Interference-Aware Distributed Predictive Resource Allocation for Automotive Applications

Abstract

Autonomous driving will rely on several safety-related connected applications that coexist with infotainment services for passenger entertainment. The simultaneous provisioning of resources to services with different quality of service requirements poses an immense challenge for future cellular networks. While most safety-related applications require low latencies, infotainment services usually necessitate a high average throughput. We propose a multi-cell, distributed predictive resource allocation framework with interference coordination based on channel distribution information to address the coexistence challenges. The approach first shifts packet transmission times in the so-called statistical look-ahead scheduling (SLAS) step, leveraging service properties. Inter-cell interference is coordinated by base station communication and a low-complexity fractional interference approximation. Lastly, packets are forwarded to an online scheduler according to the found transmission schedule. Moreover, we present a convolutional neural network to reduce the computational complexity in the SLAS step. Simulations show that the distributed approach performs very close to a central-controller solution at significantly lower computational complexity. It outperforms state-of-the-art schedulers in terms of transmission reliability and spectral efficiency.