessner and Mate Boban)


Abstract

Vehicle-to-vehicle (V2V) communication is a key technology to enable safer, more efficient, and more comfortable road traffic. The stringent reliability and latency requirements of V2V messages necessitate efficient radio resource management given the scarce spectrum and the dynamic vehicular environment. Under cellular network coverage, the resource allocation can be centrally coordinated by a base station (BS), which can efficiently ensure collision-free transmissions. When out of coverage, vehicles resort to distributed mechanisms, which yet suffer from degraded communication quality due to the vehicles’ limited local view. In this thesis, we propose a novel approach for V2V communications in expected, delimited out-of-coverage areas (DOCAs), whereby a centralized scheduler pre-assigns resources to the vehicles via the BSs surrounding the area, before vehicles enter it. We first explore the feasibility of this approach by exploiting the road and data traffic information available in coverage to reserve and provision the resources. While the required number of resources does not grow prohibitively with increased reliability targets, the rate of successful V2V transmissions gets highly impacted by various factors such as vehicle mobility, thus necessitating efficient means to cope with uncertainties in DOCAs. As a predictive method for resource allocation, we propose a vehicular reinforcement learning scheduler, VRLS, which is applicable to DOCAs that vary in vehicle density, mobility, wireless channel characteristics, and resource configurations. VRLS can significantly increase resource utilization efficiency by requiring fewer resources than state-of-the-art distributed scheduling solutions to support the same reliability targets. Nevertheless, considering that the performance of learning-based solutions may degrade upon parameter distributions much beyond their training environment, we propose a hybrid scheme that combines the centralized RL-based and the distributed sensing-based scheduling approaches. We show the performance benefits of such a solution under heavily congested road traffic due to an accident, as compared to either of the centralized or the distributed solutions. Finally, we shift our focus to those areas under network coverage where vehicles suffer from rather short and unpredictable coverage interruptions to the BSs. We consider an extension of our RL-based approach for this problem. The proposed solution performs better than the state-of-the-art baseline in the cases of coverage losses, especially under high traffic load and lower frequency of scheduling updates, otherwise delivering similar performance.

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Taylan Şahin

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@phdthesis{sahin2023resource,
    author = {{\c{S}}ahin, Taylan},
    title = {{Resource Allocation for Vehicle-to-Vehicle Communications under Intermittent Cellular Coverage}},
    advisor = {Wolisz, Adam},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {5},
    referee = {Wolisz, Adam and Dressler, Falko and Moessner, Klaus and Boban, Mate},
    school = {TU Berlin (TUB)},
    type = {PhD Thesis},
    year = {2023},
   }
   
   

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Last modified: 2024-06-21