Wideband MIMO Radar Transmit Beampattern Synthesis via Majorization–Minimization

Huang, Zhongrui; Tang, Bo; Wang, Hai; Qin, Long

doi:10.1007/s00034-021-01735-4

Cited by 2 publications

(5 citation statements)

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“…With the SLM 2 approach, the author achieved minimized computational complexity but it requires designing the antenna collective SLM approach on the MIMO system. The author accomplished less matching error with CO‐MM 6 . However, concerning the channel stimulation rate is still vague.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The author accomplished less matching error with CO-MM. 6 However, concerning the channel stimulation rate is still vague. Using DNN, 1 the author achieved better performance but it requires an adequate bandwidth rate for efficient communication.…”

Section: Reviewmentioning

confidence: 99%

“…In 2021, Huang et al 6 has framed a novel iterative strategy dependent on the CO‐MM technique. This proposed work ensured convergence by displaying the series of objective functions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, it can lower the price of equipment as well as computing complications while maintaining a sufficient gain rate as well as SNR. Obtaining the best AS typically requires assessing every potential selection via accurate searching 5 6 …”

Section: Introductionmentioning

confidence: 99%

“…Obtaining the best AS typically requires assessing every potential selection via accurate searching 5 . 6 Such optimization-driven approaches select antenna subsets based on the quality of communication parameters including BER, and channel capacity. AS optimization problems are never convex 7 .…”

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confidence: 99%

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Deep improved bidirectional long short‐term memory‐based antenna selection for multiple input multiple output software defined radio system

Chilveri

2024

Int J Communication

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SummaryMultiple input multiple output (MIMO) constitutes a wireless approach that employs several senders and receivers in order to send numerous data at once. Due to the rapid increase of cellular mobile device usage and the limitation of computing power, antenna selection (AS) has attracted more and more attention recently. AS can keep a balance between communication performance and computational complexity. In this paper, a novel deep learning (DL)‐based AS for MIMO software defined radio (SDR) system is proposed. The main objective of this work is to select the optimal antennas in MIMO systems to balance the efficiency of communication and maximize the secrecy capacity in massive MIMOME channels. First, we set up a DL‐based AS‐aided MIMO SDR environment that ensures intelligent decisions for AS. Accordingly, the hybrid loss function induced in bidirectional long short term memory (HLFI‐Bi‐LSTM) model is proposed to build the decision server, in which it takes some estimated features as input. The features such as channel state information, received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and antenna gain are considered accordingly as the input to HLFI‐Bi‐LSTM. The HLFI‐Bi‐LSTM model is the extended version of Bi‐LSTM, which is improved by inducing the hybrid loss function, which is the combination of two functions namely, combo loss and inverse mean square error. The proposed HLFI‐Bi‐LSTM attained the greater accuracy value of 0.968 at 80% for optimal selection of the antenna.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

“…In 2021, Huang et al 6 has framed a novel iterative strategy dependent on the CO‐MM technique. This proposed work ensured convergence by displaying the series of objective functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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