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.
Highlights
With the enormous development of Internet of Things (IoT), machine-type communication (MTC) devices are expected to grow exponentially and demand a significant aggregated transmission rate in the decade
We found that the cluster number can balance the compression ratio and decoding delay of Distributed source coding (DSC), and an optimal cluster number exists that maximizes the evaluation indicator
Leveraging DSC to reduce the redundancy of correlated sources can relieve the fast growing demand of communication resources from massive MTC devices
Summary
With the enormous development of Internet of Things (IoT), machine-type communication (MTC) devices are expected to grow exponentially and demand a significant aggregated transmission rate in the decade. The decoder manages to perform close to the Slepian–Wolf bound, but iterative decoding apparently brings higher computation complexity and delay.[16] Aiming at the situation that neither encoder nor decoder knows the correlation structure, a rate-adaptive scheme with feedback is proposed,[17] and the encoder first sends a short syndrome and the decoder attempts decoding; if the decoding fails, the encoder will be informed to extend the short syndrome with additional bits until the decoding achieves success This scheme is further improved in decoding complexity and delay by reducing feedback loops.[18] the request-and-decode process will make the system more fragile to channel failures
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