Supporting ultra-reliable and low-latency communications (URLLC) is crucial for vehicular traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service-aware resource allocation framework for vehicle-to-vehicle (V2V) communication is proposed. The proposed scheme incorporates the physical proximity and traffic demands of vehicles to minimize the total transmission power over the allocated resource blocks (RBs) under reliability and queuing latency constraints. A Lyapunov framework is used to decompose the power minimization problem into two interrelated sub-problems: RB allocation and power optimization. To minimize the overhead introduced by frequent information exchange between the vehicles and the roadside unit (RSU), the resource allocation problem is solved in a semi-distributed fashion. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles into disjoint zones based on mutual interference. Second, a per-zone matching game is proposed to allocate RBs to each vehicle user equipment (VUE) based on vehicles’ traffic demands and their latency and reliability requirements. In the formulated one-to-many matching game, VUE pairs and RBs rank one another using preference relations that capture both the queue dynamics and interference. To solve this game, a semi-decentralized algorithm is proposed using which the VUEs and RBs can reach a stable matching. Finally, a latency-and reliability-aware power allocation solution is proposed for each VUE pair over the assigned subset of RBs. Simulation results for a Manhattan model show that the proposed scheme outperforms the state-of-art baseline and reaches up to 45% reduction in the queuing latency and 94% improvement in reliability.
Ashraf Muhammad Ikram, Liu Chen-Feng, Bennis Mehdi, Saad Walid, Hong Choong Seon
A1 Journal article – refereed
Place of publication:
M. I. Ashraf, C. Liu, M. Bennis, W. Saad and C. S. Hong, “Dynamic Resource Allocation for Optimized Latency and Reliability in Vehicular Networks,” in IEEE Access, vol. 6, pp. 63843-63858, 2018. doi: 10.1109/ACCESS.2018.2876548
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