Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions

Ha, Jong-Gyu; Ro, Jae-Hyun; Song, Hyoung‐Kyu

doi:10.3390/electronics8070758

Cited by 4 publications

(4 citation statements)

References 21 publications

(31 reference statements)

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“…Its optimization criteria is to maximize the minimum uplink signal-to-interference plus noise power ratio (SINR). Efficient channel state information (CSI) estimation scheme was proposed in [13]. It exploits prior CSI of the previous timeslot having temporal correlation in the angular domain.…”

Section: The Present Issuementioning

confidence: 99%

Massive MIMO Systems: Present and Future

Maruta

Falcone

2020

Electronics

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Section: The Present Issuementioning

confidence: 99%

Massive MIMO Systems: Present and Future

Maruta

Falcone

2020

Electronics

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“…A review of modern publications devoted to MIMO modelling [6,7,[12][13][14] showed that scientists prefer to describe such systems at the physical level with the implementation of an electromagnetic approach to modelling a MIMO transmission channel. In most cases, the functioning of multiantenna communication systems is modelled using the equivalent representation of the latter in the form of multipoles [14][15][16][17]. In the basis of this approach, the transmitting part (signal generators, transmitting devices, matching devices), the receiving part (receiving devices, matching devices, payloads) and the communication channel (signal propagation medium, transmitting and receiving antennas) are represented by equivalent multipoles and are described by the corresponding parametric matrices.…”

Section: Introductionmentioning

confidence: 99%

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

Kovtun,

Altameem,

Al-Maitah

et al. 2023

Electronics

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In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even in the case of using Wi-Fi 6/6E-compatible equipment. An effective solution to this problem is the implementation of a centralized management mechanism for the relevant parameters of the target network ecosystem. The growing attention to ecology and rational use of electricity makes the problem of maximizing energy consumption for useful information traffic in a dense Wi-Fi 6/6E ecosystem an urgent task. Only the addressed information traffic between the transmitter and the target subscriber, which are subjects of the OFDMA technology and the MU-MIMO multiple access system (with an emphasis on the latter), is considered useful. To solve the problem, the authors formalized the Wi-Fi 6/6E ecosystem’s energy consumption model, which takes into account the specifics of OFDMA and MU-MIMO, the influence of the communication channel characteristics on the speed of target information transfer, and detailed energy consumption for maintaining the network infrastructure in a functional state. Based on the created model, the research problem is represented by the difference between two monotonic functions, relative to which the problem of optimization with restrictions is set. The process of solving this problem is presented in the form of information technology with a branch-and-bound hierarchy and a nested unconditional optimization problem. The results of simulated modelling in the MATLAB-NS3 environment showed a significant advantage of the authors’ approach. The energy power consumption by the Wi-Fi 6/6E ecosystem, the parameters of which were adjusted with the help of the authors’ information technology, decreased by more than four times.

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“…In the future, studies aim to increase channel capacity in the overall wireless communication systems in order to handle explosive data traffic [1]. Multiple-input multiple-output (MIMO) technology has been studied in the wireless communication systems since tremendous increase of channel capacity is implemented [2][3][4][5][6][7]. In the MIMO systems, accurate signal detection is one of the main issues and various researches for the MIMO detection have been proposed for efficient signal detection [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

Ro¹,

Ha²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights. For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver. The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.

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Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions

Cited by 4 publications

References 21 publications

Massive MIMO Systems: Present and Future

Massive MIMO Systems: Present and Future

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

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