Sum-Rate Maximization of Multirate NOMA-Based WSNs
This work considers optimal node pairing and channel allocation in downlink (DL) wireless sensor networks (WSNs) with multirate (MR)-nonorthogonal multiple access (NOMA). The objective is to maximize the network sum-rate and improve the IoT devices (IoDs) connectivity while satisfying the quality-of...
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| Published in: | IEEE sensors journal Vol. 25; no. 21; pp. 40806 - 40819 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1530-437X, 1558-1748 |
| Online Access: | Get full text |
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| Summary: | This work considers optimal node pairing and channel allocation in downlink (DL) wireless sensor networks (WSNs) with multirate (MR)-nonorthogonal multiple access (NOMA). The objective is to maximize the network sum-rate and improve the IoT devices (IoDs) connectivity while satisfying the quality-of-service (QoS), bit error rate (BER) requirements. The IoD channel allocation and pairing processes are formulated as a mixed integer linear programming problem where the BER expressions are derived in closed form for the two-IoD scenario over a Nakagami-m fading channel. To solve the optimization problem, an efficient band elimination algorithm (BEA) is proposed to reduce the complexity of the branch and bound (BB) algorithm. The obtained results show that pairing IoDs with different transmission rates can improve the network sum-rate and connectivity by 26% and 39%, respectively, compared to single-symbol rate (SR)-NOMA. Moreover, in another scenario, MR-NOMA demonstrated its efficacy by achieving connectivity for all IoDs, distinctly outperforming conventional SR-NOMA, which managed to connect only 66% of the IoDs, even at high signal-to-noise ratios (SNRs). The proposed BEA technique is shown to significantly reduce the BB complexity, particularly at low SNRs where complexity reduction exceeds 90%. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1530-437X 1558-1748 |
| DOI: | 10.1109/JSEN.2025.3609565 |