Machine Learning Assisted Adaptive Index Modulation for mmWave Communications

Liu, Haochen; Lu, Siyao; El‐Hajjar, Mohammed; Yang, Lie‐Liang

doi:10.1109/ojcoms.2020.3024724

Cited by 20 publications

(33 citation statements)

References 47 publications

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“…Multiuser LACO-OFDM: Originally conceived way back in the 1960s [104], Multiple-Input Multiple-Output (MIMO) systems have been widely studied in the RF field [105]- [108] for supporting simultaneous communications for multiple users within the same spectral resources. More recently, adopting MultiUser MIMO (MU-MIMO) techniques for VLC systems has also attracted much attention [109], [110] for further improving the system's performance.…”

Section: Future Research Challengesmentioning

confidence: 99%

The Evolution of Optical OFDM

Zhang

Babar

Petropoulos

et al. 2021

IEEE Commun. Surv. Tutorials

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Optical Orthogonal Frequency-Division Multiplexing (O-OFDM) is eminently suitable for mitigating the multi-path and chromatic dispersion in both Visible Light Communications (VLC) and Optical Fiber Communications. We commence our discourse by surveying the conception and historic evolution of O-OFDM designed for both VLC and optical fiber, culminating in the birth of its most flexible design alternative, namely Layered Asymmetrically Clipped Optical OFDM (LACO-OFDM). We demonstrate that it is eminently suitable for intensity-modulation and direct-detection aided optical communication systems and characterize its design flexibility. It is also shown that given its flexibility, it subsumes a wide range of optical OFDM schemes conceived over the past two decades or so. The LACO-OFDM transmitter and receiver designs strike a compelling compromise between the features of the popular Asymmetrically Clipped Optical OFDM (ACO-OFDM) and Direct Current Offset OFDM (DCO-OFDM). The pivotal role of forward error correction (FEC) designs is also surveyed with the objective of striking a coding gain versus complexity trade-off. We conclude by highlighting a suite of promising techniques capable of further improving the system performance, but require further research. The take-away message of the paper crystallized in the associated design guidelines.

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Section: Future Research Challengesmentioning

confidence: 99%

The Evolution of Optical OFDM

Zhang

Babar

Petropoulos

et al. 2021

IEEE Commun. Surv. Tutorials

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show abstract

“…For better configuration of the number of subcarriers and the size of constellation symbols in the case of mm-Wave communications, KNN was used in another study [138] to maximize the system throughput. The system was evaluated using four antenna configurations resulting in a uniform cylindrical array with the highest data rate.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

“…[ [133][134][135][136][137][138][139][140][141][142][143][144][145]154,157,162,165] ANN Optimization of dual-band antenna with desired return loss, Significant improvements in learning the modulation demodulation schemes for multipath channels in IoT applications.…”

Section: Remote Object Detection and Recognition Cnn Dcnnmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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“…This arrangement enables OFDM-IM to benefit from CS for further improving the trade-off between SE and EE [13,16]. Additionally, by introducing appropriate interleaving, the OFDM-CSIM is also capable of attaining an improved performance, when compared with the OFDM-IM [13,17].…”

mentioning

confidence: 99%

“…It is widely acknowledged that the time-varying nature of wireless channels is among the most challenging aspects of wireless system design [2]. To mitigate its effect, link adaptation was proposed as a powerful technique to optimize the SE [17]. Conventionally, link adaptation is achieved via adaptive modulation and coding (AMC) [18], which selects the appropriate modulation order and coding rate to maximize throughput, while maintaining a required reliability [18][19][20][21].…”

mentioning

confidence: 99%

Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

Liu

Zhang

et al. 2022

IEEE Trans. Veh. Technol.

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The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the k nearest neighbor (k-NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost.

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Machine Learning Assisted Adaptive Index Modulation for mmWave Communications

Cited by 20 publications

References 47 publications

The Evolution of Optical OFDM

The Evolution of Optical OFDM

Application of Machine Learning in Electromagnetics: Mini-Review

Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

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