Intelligent based hybrid precoder for millimetre wave massive MIMO system

Rajarajeswarie, B.; Sandanalakshmi, R.

doi:10.1007/s11276-023-03245-4

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Conventional fully digital (FD) beamforming architectures necessitate a dedicated radio frequency (RF) chain for each antenna, leading to substantial power and hardware consumption in practical implementation [4,5]. Owing to comparable performance and a substantial reduction in the number of RF chains, the hybrid beamforming (HBF) architecture emerges as a viable solution for striking a balance between the system performance and hardware efficiency [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Connected Hybrid Beamforming for Energy Efficiency Maximization in Multi-User Millimeter Wave Systems

Chen,

Zhang,

Miao

et al. 2024

Preprint

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Energy efficiency (EE) is a key enabler for sustainable green millimeter wave (mmWave) communication. However, conventional hybrid beamforming methods suffer from energy efficiency performance loss due to hardware limitations and the connected structure. To overcome these limitations, this work investigates a novel adaptive connected (AC) hybrid beamforming (HBF) design for multi-user mmWave systems. We formulate the problem as an EE maximization problem subject to the constraints of the adaptive connected structure, constant modulus, and power budget. Addressing this complicated non-convex problem, we harness the characteristics of the AC structure and introduce an iterative HBF design algorithm grounded in fractional programming. Numerical results demonstrate the effectiveness and flexibility of the proposed AC-based HBF design in terms of EE enhancement.

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Section: Introductionmentioning

confidence: 99%

Adaptive Connected Hybrid Beamforming for Energy Efficiency Maximization in Multi-User Millimeter Wave Systems

Chen,

Zhang,

Miao

et al. 2024

Preprint

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“…DL techniques, including a learned denoising-based approximate message passing (LDAMP) network and a spatial-frequency convolutional neural network (SF-CNN), were required to reconstruct millimeter-wave channels. Consideration is given to the relationships between space and frequency by these methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

Shailender,

Khera,

Singh

2024

Preprint

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In mm-wave massive MIMO systems, the inefficiency of the digital or analogue precoders necessitates that each antenna has its own radio frequency chain. From this standpoint, a method of hybrid precoding that is economically viable has been devised. Digital beamformers are used for controlling antenna elements using short-dimensional precoding. From this standpoint, a method of hybrid precoding that is economically viable has been devised. During the channel estimation phase, we propose using deep recurrent convolutional neural networks (Deep RCNNs). Salp Swarm Algorithm (SSA) and Tuna Swarm Optimization (TSO) algorithms are combined in a hybrid meta-heuristic algorithm to improve Deep RCNN's channel estimation efficiency. O-BiLSTM, or optimised bidirectional long short-term memory, is utilised by hybrid precoding in order to execute the optimal precode across the anticipated channel. In relation to spectral efficiency and normalised mean square error (NMSE), the enhanced channel estimation approach surpasses the existing schemes through the implementation of Deep RCNN. The spectrum efficacy of the O-BiLSTM hybrid precoder design method is observed to be superior when compared to alternative approaches.

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Adaptive connected hybrid beamforming for energy efficiency maximization in multi-user millimeter wave systems

Chen,

Zhang,

Miao

et al. 2024

Wireless Netw

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Intelligent based hybrid precoder for millimetre wave massive MIMO system

Cited by 3 publications

References 23 publications

Adaptive Connected Hybrid Beamforming for Energy Efficiency Maximization in Multi-User Millimeter Wave Systems

Adaptive Connected Hybrid Beamforming for Energy Efficiency Maximization in Multi-User Millimeter Wave Systems

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

Adaptive connected hybrid beamforming for energy efficiency maximization in multi-user millimeter wave systems

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