IQ Symbols Processing Schemes With LSTMs in OFDM System

Li, Jun; Xin, Tongliang; He, Biao; Li, Wenxin

doi:10.1109/access.2022.3170410

Cited by 9 publications

(6 citation statements)

References 19 publications

(28 reference statements)

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“…D are the noise vector at the relay and destination terminals, respectively. Note that we add superscripts 1 and 2 to the signal received at the destination to highlight that the signal received at the first time slot is different from that at the second time slot, as described in (10) and (13). Given that the relay only receives in a single time slot, we can omit the superscript from the signal's representation in (9).…”

Section: System Descriptionmentioning

confidence: 99%

“…OFDM systems are more susceptible to the IQ discrepancy than singlecarrier systems because of the spectral overlap between the subcarriers. As a consequence of the IQ mismatch issue, sub-carrier orthogonality is lost, which in turn causes inter-carrier interference and a significant drop in performance [12], [13]. There is a wealth of literature on the topic of the impact, estimate, and correction of IQ mismatch for non-cooperative OFDM transmissions, see e.g., [14] and references therein.…”

Section: Introductionmentioning

confidence: 99%

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Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

Marey

Mostafa

2023

IEEE Access

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Amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM) transmissions encounter a significant difficulty in the form of in-phase and quadrature phase (IQ) mismatch. Previous reports on this problem have solely been discussed in the context of uncoded transmissions. In addition, in these precedent studies, IQ equalization must be conducted following the estimation stage for accurate detection of data symbols. This research delves into the issue of the IQ mismatch between transmission and reception in AF-OFDM systems in the context of channel coding. We design a magnificent code-aided approach to predict the overall channel impulse responses (CIRs), which encompass the actual CIRs and IQ mismatch originating at the source, relay, and destination. Instead of using a collection of algorithms, the proposed approach can be utilized to estimate nine parameters simultaneously. Due to the impractical nature of the precise maximum-likelihood (ML) strategy to this situation, we instead utilize an expectation-maximization (EM) process as a low complexity strategy to predict the parameters under consideration. The suggested estimation approach uses an iterative process to improve predictions exploiting the a priori knowledge gained from the soft information supplied by the channel decoder. In addition, we demonstrate how to carry out data detection by making use of the estimated parameters. The simulation results verify the effectiveness of the proposed estimator and detector for usage in real-world settings, with superiority over the conventional ones.

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Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

Marey

Mostafa

2023

IEEE Access

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“…However, most of these techniques have shortcomings, such as poor system performance (bit error rate [BER]) and/or computational complexity. Well-known examples are distortionless schemes (e.g., SLM and PTS families) [2][3][4][5][6][7], clipping and filtering schemes [8,9], tone injection (TI) [10], active constellation extension (ACE) [11], interleaving [12], encoding family schemes [13], and artificial intelligent families [13][14][15][16][17][18]. Unfortunately, none of these solutions can guarantee the achievement of all standards, that is, acceptable levels of PAPR, BER performance, and computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A conditionally applied neural network algorithm for PAPR reduction without the use of a recovery process

Eldukhri,

Al‐Rayif

2023

ETRI Journal

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This study proposes a novel, conditionally applied neural network technique to reduce the overall peak‐to‐average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) system while maintaining an acceptable bit error rate (BER) level. The main purpose of the proposed scheme is to adjust only those subcarriers whose peaks exceed a given threshold. In this respect, the developed C‐ANN algorithm suppresses only the peaks of the targeted subcarriers by slightly shifting the locations of their corresponding frequency samples without affecting their phase orientations. In turn, this achieves a reasonable system performance by sustaining a tolerable BER. For practical reasons and to cover a wide range of application scenarios, the threshold for the subcarrier peaks was chosen to be proportional to the saturation level of the nonlinear power amplifier used to pass the generated OFDM blocks. Consequently, the optimal values of the factor controlling the peak threshold were obtained that satisfy both reasonable PAPR reduction and acceptable BER levels. Furthermore, the proposed system does not require a recovery process at the receiver, thus making the computational process less complex. The simulation results show that the proposed system model performed satisfactorily, attaining both low PAPR and BER for specific application settings using comparatively fewer computations.

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“…DeseNet takes multiple modules as one system for joint optimization, and its BER outperforms block-by-block receivers. A signal detection scheme based on LSTM was proposed in [28]. The authors utilized an RNN with the BiLSTM architecture for signal detection in [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

Zhang

Wang

et al. 2022

JMSE

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Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

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IQ Symbols Processing Schemes With LSTMs in OFDM System

Cited by 9 publications

References 19 publications

Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

Coded Assisted Transmit and Receive IQ Mismatch Compensation With Channel Estimation for AF Cooperative OFDM Systems

A conditionally applied neural network algorithm for PAPR reduction without the use of a recovery process

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

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