Tree-search techniques for joint iterative compressive sensing and LDPC decoding in wireless sensor networks
Tree-search techniques for joint iterative compressive sensing and LDPC decoding in wireless sensor networks
Novel techniques are conceived for joint compressive sensing (CS) and low-density parity check (LDPC) coding in wireless sensor networks (WSNs), namely, a soft-input soft-output (SISO) tree search sphere decoding (SD) technique and an SISO Hamming distance (HD)-based solution. Factor graphs are utilized to describe the connectivity between the signals and sensors, as well as with the LDPC codes. In the fusion center (FC), the factor graphs may be used for iterative joint LDPC-CS decoding in order to recover the signals observed. However, the CS decoder of the FC suffers from high complexity if the exhaustive Maximum A Posteriori (e-MAP) technique is employed, which considers all possible combinations of source signals detected by each of the associated sensors. Hence, in the proposed SD and HD schemes, only the more likely combinations of source signals are tested for reducing the CS decoding complexity. More specifically, a tree search technique is used in the first step to find the most likely combination of source signal values. Then, in the second step, the proposed SD continues the tree search to find a set of alternative hypotheses. This facilitates the generation of high-quality extrinsic information, which may be iteratively exchanged with the LDPC decoder. By contrast, in the HD approach, the second step obtains the alternative hypotheses within a certain HD of the most likely source signal combination. Both our BLock Error Rate (BLER) results and EXtrinsic Information Transfer (EXIT) charts show that the proposed SD and HD techniques approach the performance of the full-search e-MAP approach at a significantly reduced complexity. In particular, we show that the e-MAP solution is about 56 times more complex than the SD approach and around 210 times more complex than the HD approach. Compared to a separate source-channel coding (SSCC) hard information benchmarker, the proposed SISO schemes improve the decoding performance by about 1.7 dB. Furthermore, the SISO schemes allow the iterations inside the CS decoding to eliminate the error floors and obtain a further 2.45 dB gain.
EXIT charts, Hamming distance technique, Joint source-channel coding, LDPC coding, compressive sensing, factor graph, sphere decoding, tree search
434-451
Chen, Jue
14b8e7c8-7f5e-4e68-a250-fd0989e1567b
Wang, Tsang Yi
7f1c0642-9107-4096-b255-799aff0b3176
Wu, Jwo Yuh
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Li, Chih Peng
aa5cdbec-f67a-41c7-8b87-037db1ae69e3
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Rob
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 January 2023
Chen, Jue
14b8e7c8-7f5e-4e68-a250-fd0989e1567b
Wang, Tsang Yi
7f1c0642-9107-4096-b255-799aff0b3176
Wu, Jwo Yuh
1c95bdaf-16e4-4c34-85b7-2df0eb2a1c0e
Li, Chih Peng
aa5cdbec-f67a-41c7-8b87-037db1ae69e3
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Rob
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Jue, Wang, Tsang Yi, Wu, Jwo Yuh, Li, Chih Peng, Ng, Soon Xin, Maunder, Rob and Hanzo, Lajos
(2023)
Tree-search techniques for joint iterative compressive sensing and LDPC decoding in wireless sensor networks.
IEEE Sensors Journal, 23 (1), .
(doi:10.1109/JSEN.2022.3221663).
Abstract
Novel techniques are conceived for joint compressive sensing (CS) and low-density parity check (LDPC) coding in wireless sensor networks (WSNs), namely, a soft-input soft-output (SISO) tree search sphere decoding (SD) technique and an SISO Hamming distance (HD)-based solution. Factor graphs are utilized to describe the connectivity between the signals and sensors, as well as with the LDPC codes. In the fusion center (FC), the factor graphs may be used for iterative joint LDPC-CS decoding in order to recover the signals observed. However, the CS decoder of the FC suffers from high complexity if the exhaustive Maximum A Posteriori (e-MAP) technique is employed, which considers all possible combinations of source signals detected by each of the associated sensors. Hence, in the proposed SD and HD schemes, only the more likely combinations of source signals are tested for reducing the CS decoding complexity. More specifically, a tree search technique is used in the first step to find the most likely combination of source signal values. Then, in the second step, the proposed SD continues the tree search to find a set of alternative hypotheses. This facilitates the generation of high-quality extrinsic information, which may be iteratively exchanged with the LDPC decoder. By contrast, in the HD approach, the second step obtains the alternative hypotheses within a certain HD of the most likely source signal combination. Both our BLock Error Rate (BLER) results and EXtrinsic Information Transfer (EXIT) charts show that the proposed SD and HD techniques approach the performance of the full-search e-MAP approach at a significantly reduced complexity. In particular, we show that the e-MAP solution is about 56 times more complex than the SD approach and around 210 times more complex than the HD approach. Compared to a separate source-channel coding (SSCC) hard information benchmarker, the proposed SISO schemes improve the decoding performance by about 1.7 dB. Furthermore, the SISO schemes allow the iterations inside the CS decoding to eliminate the error floors and obtain a further 2.45 dB gain.
Text
Tree Search Techniques for Joint iterative Compressive Sensing and LDPC Decoding in Wireless Sensor Networks
- Accepted Manuscript
More information
e-pub ahead of print date: 9 November 2022
Published date: 1 January 2023
Additional Information:
Funding information: The work of Tsang-Yi Wang was supported by the Ministry
of Science and Technology (MOST) of Taiwan under Grant 110-2221-E110-021. The work of Jwo-Yuh Wu was supported in part by the MOST of Taiwan under Grant MOST 108-2221-E-009-025 MY3 and Grant MOST 110-2634-F-009-025, in part by the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and the Ministry of Education of Taiwan, and in part by the MOST Joint Research Center for AI Technology and All Vista Healthcare. The work of Chih-Peng Li was supported in part by the MOST of Taiwan under Grant MOST 108-2218-E-110-014 and Grant MOST 109-2218-E-110-006. The work of
Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council under Project EP/P034284/1 and Project EP/P003990/1 (COALESCE), and in part by the European Research Council’s Advanced Fellow Grant QuantCom under Grant 789028.
Keywords:
EXIT charts, Hamming distance technique, Joint source-channel coding, LDPC coding, compressive sensing, factor graph, sphere decoding, tree search
Identifiers
Local EPrints ID: 473884
URI: http://eprints.soton.ac.uk/id/eprint/473884
ISSN: 1530-437X
PURE UUID: 264e63cd-fdfe-451e-a174-05b0bdf60fba
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Date deposited: 02 Feb 2023 17:38
Last modified: 02 Aug 2023 01:51
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Contributors
Author:
Jue Chen
Author:
Tsang Yi Wang
Author:
Jwo Yuh Wu
Author:
Chih Peng Li
Author:
Soon Xin Ng
Author:
Rob Maunder
Author:
Lajos Hanzo
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