Coded Computation Against Processing Delays for Virtualized Cloud-Based Channel Decoding

TL;DR

This paper proposes a novel coding strategy for cloud-based channel decoding to mitigate processing delays, analyzing its impact on latency and reliability in virtualized radio networks.

Contribution

It introduces a new coding approach for cloud decoding that improves latency-reliability trade-offs and provides analytical bounds for system performance.

Findings

Coding reduces frame unavailability probability.

Trade-off between latency and error rate is characterized.

Bounds assist in code design for cloud decoding.

Abstract

The uplink of a cloud radio access network architecture is studied in which decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the trade-off between average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability.