Achieving Extremely Low Latency: Incremental Coding for Real-Time Applications

Abstract

Extremely low-latency communication has attracted considerable recent attention because it holds the promise of supporting emerging real-time applications such as autonomous driving, smart grids, and Industrial Internet of Things (IIoT). Owing to the limited bandwidth in wireless environments, the sub-packets or even bits have to be transmitted successively, thereby inducing non-negligible delay-induced cost for real-time remote monitoring, estimation, decision making, and control. In this paper, we present a unified incremental decoding framework for real-time applications, the costs of which are extremely sensitive to the latency of each individual sub-packet or bit. In contrast to conventional methods, in which a decision is made after fully decoding the entire packet, the incremental decoding strategy allows monitors or actors to make their decisions in real time based on partially received packet. By this means, there is no need to wait for the whole packet to be decoded, thereby reducing the delay-induced costs substantially. To minimize cumulative cost during the real-time monitoring and control, we design source coding and decision making algorithms jointly, in which a backward induction property is found. Furthermore, we conceive a dynamic programming algorithm for a given source codebook to significantly reduce the cumulative decision costs while maintaining low computational complexity.