Joint user activity detection and channel estimation in MC-GFMA systems by block sparse Bayesian learning with threshold optimization
Joint user activity detection and channel estimation in MC-GFMA systems by block sparse Bayesian learning with threshold optimization
Future wireless communications are expected to support massive connectivity in various applications, such as massive Machine-Type Communications (mMTC) and different types of IoT networks, where many applications have the data traffic of sporadicnature. To support these kinds of applications, grant free multiple-access (GFMA) has been recognized to be more efficient thanthe conventional granted multiple access (GMA). However, due to sporadic transmission, GFMA faces the main challenges of User Activity Detection (UAD) and Channel Estimation (CE). To meet these challenges, in this paper, a multicarrier GFMA (MC-GFMA) system is introduced for supporting massive connectivity. A block-sparse signal model is derived, where the Expectation Maximization assisted Block Sparse Bayesian Learning (EM-BSBL) algorithm is employed to solve the joint UAD and CE problem. Furthermore, to augment the performance of EM-BSBL algorithm in GFMA systems, the statistical properties of the activity weights generated by EM-BSBL algorithm are investigated, showing that the activity weights follow closely the Gamma distribution. Then, using the Gamma modelling of the activity weights, the Neyman-Pearson (NP) method is considered for optimizing the threshold used for decision making in the EM-BSBL algorithm. Finally, the performance of GFMA systems is comprehensively studied by numerical simulations. Our results and analysis demonstrate that MC-GFMA is a feasible signalling scheme for supporting a massive number of users transmitting sporadic information. With the aid of the EM-BSBL algorithm enhanced by the NP-assisted threshold optimization, MC-GFMA is robust for operation in the communications environments where active users are random and the number of them is highly dynamic.
2441-2458
Zhao, Yi
9597217e-03eb-4687-84ee-5e9e610148b2
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
28 August 2025
Zhao, Yi
9597217e-03eb-4687-84ee-5e9e610148b2
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Zhao, Yi, El-Hajjar, Mohammed and Yang, Lie-Liang
(2025)
Joint user activity detection and channel estimation in MC-GFMA systems by block sparse Bayesian learning with threshold optimization.
IEEE Open Journal of Vehicular Technology, 6, .
(doi:10.1109/OJVT.2025.3603690).
Abstract
Future wireless communications are expected to support massive connectivity in various applications, such as massive Machine-Type Communications (mMTC) and different types of IoT networks, where many applications have the data traffic of sporadicnature. To support these kinds of applications, grant free multiple-access (GFMA) has been recognized to be more efficient thanthe conventional granted multiple access (GMA). However, due to sporadic transmission, GFMA faces the main challenges of User Activity Detection (UAD) and Channel Estimation (CE). To meet these challenges, in this paper, a multicarrier GFMA (MC-GFMA) system is introduced for supporting massive connectivity. A block-sparse signal model is derived, where the Expectation Maximization assisted Block Sparse Bayesian Learning (EM-BSBL) algorithm is employed to solve the joint UAD and CE problem. Furthermore, to augment the performance of EM-BSBL algorithm in GFMA systems, the statistical properties of the activity weights generated by EM-BSBL algorithm are investigated, showing that the activity weights follow closely the Gamma distribution. Then, using the Gamma modelling of the activity weights, the Neyman-Pearson (NP) method is considered for optimizing the threshold used for decision making in the EM-BSBL algorithm. Finally, the performance of GFMA systems is comprehensively studied by numerical simulations. Our results and analysis demonstrate that MC-GFMA is a feasible signalling scheme for supporting a massive number of users transmitting sporadic information. With the aid of the EM-BSBL algorithm enhanced by the NP-assisted threshold optimization, MC-GFMA is robust for operation in the communications environments where active users are random and the number of them is highly dynamic.
Text
Joint_User_Activity_Detection_and_Channel_Estimation_in_MC-GFMA_Systems_by_Block_Sparse_Bayesian_Learning_With_Threshold_Optimization
- Version of Record
More information
Accepted/In Press date: 25 August 2025
Published date: 28 August 2025
Identifiers
Local EPrints ID: 505524
URI: http://eprints.soton.ac.uk/id/eprint/505524
ISSN: 2644-1330
PURE UUID: 8f3c93a6-77d5-4f9d-828e-f310f5fe755a
Catalogue record
Date deposited: 10 Oct 2025 17:35
Last modified: 11 Oct 2025 01:51
Export record
Altmetrics
Contributors
Author:
Yi Zhao
Author:
Mohammed El-Hajjar
Author:
Lie-Liang Yang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics