Beam Index Modulation Wireless Communication With Analog Beamforming

Ding, Yuan; Fusco, Vincent; Shitvov, Alexey; Xiao, Yao; Li, Hailin

doi:10.1109/tvt.2018.2819728

Cited by 53 publications

(30 citation statements)

References 40 publications

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“…Within the context of unconventional wireless communication paradigms, there has been a growing interest in controlling the propagation environment in order to increase the QoS and/or spectral efficiency. IM-based emerging schemes such as MBM [13]- [15], spatial scattering modulation [11], and beam IM [12], use the variations in the signatures of received signals by exploiting reconfigurable antennas or scatterers to transmit additional information bits in rich scattering environments [9]. Similarly, SM schemes [4]- [6], [8], [9] create a new and extended signal constellation by exploiting the indices of the available transmit antennas of MIMO systems, still benefiting from the distinguishable propagation characteristics of different transmit antennas in rich scattering environments.…”

Section: Historical Perspective and State-of-the-art Solutionsmentioning

confidence: 99%

“…SM maps information bits onto transmit antenna indices by exploiting different fading realizations of multiple-input multiple-output (MIMO) antennas. Taking SM one step further, spatial scattering modulation [11] and beam IM [12] exploit the indices of the scatterers available in the environment to convey information. Finally, media-based modulation (MBM) utilizes reconfigurable antennas [13]- [15] by encoding the information bits onto multiple distinguishable radiation patterns [16]- [18].…”

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confidence: 99%

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Wireless Communications Through Reconfigurable Intelligent Surfaces

et al. 2019

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The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.INDEX TERMS 6G, large intelligent surfaces, meta-surfaces, reconfigurable intelligent surfaces, smart reflect-arrays, software-defined surfaces, wireless communications, wireless networks. I. INTRODUCTIONAccording to the February 2019 report of Cisco [2], by the year of 2022, the number of networked devices and connections will reach up to 28.5 billions, and 12.3 billions of them will consist of mobile-ready devices and connections.The associate editor coordinating the review of this manuscript and approving it for publication was Feng Li.

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Section: Historical Perspective and State-of-the-art Solutionsmentioning

confidence: 99%

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confidence: 99%

Wireless Communications Through Reconfigurable Intelligent Surfaces

et al. 2019

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“…This approach suffers from reduced array gain because it always uses a subarray. single-user scenario, [7] proposed beamspace-based approaches for spatial path index modulation (SPIM), which modulates the indices of the spatial paths to create different spatial patterns. The use of beamspace model is further exploited in [8] by employing lens arrays at both transmitter and receiver to improve the bit-error-rate (BER).…”

Section: Introductionmentioning

confidence: 99%

“…A joint design for analog and baseband precoders for SPIM is performed in [11] by implementing zero-forcing baseband precoding and selecting the steering vectors as analog beamformer candidates. Similar to [11], [6,7] also design the analog precoders with a predefined codebook of steering vectors, which entails a beam training task prior to the precoder design. Most of the aforementioned works investigate the single-user scenario.…”

Section: Introductionmentioning

confidence: 99%

Federated Dropout Learning for Hybrid Beamforming with Spatial Path Index Modulation in Multi-User Mmwave-Mimo Systems

Elbir

Coleri

Mishra

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.

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“…This design is capable of increasing the data rate, since the information is carried by both the classic Mary alphabet and by the DM index as well as by the antenna index combination. In mmWave communications, where the channel supports a few clusters of rays, the data rate of the MS-STSK design can be further enhanced by coupling it with the concept of beam index modulation (BIM) [17]. In the BIM aided transmission, information is implicitly conveyed by the index of the beams in addition to the classic M-ary constellation.…”

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Deep Learning Assisted Detection for Index Modulation Aided mmWave Systems

et al. 2020

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In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK's conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.

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Beam Index Modulation Wireless Communication With Analog Beamforming

Cited by 53 publications

References 40 publications

Wireless Communications Through Reconfigurable Intelligent Surfaces

Wireless Communications Through Reconfigurable Intelligent Surfaces

Federated Dropout Learning for Hybrid Beamforming with Spatial Path Index Modulation in Multi-User Mmwave-Mimo Systems

Deep Learning Assisted Detection for Index Modulation Aided mmWave Systems

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