Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains
Energy efficiency (EE) is the main target of wireless communication nowadays. In this paper, we investigate hybrid precoding (HP) and massive multiple-input multiple-output (MIMO) systems for a high-altitude platform (HAP). The HAP is an emerging solution operating in the stratosphere at an amplitud...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/4/893 |
| _version_ | 1797621322580230144 |
|---|---|
| author | Shabih ul Hassan Talha Mir Sultan Alamri Naseer Ahmed Khan Usama Mir |
| author_facet | Shabih ul Hassan Talha Mir Sultan Alamri Naseer Ahmed Khan Usama Mir |
| author_sort | Shabih ul Hassan |
| collection | DOAJ |
| description | Energy efficiency (EE) is the main target of wireless communication nowadays. In this paper, we investigate hybrid precoding (HP) and massive multiple-input multiple-output (MIMO) systems for a high-altitude platform (HAP). The HAP is an emerging solution operating in the stratosphere at an amplitude of up to 20–40 km to provide communication facilities that can achieve the best features of both terrestrial and satellite systems. The existing hybrid beamforming solution on a HAP requires a large number of high-resolution phase shifters (PSs) to realize analog beamforming and radio frequency (RF) chains associated with each antenna and achieve better performance. This leads to enormous power consumption, high costs, and high hardware complexity. To address such issues, one possible solution that has to be tweaked is to minimize the number of PSs and RFs or reduce their power consumption. This study proposes an HP sub-connected low-resolution bit PSs to address these challenges while lowering overall power consumption and achieving EE. To significantly reduce the RF chain in a massive MIMO system, HP is a suitable solution. This study further examined adaptive cross-entropy (ACE), a machine learning-based optimization that optimizes the achievable sum rate and energy efficiency in the Rician fading channel for HAP massive MIMO systems. ACE randomly generates several candidate solutions according to the probability distribution (PD) of the elements in HP. According to their sum rate, it adaptively weights these candidates’ HP and improves the PD in HP systems by minimizing the cross-entropy. Furthermore, this work suggests energy consumption analysis performance evaluation to unveil the fact that the proposed technique based on a sub-connected low-bit PS architecture can achieve near-optimum EE and sum rates compared with the previously reported methods. |
| first_indexed | 2024-03-11T08:55:17Z |
| format | Article |
| id | doaj.art-ca4472424bc646fcba515041550357dc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:55:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ca4472424bc646fcba515041550357dc2023-11-16T20:11:35ZengMDPI AGElectronics2079-92922023-02-0112489310.3390/electronics12040893Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF ChainsShabih ul Hassan0Talha Mir1Sultan Alamri2Naseer Ahmed Khan3Usama Mir4Information and Communication, University of Science and Technology of China, Hefei 230052, ChinaDepartment of Electronic Engineering, Baluchistan University of IT, Engineering and Management Sciences Pakistan (BUITEMS), Quetta 87300, PakistanCollege of Computing and Informatics, Saudi Electronic University, Riyadh 13316, Saudi ArabiaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710060, ChinaSchool of Computer Science, University of Windsor, Windsor, ON N9B 3P4, CanadaEnergy efficiency (EE) is the main target of wireless communication nowadays. In this paper, we investigate hybrid precoding (HP) and massive multiple-input multiple-output (MIMO) systems for a high-altitude platform (HAP). The HAP is an emerging solution operating in the stratosphere at an amplitude of up to 20–40 km to provide communication facilities that can achieve the best features of both terrestrial and satellite systems. The existing hybrid beamforming solution on a HAP requires a large number of high-resolution phase shifters (PSs) to realize analog beamforming and radio frequency (RF) chains associated with each antenna and achieve better performance. This leads to enormous power consumption, high costs, and high hardware complexity. To address such issues, one possible solution that has to be tweaked is to minimize the number of PSs and RFs or reduce their power consumption. This study proposes an HP sub-connected low-resolution bit PSs to address these challenges while lowering overall power consumption and achieving EE. To significantly reduce the RF chain in a massive MIMO system, HP is a suitable solution. This study further examined adaptive cross-entropy (ACE), a machine learning-based optimization that optimizes the achievable sum rate and energy efficiency in the Rician fading channel for HAP massive MIMO systems. ACE randomly generates several candidate solutions according to the probability distribution (PD) of the elements in HP. According to their sum rate, it adaptively weights these candidates’ HP and improves the PD in HP systems by minimizing the cross-entropy. Furthermore, this work suggests energy consumption analysis performance evaluation to unveil the fact that the proposed technique based on a sub-connected low-bit PS architecture can achieve near-optimum EE and sum rates compared with the previously reported methods.https://www.mdpi.com/2079-9292/12/4/893energy efficiencyhybrid precodingmassive MIMOmachine learningadaptive cross-entropyRician fading channel |
| spellingShingle | Shabih ul Hassan Talha Mir Sultan Alamri Naseer Ahmed Khan Usama Mir Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains Electronics energy efficiency hybrid precoding massive MIMO machine learning adaptive cross-entropy Rician fading channel |
| title | Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains |
| title_full | Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains |
| title_fullStr | Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains |
| title_full_unstemmed | Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains |
| title_short | Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains |
| title_sort | machine learning inspired hybrid precoding for hap massive mimo systems with limited rf chains |
| topic | energy efficiency hybrid precoding massive MIMO machine learning adaptive cross-entropy Rician fading channel |
| url | https://www.mdpi.com/2079-9292/12/4/893 |
| work_keys_str_mv | AT shabihulhassan machinelearninginspiredhybridprecodingforhapmassivemimosystemswithlimitedrfchains AT talhamir machinelearninginspiredhybridprecodingforhapmassivemimosystemswithlimitedrfchains AT sultanalamri machinelearninginspiredhybridprecodingforhapmassivemimosystemswithlimitedrfchains AT naseerahmedkhan machinelearninginspiredhybridprecodingforhapmassivemimosystemswithlimitedrfchains AT usamamir machinelearninginspiredhybridprecodingforhapmassivemimosystemswithlimitedrfchains |