LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks
Jue Chen, Shuai Shao, Tsang-Yi Wang, Jwo-Yuh Wu, Chih–Peng Li, Soon Xin Ng, Robert G. Maunder, Lajos Hanzo · IEEE Transactions on Vehicular Technology · 2022
The novel concept of joint Compressive Sensing (CS) and Low Density Parity Check (LDPC) coding is conceived for Joint Source-Channel Coding (JSCC) in Wireless Sensor Networks (WSNs) supporting a massive number of signals. More explicitly, we demonstrate this concept for a specific scheme, which supports a massive number of signals simultaneously, using a small number of Internet of Things Nodes (IoTNs) based on the concept of CS. The compressed signals are LDPC coded in order to protect them from poor transmission channels. We also propose the new iterative joint source-channel decoding philosophy for exchanging softextrinsicinformation, which combines CS decoding and LDPC decoding by merging their respective factor graphs. We then characterize this scheme using Extrinsic Information Transfer (EXIT) chart analysis. Our BLock Error Rate (BLER) results show that the proposed iterative joint LDPC-CS decoding scheme attains about 1.5 dB gain at a BLER of$10^{-3}$compared to a benchmarker, which employs separate CS and LDPC decoding. Naturally, this gain is achieved at the cost of approximately doubling the complexity of the proposed iterative joint LDPC-CS decoding scheme.