Tradeoff between compression ratio and decoding delay of distributed source coding for uplink transmissions in machine-type communication

Abstract

Rapid growth of machine-type communications devices challenges the future network with a significant aggregated data traffic. Distributed source coding is a promising technique that compresses data sources and decreases required aggregated data transmission rate. In this article, we discuss the merits and demerits of deploying distributed source coding in machine-type communications uplink transmissions. We analyze how the decoding delay and storage consumption scale with the number of users and prove that the average decoding delay grows linearly with the user number under some assumptions. A machine-type communications uplink transmission scheme adopting clustered distributed source coding is proposed to balance the compression ratio and decoding delay of distributed source coding where users are divided into independently encoded and decoded clusters. We evaluate three clustering algorithms, grid dividing, Weighted Pair Group Method with Arithmetic Mean, and K-medoids in our system model. The grid dividing algorithm clusters users based on their locations, while Weighted Pair Group Method with Arithmetic Mean and K-medoids cluster users using the correlation intensity between them. Our simulation results show that Weighted Pair Group Method with Arithmetic Mean and K-medoids outperform grid dividing on compression ratio and K-medoids and grid dividing have a more balanced delay distribution among different clusters than Weighted Pair Group Method with Arithmetic Mean.