A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols
Massive grant-free non-orthogonal multiple access (NOMA) enables low-latency data transmission for a subset of coexisting user terminals, which may not be known a priori, and operates in an uncoordinated manner. On the receiver side, the effectiveness of user data decoding techniques heavily relies...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11060008/ |
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| author | Fakher Sagheer Frederic Lehmann Antoine O. Berthet |
| author_facet | Fakher Sagheer Frederic Lehmann Antoine O. Berthet |
| author_sort | Fakher Sagheer |
| collection | DOAJ |
| description | Massive grant-free non-orthogonal multiple access (NOMA) enables low-latency data transmission for a subset of coexisting user terminals, which may not be known a priori, and operates in an uncoordinated manner. On the receiver side, the effectiveness of user data decoding techniques heavily relies on accurate initial estimates of user activity, channel conditions, and parameters, all of which are derived from pilot symbols. However, in practical systems where pilot symbols are shared among a large number of users, the process is complicated by pilot interference. This paper introduces a pilot-only clustered multiple sparse Bayesian learning approach to address this acquisition challenge. It employs a multiple measurement vector (MMV) model to handle the common support shared by the multi-antenna channel impulse responses of each user. The resulting estimates are then used to initialize a low-complexity Bayesian procedure, based on expectation propagation, to refine both user activity and channel estimations, as well as to facilitate multi-user detection and decoding by leveraging both pilot and data observations. Numerical simulations demonstrate the good performance of the proposed two-stage Bayesian receiver for massive grant-free NOMA under frequency-selective channels with antenna correlation. |
| format | Article |
| id | doaj-art-e511a14290d340ffb27d290a5e898b64 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-e511a14290d340ffb27d290a5e898b642025-08-20T03:15:35ZengIEEEIEEE Access2169-35362025-01-011311290511291710.1109/ACCESS.2025.357859311060008A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot SymbolsFakher Sagheer0https://orcid.org/0009-0007-9772-5030Frederic Lehmann1https://orcid.org/0000-0002-6409-1904Antoine O. Berthet2https://orcid.org/0000-0002-0524-6814Communications, Images and Information Processing Department, SAMOVAR, Telecom SudParis, IP Paris, Palaiseau, FranceCommunications, Images and Information Processing Department, SAMOVAR, Telecom SudParis, IP Paris, Palaiseau, FranceLaboratory of Signals and Systems, CNRS, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, FranceMassive grant-free non-orthogonal multiple access (NOMA) enables low-latency data transmission for a subset of coexisting user terminals, which may not be known a priori, and operates in an uncoordinated manner. On the receiver side, the effectiveness of user data decoding techniques heavily relies on accurate initial estimates of user activity, channel conditions, and parameters, all of which are derived from pilot symbols. However, in practical systems where pilot symbols are shared among a large number of users, the process is complicated by pilot interference. This paper introduces a pilot-only clustered multiple sparse Bayesian learning approach to address this acquisition challenge. It employs a multiple measurement vector (MMV) model to handle the common support shared by the multi-antenna channel impulse responses of each user. The resulting estimates are then used to initialize a low-complexity Bayesian procedure, based on expectation propagation, to refine both user activity and channel estimations, as well as to facilitate multi-user detection and decoding by leveraging both pilot and data observations. Numerical simulations demonstrate the good performance of the proposed two-stage Bayesian receiver for massive grant-free NOMA under frequency-selective channels with antenna correlation.https://ieeexplore.ieee.org/document/11060008/Massive grant-free NOMAOFDMstructured sparsitysparse Bayesian learningexpectation progagationnon-orthogonal pilot symbols |
| spellingShingle | Fakher Sagheer Frederic Lehmann Antoine O. Berthet A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols IEEE Access Massive grant-free NOMA OFDM structured sparsity sparse Bayesian learning expectation progagation non-orthogonal pilot symbols |
| title | A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols |
| title_full | A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols |
| title_fullStr | A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols |
| title_full_unstemmed | A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols |
| title_short | A Two-Stage Bayesian Receiver for Massive Grant-Free OFDM-NOMA With Interfering Pilot Symbols |
| title_sort | two stage bayesian receiver for massive grant free ofdm noma with interfering pilot symbols |
| topic | Massive grant-free NOMA OFDM structured sparsity sparse Bayesian learning expectation progagation non-orthogonal pilot symbols |
| url | https://ieeexplore.ieee.org/document/11060008/ |
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