Achieving Ultra High Freshness in Real-Time Monitoring and Decision Making with Incremental Decoding

Real-time monitoring and remote control of stochastic systems have attracted considerable attention due to their potential in task-oriented communications and industrial Internet of Things (IIoT). How to achieve ultra high-freshness in real-time monitoring and remote control becomes a challenging problem. In this paper, we are interested in the freshness oriented source coding with incremental decoding. This is contrast to con-ventional source encoding/decoding, in which a random sample is estimated after its entire codeword is received. Incremental decoding, however, allows the real-time estimation of a random sample once a new bit or channel coding block is decoded in the physical layer. Its source codebook is then optimized, based on which we further conceive a real-time decision policy. Our policies minimize the average mean square error (MSE) or decision cost by judiciously designed codebook for source encoding. Numerical results show that the incremental decoding substantially reduces the MSE and decision cost in real-time monitoring.