Antenna Design For Noncoherent Massive MIMO Systems

Bucher, Stephan; Ragab, Ahmed N.; Yammine, George; Fischer, Robert F. H.; Waldschmidt, Christian

doi:10.1109/iswcs.2018.8491104

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The orthogonal frequency-division multiplexing (OFDM) technology has been widely adopted for wireless communications, including Wi-Fi and cellular systems [1], [2]. However, the performance of OFDM systems is sensitive to the front-end nonidealities [3], [4], [5], which may cause problems such as signal nonlinearity, clipping, in-phase/quadraturephase (IQ) imbalance, phase noise (PN), carrier frequency offset (CFO), and sampling clock offset (SCO). Algorithms have been proposed to compensate individual hardware impairments such as CFO [6]- [8], IQ imbalance [9], [10], phase noise [11]- [13], clipping [14], [15], and SCO [16], and deep learning-based approaches have also been proposed to tackle clipping [17], phase noise [18], and CFO [19] issues.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based End-to-End Design for OFDM Systems With Hardware Impairments

Wu,

Lin,

et al. 2023

IEEE Open J. Commun. Soc.

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Orthogonal frequency-division multiplexing (OFDM) is a key technology for cellular and Wi-Fi systems, but its performance may be degraded by hardware impairments. Existing works focus mostly on single hardware impairment in OFDM systems, without considering the joint effect of hardware impairments on the entire system. In this paper, hardware impairments including nonlinear power amplification, clipping, in-phase/quadrature-phase (IQ) imbalance, phase noise, carrier frequency offset, and sampling clock offset in OFDM systems are simultaneously considered. We propose end-to-end deep learning-based designs, which jointly optimize transmitter and receiver, to effectively mitigate the performance loss due to hardware impairments. For singleantenna systems and 2×2 multiple-input and multiple-output (MIMO) systems, the proposed design featuring the dense layer neural network (DLNN) significantly outperforms traditional impairmentmitigating methods under both the additive white Gaussian noise (AWGN) channel and the Rayleigh fading channel. Meanwhile, the complexity of the proposed scheme is six times smaller. For 2×4 MIMO systems, the proposed design featuring the residual dense convolution dense neural network (ResNet-DCDNN) outperforms the traditional methods by a large margin. Additionally, transfer learning is applied to effectively address the issue of time-varying impairment levels.INDEX TERMS Deep learning, orthogonal frequency-division multiplexing (OFDM), hardware impairments, end-to-end design, multiple antennas, transfer learning.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based End-to-End Design for OFDM Systems With Hardware Impairments

Wu,

Lin,

et al. 2023

IEEE Open J. Commun. Soc.

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“…In order to provide favorable PSPs, the BS has to be physically large and the users have to be located in the near-field of the receive array as shown in [14]. A better user separability can be achieved by employing directional antennas at the BS [15].…”

Section: Introductionmentioning

confidence: 99%

A Noncoherent Massive MIMO System Employing Beamspace Techniques

Bucher¹,

Yammine

Fischer

et al. 2019

IEEE Trans. Veh. Technol.

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Noncoherent detection schemes are an appealing and low-complexity alternative in multiuser massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multiuser detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability.

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Model of spatiotemporal coherent summation of ultra-wideband chaotic radio pulses formed by independent emitters

Zubkov,

Kuzmin,

Efremova

2024

Radiotehnika i èlektronika

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A model of spatiotemporal summation of ultra-wideband chaotic radio pulses is proposed. The feasibility of using this model for analyzing scenarios of time-coherent radiation from independent sources of ultra-wideband chaotic radio pulses in wireless ultra-wideband systems is substantiated. Two scenarios are considered: where the distance between receiving point and emitters is much greater than the typical size of a group of emitters and where the receiving point is located between the emitters. Distributions of the total pulse energy in space for these scenarios are obtained.

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Antenna Design For Noncoherent Massive MIMO Systems

Cited by 4 publications

References 11 publications

Deep Learning-Based End-to-End Design for OFDM Systems With Hardware Impairments

Deep Learning-Based End-to-End Design for OFDM Systems With Hardware Impairments

A Noncoherent Massive MIMO System Employing Beamspace Techniques

Model of spatiotemporal coherent summation of ultra-wideband chaotic radio pulses formed by independent emitters

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