Towards low-latency and ultra-reliable vehicle-to-vehicle communication

By Muhammad Ikram Ashraf, Chen-Feng Liu, Medhi Bennis, Walid Saad. 2017

72 citations recorded by the source corpus.

Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling technology to ensure traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation for V2V communication is proposed. The proposed approach exploits the spatial-temporal aspects of vehicles in terms of their physical proximity and traffic demands, to minimize the total transmission power while considering queuing latency and reliability. Due to the overhead caused by frequent information exchange between vehicles and the roadside unit (RSU), the centralized problem is decoupled into two interrelated subproblems. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles in zones based on their physical proximity. Given the vehicles' traffic demands and their QoS requirements, resource blocks are assigned to each zone. Second, leveraging techniques from Lyapunov stochastic optimization, a power minimization solution is proposed for each V2V pair within each zone. Simulation results for a Manhattan model have shown that the proposed scheme outperforms the baseline in terms of average queuing latency reduction up to 97% and significant improvement in reliability.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

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Citations in the corpus

Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “Towards this end, the majority of existing works including [8], [9] and [10] focus on improving the average latency of vehicular networks while a handful such as [1] and [2] impose a probabilistic constraint to maintain small queue lengths.”
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, background): “mong vehicles to reduce the V2V communication delays. In [13], the tradeoff between service delay and transmission success in V2V communications is optimized. The URLLC aspects of this prior art [4], [5], [8]–[14] are captured by either improving the average latencies or imposing a probabilistic con”
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, background): “In terms of 5G verticals, challenges of ultrareliable vehicular communication were looked at in [12] and [43], whereas mobile edge computing with URLLC guarantees was studied in [44].”
Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach (2018, methodology): “SIMULATION PARAMETERS [1], [2], [7], [8]”
High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies (2019, background): “in [85], where the RSU grouped vehicles into pairs of virtual zones over the set of allocated RBs.”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, methodology): “For our simulations, we use a 250 × 250 m 2 area Manhattan mobility model as in [8], [13].”