Blind Detection for Spatial Modulation Systems Based on Clustering

You, Longfei; Yang, Ping; Xiao, Yao; Shi, Rong; Deng, Ke; Li, Shaoqian

doi:10.1109/lcomm.2017.2734648

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…Specifically, the authors of [35] applied a K-means clustering (KMC) for blind detection in space shift keying (SSK) systems. To mitigate the error floor effect in [35], we proposed an improved KMC detector in [36], which selects the initial centroids based on a novel rule. Moreover, in [36] we proposed an affinity propagation detector based on belief propagation, to strike a tradeoff between the imposed computational complexity and the attainable BER.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

“…To mitigate the error floor effect in [35], we proposed an improved KMC detector in [36], which selects the initial centroids based on a novel rule. Moreover, in [36] we proposed an affinity propagation detector based on belief propagation, to strike a tradeoff between the imposed computational complexity and the attainable BER. These data-driven methods have shown to be capable of outperforming conventional detectors.…”

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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Section: A Related Work and Motivationmentioning

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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“…In [28], a coding-aided K-means clustering (CKMC) blind detector for space shift keying (SSK) multiple-input multiple-output (MIMO) systems is proposed where the training of CSI is not required. Improved K-means blind detectors are proposed in [29], [30] to avoid the error floor effects caused by bad initial cluster centers. Density-based spatial clustering applications with noise (DBSCAN) algorithm is utilized to address the problem of anomaly detection [31].…”

Section: A Related Workmentioning

confidence: 99%

Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks

et al. 2019

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To realize the Internet of Things, one of the essential elements is wireless sensor networks which can sense the physical conditions of the environment. The ubiquitous sensing is achieved by a large number of spatially dispersed sensors and distributed estimation technology. However, the low-cost sensors are insufficient to support conventional distributed estimation schemes. Since most conventional schemes include channel training process, the resource consumption of which is enormous. Thus, one key challenge in designing a feasible distributed estimation scheme is to reduce resource consumption from channel training. We tackle the challenge by proposing a distributed blind estimation scheme. The proposed scheme consists of two components: random transmission and statistical inference. Specifically, assuming sensors contain only two states that are active and inactive. The random transmission strategy turns the sensing value into a parameter to govern the sensor states. At the fusion center, statistical inference method is used to recover the sensing value. The specific design of the inference method involves the distribution approximation and clustering, which are accomplished by Gaussian mixture model and expectation-maximization principle. By the proposed scheme, the channel information is no longer needed in distributed estimation. Therefore, it is more energy-efficient and more applicable to the complicated wireless environment compared with conventional schemes. Besides, we investigate the impacts of the number of sensors and quantization on the estimation performance. Finally, simulation results demonstrate the effectiveness of the proposed blind estimation scheme.

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“…• We convert the blind detection problem of indoor mmWave communications into an unsupervised clustering problem (see Section III for details). Although clustering is a widely studied problem in a variety of application domains including data mining [13], spatial modulation [14], [15] and coherent optical communication [16]. To the best of our knowledge, this perspective to analysis nonlinear detection problem of indoor mmWave communications has not been investigated before our work.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

Zhang

Liu

et al. 2019

IEEE Access

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Millimeter-wave (mmWave) systems have been considered as a promising candidate for 5G networks because of their potential advances in significant bandwidth enhancement. However, due to the extremely high operating frequency of mmWave systems, they generally suffer from severe frequencyselective propagation and nonlinear distortion in the power amplifier, which introduces unfavorable impact on the signal detection process. We discover that for indoor mmWave communications, the constellation of signals becomes much more ''clean and tidy'' at the receiver side compared with the current wireless systems (e.g., LTE and WiFi), thanks to the channel sparsity characteristic of mmWave communications. Motivated by this observation, we propose in this paper several detection algorithms. Specifically, K-means clustering (KMC) algorithm is first introduced into clustering signal detection due to its advantage in the circle or spherical cluster shape. Then, an improved KMC detector is proposed to avoid the deficiencies of KMC for the error floor and high complexity. Moreover, a density and distance-based clustering detector, a non-iterative algorithm, is proposed and it does not need to preset the number of clusters. The aboveproposed algorithms do not require any prior information about the power amplifier and the channel state information at the receiver end, which presents noticeable practical achievements on cost, complexity, and hardware constraints. The simulation results verify the effectiveness of the proposed schemes.INDEX TERMS Millimeter-wave (mmWave) communication, nonlinear signal detection, nonlinear power amplifier, unsupervised clustering.

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Blind Detection for Spatial Modulation Systems Based on Clustering

Cited by 15 publications

References 18 publications

Adaptive Spatial Modulation MIMO Based on Machine Learning

Adaptive Spatial Modulation MIMO Based on Machine Learning

Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks

Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

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