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LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks
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DOI：10.1109/TVT.2022.3212025.png)
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Abstract
En 中文
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 soft extrinsic information, 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.
Keywords:
Iterative decoding
Decoding
Wireless sensor networks
Iterative methods
Channel coding
Sensors
Complexity theory
Joint source-channel coding
compressive sensing
LDPC codes
factor graphs
wireless sensor networks
extrinsic information transfer charts
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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