Power allocation algorithms for massive MIMO systems with multi-antenna users

Bobrov, Evgeny; Boris, Chinyaev,; Кузнецов, В. А.; Миненков, Д. С.; Daniil, Yudakov,

doi:10.1007/s11276-023-03442-1

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…Distributed grid matching pursuit (DGMP) [16], a channel estimation technique that locates and intermittently updates the common elements along the lineof-sight (LOS) channel path, is one of the developments in this field. An alternative approach to identify dominant elements across multiple channel paths involves the implementation of a channel estimation algorithm known as orthogonal matching pursuit (OMP) [17]. An alternative method for sensitively estimating the frequency of mmWave channels [18]; SWOMP is an extension of the OMP method and stands for simultaneous weighted orthogonal matching pursuit.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…This subsection presents the methodology of power allocation to maximize the DASR of an FDD system. While several power allocation approaches have been investigated in the literature, see, e.g., [64][65][66][67][68][69][70], the concentrate was based on the TDD transmission mode. Here, we investigate the power allocation process to maximize the DASR in the FDD transmission mode using a simple straightforward approach.…”

Section: Power Allocation Optimization Processmentioning

confidence: 99%

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

Naser,

Salman,

Alsabah

2024

Telecom

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The expected development of the future generation of wireless communications systems such as 6G aims to achieve an ultrareliable and low-latency communications (URLLCs) while maximizing the data rates. These requirements push research into developing new advanced technologies. To this end, massive multiple input multiple output (MMIMO) is introduced as a promising transmission approach to fulfill these requirements. However, maximizing the downlink-achievable sum rate (DASR) in MMIMO with a frequency division duplex (FDD) transmission mode and limited coherence time (LCT) is very challenging. To address this challenge, this paper proposes a DASR maximization approach using a feasible power allocation optimization method. The proposed approach is based on smartly allocating the total transmit power between the data transmission and training sequence transmission for channel estimation. This can be achieved by allocating more energy to the training signal than the data transmission during the channel estimation process to improve the quality of channel estimation without compromising more training sequence length, thus maximizing the DASR. Additionally, the theory of random matrix approach is exploited to derive an asymptotic closed-form expression for the DASR with a regularized zero-forcing precoder (RZFP), which allows the power optimization process to be achieved without the need for computationally complex Monte Carlo simulations. The results provided in this paper indicate that a considerable enhancement in the DASR performance is achieved using the proposed power allocation method in comparison with the conventional uniform power allocation method.

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Optimized precoding for massive MU-MIMO systems with KLDA dimension reduction and RNN-crossover GBO algorithm

Ramanathan,

Bennet

2024

Telecommun Syst

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Power allocation algorithms for massive MIMO systems with multi-antenna users

Cited by 3 publications

References 49 publications

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

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

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

Optimized precoding for massive MU-MIMO systems with KLDA dimension reduction and RNN-crossover GBO algorithm

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