Throughput Maximization for Delay-Sensitive Random Access Communication

TL;DR

This paper develops an analytical framework for optimizing resource partitioning in delay-sensitive random access communication systems, aiming to maximize throughput in 5G URLLC scenarios.

Contribution

It introduces explicit expressions for optimal resource partitioning and throughput under high and low SNR, using Shannon and finite block length analyses.

Findings

Optimal partition granularity maximizes throughput.

Throughput scaling is valid across various SNRs.

Framework aids resource allocation in 5G URLLC uplink access.

Abstract

Future 5G cellular networks supporting ultra-reliable, low-latency communications (URLLC) could employ random access communication to reduce the overhead compared to scheduled access techniques used in 4G networks. We consider a wireless communication system where multiple devices transmit payloads of a given fixed size in a random access fashion over shared radio resources to a common receiver. We allow retransmissions and assume Chase combining at the receiver. The radio resources are partitioned in the time and frequency dimensions, and we determine the optimal partition granularity to maximize throughput, subject to given constraints on latency and outage. In the regime of high and low signal-to-noise ratio (SNR), we derive explicit expressions for the granularity and throughput, first using a Shannon capacity approximation and then using finite block length analysis. Numerical results show that the throughput scaling results are applicable over a range of SNRs. The proposed analytical framework can provide insights for resource allocation strategies in general random access systems and in specific 5G use cases for massive URLLC uplink access.