S. Berra, S. Chakraborty, R. Dinis and S. Shahabuddin, "Deep Unfolding of Chebyshev Accelerated Iterative Method for Massive MIMO Detection," in IEEE Access, vol. 11, pp. 52555-52569, 2023, doi: 10.1109/ACCESS.2023.3279350.
Deep unfolding of Chebyshev accelerated iterative method for massive MIMO detection
|Author:||Berra, Salah1; Chakraborty, Sourav2; Dinis, Rui3;|
1COPELABS, Universidade Lusófona, Lisbon, Portugal
2Department of Electronics and Communication Engineering, Cooch Behar Government Engineering College, Cooch Behar, India
3Instituto de Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
4Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023062759985
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-06-28
The zero-forcing (ZF) and minimum mean square error (MMSE) based detectors can approach optimal performance in the uplink of massive multiple-input multiple-output (MIMO) systems. However, they require inverting a matrix whose complexity is cubic in relation to the matrix dimension. This can lead to the high computational effort, especially in massive MIMO systems. To mitigate this, several iterative methods have been proposed in the literature. In this paper, we consider accelerated Chebyshev SOR (AC-SOR) and accelerated Chebyshev AOR (AC-AOR) algorithms, which improve the detection performance of conventional Successive Over-Relaxation (SOR) and Accelerated Over-Relaxation (AOR) methods, respectively. Additionally, we propose using a deep unfolding network (DUN) to optimize the parameters of the iterative AC-SOR and AC-AOR algorithms, leading to the AC-AORNet and AC-SORNet methods, respectively. The proposed DUN-based method leads to significant performance improvements compared to conventional iterative detectors for various massive MIMO channels. The results demonstrate that the AC-AORNet and AC-SORNet are effective, outperforming other state-of-the-art algorithms. Furthermore, they are highly effective, particularly for high-order modulations such as 256-QAM (Quadrature Amplitude Modulation). Moreover, the proposed AC-AORNet and AC-SORNet require almost the same number of computations as AC-AOR and AC-SOR methods, respectively, since the use of deep unfolding has a negligible impact on the system’s detection complexity. Furthermore, the proposed DUN features a fast and stable training scheme due to its smaller number of trainable parameters.
|Pages:||52555 - 52569|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) under the projects Copelabs (UIDB/04111/2020), Instituto de Telecomunicações (UIDB/50008/2020) and CELL-LESS6G (2022.08786.PTDC).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/