Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system

Liu, Xiaokai; Zhao, Chenglin; Wang, Pengbiao; Zhang, Yang; Yang, Tianpu

doi:10.1049/iet-com.2015.1222

Cited by 18 publications

(12 citation statements)

References 25 publications

(24 reference statements)

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“…• Based on the training feature-label pairs (22) where ξ r i is the penalty parameter, C r is the regularization parameter, while w and b are the linear parameters for the rth SVM.…”

Section: B Proposed Slc Methodsmentioning

confidence: 99%

“…In [21], the authors exploited extreme learning machine (ELM), which is a simple version of the DNN with a fast learning speed, to reduce the minimum Bayes risk in physical-layer authentication. Moreover, in [22] ELM was further developed for blind MIMO signal classification in spatially correlated channels. In [23], the inherent parallel structure of the DNN was utilized for one-shot decoding of random and structured codes, such as polar codes.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

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Adaptive Spatial Modulation MIMO Based on Machine Learning

Yang

Xiao

et al. 2019

IEEE J. Select. Areas Commun.

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In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multipleinput multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a nearoptimal performance with a significantly lower complexity.

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“…• Based on the training feature-label pairs (22) where ξ r i is the penalty parameter, C r is the regularization parameter, while w and b are the linear parameters for the rth SVM.…”

Section: B Proposed Slc Methodsmentioning

confidence: 99%

Section: A Related Work and Motivationmentioning

confidence: 99%

Adaptive Spatial Modulation MIMO Based on Machine Learning

Yang

Xiao

et al. 2019

IEEE J. Select. Areas Commun.

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“…The classified feature data after clustering are taken as the training set and the verification set of the neural network. Since the input data of neural network have been extracted by clustering method, this paper takes a relatively mature inverse error back propagation (BP) algorithm [17,18]. The designed inverse error propagation neural network is shown in Figure 10 Therefore, the decision of retransmission mode is to select the one with the largest value from the output.…”

Section: Algorithm Modelmentioning

confidence: 99%

Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

Wang

Fang

et al. 2021

IET Communications

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With the increase of Wi-Fi connections and throughput, the problems posed by unreliable connection and unstable delay need to be urgently solved through advanced methods. The IEEE 802.11ax standard was designed to improve the reliability of connection and increase the throughput in dense Wi-Fi scenario where the inter-site interference becomes more serious and the spectral resources are more insufficient. Furthermore, in the next generation of Wi-Fi standard IEEE 802.11be, the hybrid automatic repeat request (HARQ) and multi-band technologies will be introduced to solve the problems of unstable delay, serious interference and insufficient spectrum resources. In this paper, retransmission schemes combined multi-band and hybrid automatic repeat request are deeply studied to improve the retransmission efficiency. Combining hybrid automatic repeat request with multi-band, this paper proposes three kinds of retransmission mode: insisting on the current frequency band (ICFB) retransmission, switching to the backup frequency band (SBFB) retransmission and concurrent retransmitting on dual-band (CRDB). Then, an intelligent selection algorithm of retransmission mode based on machine learning is designed to determine the optimal mode. Theoretical analysis and experimental results show that the proposed method can greatly improve the retransmission efficiency and thereby reduce the transmission latency and increase the throughput for multi-band Wi-Fi networks.

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“…Based on Eqs. (3-5), if signal y is zero-mean with N samples, the moments and the cumulant can be expressed as in [19].…”

Section: Features Extractionmentioning

confidence: 99%

“…As in the paper [19,20], signal modulation recognition using ELM method includes the following steps:…”

Section: Extreme Learning Machinesmentioning

confidence: 99%

A new method to solve the problem of facing less learning samples in signal modulation recognition

Liu

2020

J Wireless Com Network

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Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system

Cited by 18 publications

References 25 publications

Adaptive Spatial Modulation MIMO Based on Machine Learning

Adaptive Spatial Modulation MIMO Based on Machine Learning

Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

A new method to solve the problem of facing less learning samples in signal modulation recognition

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