Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach

By Chen-Feng Liu, Medhi Bennis. 2018

71 citations recorded by the source corpus.

Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize the statistics of extreme events in terms of the maximal queue length. By leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.

Subfields: Electrical and Electronic Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Medhi Bennis
2. Merouane Debbah
3. Walid Saad
4. Sumudu Samarakoon
5. H. Vincent Poor
6. Chen-Feng Liu
7. Choong Seon Hong
8. Mohamed K. Abdel-Aziz

In Statistics and Probability

1. Walid Saad
2. Sumudu Samarakoon
3. Merouane Debbah
4. Medhi Bennis
5. Chen-Feng Liu
6. H. Vincent Poor

Citations in the corpus

• Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “EVT asymptotically characterizes the statistics of extreme events by providing analytical models to analyze network traffic, worst-case delays, peak-to-average-power ratios, and ultra-reliable V2V communication in wireless systems [2], [13]–[15].”
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, methodology): “The RB allocation per group is adopted from [6] and [4].”
• Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, background): “Other applications of EVT in vehicular communication can be found in [53] and [67].”
• High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies (2019, unknown): “HRLL designs for various IoT applications [84]–[86].”
• Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, background): “, the maximal queue length among all UEs as in [44], and enables us to proactively deal with the occurrence of extreme events.”
• Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020, background): “The work in [8] studied the power minimization subject to latency and reliability constraints considering a Manhattan mobility model in Vehicle-to-Vehicle (V2V) networks.”
• Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, methodology): “The results also show over two-fold performance gains in AoI and queue length compared to a baseline whose design is based on the maximum queue length [13].”