Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems

Khalid, Salman; Mehmood, Rashid; Abbas, Waqas Bin; Khalid, Farhan; Naeem, Muhammad

doi:10.1007/s11235-020-00728-z

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Khalid et al 21 developed a probabilistic distribution learning method that used an exhaustive search algorithm (ESA) for optimal TAS process and successive interference cancelation for proceeding. This method provides a better solution using an increased number of samples.…”

Section: Related Workmentioning

confidence: 99%

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An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

Singh

Kishoreraja

2022

Int J Communication

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Summary The massive multiple‐input multiple‐output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and transmit antenna selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA and the TAS of wireless communication in millimeter‐wave (mm‐wave) with massive MIMO technology are considered. Two different solutions are developed for this research such as the deep learning method for efficient resource allocation process and optimization algorithm for TAS process. Here, the RA process is done with the help of attention‐based capsule auto‐encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, battle royale optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipment (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances spectral efficiency (SE), energy efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE, and SE.

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Section: Related Workmentioning

confidence: 99%

“…The algorithm 21 learns from the probability distribution of the best possible solutions. For the real‐time antenna selection, this method was considered as the efficient and optimum solution.…”

Section: Related Workmentioning

confidence: 99%

An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

Singh

Kishoreraja

2022

Int J Communication

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“…Massive Multiple-Input Multiple-Output (MIMO) systems have achieved importance by improving the spatial multiplexing gain and system energy efficiency [1]. Some emerging wireless systems have used the millimeter-wave range to achieve higher data throughput in a less occupied frequency range.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, some architectures are used to transmit with a large number of antennas. Fully connected architectures are characterized by the connection of each antenna element to the RF chain [1]. This approach provides a good spectral efficiency in spite of the high complexity.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Algorithm for Full-Array Antenna Selection based on Matching Pursuit

Gois¹,

Ferreira²

2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Massive MIMO systems have received a growing interest since the apogee of 5G and the perspective of 6G. Massive MIMO allows an efficient usage of spectral resources and provides higher data rates. When the number of antennas increases, the energy consumption and the hardware cost in the base station also increase. The high energy consumption and processing may be addressed through the selection of antennas. Among various possible structures in the literature, the present work proposes the combination of full-array architecture to make a virtual sectorization and antenna selection using matching pursuit. The algorithm was tested in different channels with complexity reduction in our tests while maintaining bit-error rate in most scenarios.

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“…This may require the knowledge of the channel and some signal processing technique is available in the recent advanced massive MIMO system [6]. Choosing the optimum subset could be a challenging task as it added complexity to the process [4]. Many research articles have been presented in this field.…”

Section: Introduction (10 Pt)mentioning

confidence: 99%

Study of the Impact of Antenna Selection Algorithms of Massive MIMO on Capacity and Energy Efficiency In 5G Communication Systems

Abdullah¹,

Mohmood²,

Ali³

2021

(AREJ)

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Massive MIMO system in the fifth generation can consume a large amount of energy. In this research, the selection of antennas was studied based on several algorithms and a new algorithm was proposed for the selection of antennas. The results showed through a comparison between the approved and proposed methods in the research in terms of capacitance that The proposed algorithm is the closest in terms of capacity to the ideal case, as this method is considered almost ideal for its application in multi-input and multi-output systems to improve performance in the fifth generation, followed by the greedy algorithm and algorithm norm, as it was noted from During the comparison in terms of energy efficiency, it is possible to increase energy efficiency when choosing a certain number of antennas

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Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems

Cited by 9 publications

References 26 publications

An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

Low-Complexity Algorithm for Full-Array Antenna Selection based on Matching Pursuit

Study of the Impact of Antenna Selection Algorithms of Massive MIMO on Capacity and Energy Efficiency In 5G Communication Systems

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