A Low Complexity Antenna Selection Algorithm for Energy Efficiency in Massive MIMO Systems

Tai, Tzu-Hao; Chung, Wei-Ho; Lee, Ta-Sung

doi:10.1109/dsdis.2015.39

Cited by 16 publications

(23 citation statements)

References 15 publications

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“…On the base of the aforementioned discussion, a low complexity scheme is needed from the practical point of view for JASUS in multi-cell multi-user massive MIMO scenarios to reduce the complexity of function (11) while decreasing the cost of the CSI feedback.…”

Section: Computational Complexity: the Second Problem Ismentioning

confidence: 99%

“…where L MCMC denotes the total number of samples, and Φ Ã C is the estimated value of formula (11). Given that scheduled of per user ϖ u and selection of each antenna ω m are binary variables, we use the Boltzmann distribution of the objective function C i sum ð G fω ℓ;t ;ω ℓ;t g Þ with a suitable temperature τ…”

Section: Derivation Of the Candidate Sampling Distribution For The MCmentioning

confidence: 99%

“…Different schemes were used in many types of research, such as hybrid precoding and spatial modulation, to reduce the cost of the hardware and the power consumption of the system [10]. One of the best schemes to solve this problem is to applying antenna selection [11][12][13] to decide optimal subset of BS transmit antennas for decreasing the required number of high pricey RF chains while decreasing the resulting network performance loss.…”

Section: Introductionmentioning

confidence: 99%

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A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Maimaiti

Chuai

Gao

et al. 2019

J Wireless Com Network

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The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

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Section: Computational Complexity: the Second Problem Ismentioning

confidence: 99%

Section: Derivation Of the Candidate Sampling Distribution For The MCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Maimaiti

Chuai

Gao

et al. 2019

J Wireless Com Network

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show abstract

“…In [10] and [14], the authors proposed the transmit antenna selection algorithm based on the channel capacity analysis for MIMO and Massive MIMO systems, respectively. In [15], the authors proposed the transmit antenna selection algorithm based on the norm analysis and the correlation between columns of the channel matrix to maximize energy efficiency for Massive MIMO system. In [16], based on the principal components analysis technique, the authors proposed the transmit antenna selection algorithm to remove antennas that contribute least to the total channel capacity for Massive MIMO system.…”

mentioning

confidence: 99%

“…In [16], based on the principal components analysis technique, the authors proposed the transmit antenna selection algorithm to remove antennas that contribute least to the total channel capacity for Massive MIMO system. The proposed algorithms in [10], [13], [14], [15] and [16] significantly improve the energy efficiency and performance of the system. However, these algorithms are performed by scanning the antennas in a one-by-one basis.…”

mentioning

confidence: 99%

Transmit Antenna Selection Aided Linear Group Precoding for Massive MIMO Systems

Dinh¹,

Le²,

Ngo³

et al. 2019

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In this paper, we investigate the combination of linear group precoding with a transmit antenna group selection (TA-GS) algorithm based on the channel capacity analysis for Massive MIMO systems. Simultaneously, we propose a low complexity linear precoding algorithm that works on the selected antennas. The proposed precoder is developed based on the conventional linear precoders in combination with the element-base lattice reduction shortest longest vector technique having low complexity. Numerical and simulation results show that the system performance significantly improves when the transmit selection technique is applied. Besides, the proposed precoder has remarkably lower computational complexity than its LC-RBD-LR-ZF counterpart. The bit error rate (BER) performance of the proposed precoder can approach that of the LC-RBD-LR-ZF precoder as the number of groups increases, yet at the cost of higher detection complexity.

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Global-Searching-Based Iterative Swapping Antenna Selection for Massive MIMO Systems with Imperfect Channel Estimation

2018

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A Low Complexity Antenna Selection Algorithm for Energy Efficiency in Massive MIMO Systems

Cited by 16 publications

References 15 publications

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Transmit Antenna Selection Aided Linear Group Precoding for Massive MIMO Systems

Global-Searching-Based Iterative Swapping Antenna Selection for Massive MIMO Systems with Imperfect Channel Estimation

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