Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems
Universal filtered multicarrier (UFMC) has emerged as a potential waveform contender of orthogonal frequency division multiplexing (OFDM) for the fifth generation (5G) and beyond wireless systems. In this paper, we propose a bidirectional long short-term memory (Bi-LSTM)-based detector for the UFMC...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022001001 |
| _version_ | 1811201202944212992 |
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| author | Md. Ferdous Ahammed A. Alim Molla Rafiul Kadir Mohammad Ismat Kadir |
| author_facet | Md. Ferdous Ahammed A. Alim Molla Rafiul Kadir Mohammad Ismat Kadir |
| author_sort | Md. Ferdous Ahammed |
| collection | DOAJ |
| description | Universal filtered multicarrier (UFMC) has emerged as a potential waveform contender of orthogonal frequency division multiplexing (OFDM) for the fifth generation (5G) and beyond wireless systems. In this paper, we propose a bidirectional long short-term memory (Bi-LSTM)-based detector for the UFMC system. The proposed detector directly detects the transmitted symbols using the deep learning (DL)-based training data. The system is first trained with the aid of training data and pilot symbols. The training tunes the DL-based network parameters. During the testing phase, the signal is detected using the trained network. The performance of the proposed scheme is compared with that of the DL-aided OFDM system, and with the signal detection strategies using the conventional channel estimation techniques. Our simulations show that the proposed Bi-LSTM-based DL can flexibly and effectively detect UFMC signals. |
| first_indexed | 2024-04-12T02:18:18Z |
| format | Article |
| id | doaj.art-aefc97ab1f3044f5b2cedf93c800b5db |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-12T02:18:18Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-aefc97ab1f3044f5b2cedf93c800b5db2022-12-22T03:52:12ZengElsevierMachine Learning with Applications2666-82702022-12-0110100425Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systemsMd. Ferdous Ahammed0A. Alim Molla1Rafiul Kadir2Mohammad Ismat Kadir3Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, BangladeshElectronics and Communication Engineering Discipline, Khulna University, Khulna 9208, BangladeshElectronics and Communication Engineering Discipline, Khulna University, Khulna 9208, BangladeshCorresponding author.; Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, BangladeshUniversal filtered multicarrier (UFMC) has emerged as a potential waveform contender of orthogonal frequency division multiplexing (OFDM) for the fifth generation (5G) and beyond wireless systems. In this paper, we propose a bidirectional long short-term memory (Bi-LSTM)-based detector for the UFMC system. The proposed detector directly detects the transmitted symbols using the deep learning (DL)-based training data. The system is first trained with the aid of training data and pilot symbols. The training tunes the DL-based network parameters. During the testing phase, the signal is detected using the trained network. The performance of the proposed scheme is compared with that of the DL-aided OFDM system, and with the signal detection strategies using the conventional channel estimation techniques. Our simulations show that the proposed Bi-LSTM-based DL can flexibly and effectively detect UFMC signals.http://www.sciencedirect.com/science/article/pii/S2666827022001001Universal filtered multicarrierDeep learningLong short-term memory (LSTM)Bidirectional LSTMRayleigh fading channel |
| spellingShingle | Md. Ferdous Ahammed A. Alim Molla Rafiul Kadir Mohammad Ismat Kadir Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems Machine Learning with Applications Universal filtered multicarrier Deep learning Long short-term memory (LSTM) Bidirectional LSTM Rayleigh fading channel |
| title | Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems |
| title_full | Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems |
| title_fullStr | Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems |
| title_full_unstemmed | Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems |
| title_short | Deep bidirectional LSTM for the signal detection of universal filtered multicarrier systems |
| title_sort | deep bidirectional lstm for the signal detection of universal filtered multicarrier systems |
| topic | Universal filtered multicarrier Deep learning Long short-term memory (LSTM) Bidirectional LSTM Rayleigh fading channel |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022001001 |
| work_keys_str_mv | AT mdferdousahammed deepbidirectionallstmforthesignaldetectionofuniversalfilteredmulticarriersystems AT aalimmolla deepbidirectionallstmforthesignaldetectionofuniversalfilteredmulticarriersystems AT rafiulkadir deepbidirectionallstmforthesignaldetectionofuniversalfilteredmulticarriersystems AT mohammadismatkadir deepbidirectionallstmforthesignaldetectionofuniversalfilteredmulticarriersystems |