Gaussian mixture-learned approximate message passing (GM-LAMP) based hybrid precoders for mmWave massive MIMO systems
Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design...
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| Published in: | China communications Vol. 21; no. 12; pp. 66 - 79 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
China Institute of Communications
01.12.2024
Department of Electronics and Communication Engineering,Presidency University,Itgalpura,Rajanukunte,Yelahanka,Bengaluru,Karnataka 560064,India%Department of Electronics and Communication Engineering,Bannari Amman Institute of Technology,Erode,Tamil Nadu,638 401,India |
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| ISSN: | 1673-5447 |
| Online Access: | Get full text |
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| Abstract | Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems. Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit (OMP) and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high. This drawback can be addressed using classical iterative algorithms such as approximate message passing (AMP), which has comparatively low computational complexity. The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders. In this paper, the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP (LAMP) network, and is further enhanced as GM-LAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders. The simulation results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates, better accuracy and low computational complexity compared to the existing algorithms. |
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| AbstractList | Hybrid precoder design is a key tech-nique providing better antenna gain and reduced hard-ware complexity in millimeter-wave(mmWave)mas-sive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iter-ative algorithms such as approximate message pass-ing(AMP),which has comparatively low computa-tional complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GM-LAMP network using the concept of Gaussian Mix-ture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP net-work achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing al-gorithms. Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems. Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit (OMP) and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high. This drawback can be addressed using classical iterative algorithms such as approximate message passing (AMP), which has comparatively low computational complexity. The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders. In this paper, the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP (LAMP) network, and is further enhanced as GM-LAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders. The simulation results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates, better accuracy and low computational complexity compared to the existing algorithms. |
| Author | Ali, K. Shoukath Philip, Sajan P. Perarasi, T. |
| AuthorAffiliation | Department of Electronics and Communication Engineering,Presidency University,Itgalpura,Rajanukunte,Yelahanka,Bengaluru,Karnataka 560064,India%Department of Electronics and Communication Engineering,Bannari Amman Institute of Technology,Erode,Tamil Nadu,638 401,India |
| AuthorAffiliation_xml | – name: Department of Electronics and Communication Engineering,Presidency University,Itgalpura,Rajanukunte,Yelahanka,Bengaluru,Karnataka 560064,India%Department of Electronics and Communication Engineering,Bannari Amman Institute of Technology,Erode,Tamil Nadu,638 401,India |
| Author_xml | – sequence: 1 givenname: K. Shoukath surname: Ali fullname: Ali, K. Shoukath organization: Department of Electronics and Communication Engineering, Presidency University, Itgalpura, Rajanukunte, Yelahanka, Bengaluru, Karnataka 560064, India – sequence: 2 givenname: Sajan P. surname: Philip fullname: Philip, Sajan P. organization: Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, 638 401, India – sequence: 3 givenname: T. surname: Perarasi fullname: Perarasi, T. organization: Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, 638 401, India |
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| Keywords | deep neu-ral network massive MIMO millimeter wave approximate message passing Gaussian Mixture model |
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| Publisher | China Institute of Communications Department of Electronics and Communication Engineering,Presidency University,Itgalpura,Rajanukunte,Yelahanka,Bengaluru,Karnataka 560064,India%Department of Electronics and Communication Engineering,Bannari Amman Institute of Technology,Erode,Tamil Nadu,638 401,India |
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| Snippet | Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input... Hybrid precoder design is a key tech-nique providing better antenna gain and reduced hard-ware complexity in millimeter-wave(mmWave)mas-sive multiple-input... |
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| SubjectTerms | approximate message passing Baseband Computational complexity deep neural network Gaussian Mixture model Massive MIMO Matching pursuit algorithms Mathematical models Message passing millimeter wave Millimeter wave communication Radio frequency Sparse matrices Spectral efficiency |
| Title | Gaussian mixture-learned approximate message passing (GM-LAMP) based hybrid precoders for mmWave massive MIMO systems |
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