Block-Structured Deep Learning-Based OFDM Channel Equalization

Logins, Alvis; He, Jiale; Paramonov, Kirill

doi:10.1109/lcomm.2021.3133018

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Logins, Alvis, Jiale He, and Kirill Paramonov. [16] demonstrated that OFDM signal equalization had been experimentally investigated. Utilizing physical experimental equipment, they compared two different types of power amplifiers (PA) on the transmit side and three different types of trans-impedance amplifiers (TIA) on the receive side.…”

Section: Related Workmentioning

confidence: 99%

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A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

S Hanoon,

F Khazal,

M Jamel

2024

ejuow

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Channel equalization is crucial to the efficiency of wireless network systems. To improve communication reliability and decrease computing complexity, 5G networks have made great progress with the help of deep learning (DL). When applied to 5G and future networks, deep learning has been proven to increase system performance while decreasing computational complexity. ZF is often used to acquire a channel equalizer because of its inexpensive cost and lack of statistical expertise; nonetheless, ZF has a large equalizer error. Because of the limitations of the Minimum Mean Square Error (MMSE) method, deep learning models provide a more convenient solution for solving the channel equalizer issue. Since deep learning may give a better performance-complexity trade-off, it can be used to enhance MMSE and ZF channel equalizers. Its generalization and resilience also make it an attractive tool for use in this context. To address the shortcomings of the ZF and MMSE equalizers, this thesis focuses on developing a DL-based channel equalizer. An Orthogonal Frequency-Division Multiplexing (OFDM)-based single-input-single-output (SISO) system is used to measure the DL-based equalizer's efficacy. Simulation results show that the DL-based channel equalizer can achieve at least a 3 dB gain in SNR over the MMSE equalizer for a SER = 10-3 in low and high selective channel models, respectively, validating the performance of the benchmarked equalizer using different frequency selectivity levels. Furthermore, the DL-based equalizer results in a drastic decrease in computing complexity in contrast to the ZF and MMSE equalization techniques.

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Section: Related Workmentioning

confidence: 99%

“…Validation and Testing: After the network has been trained, its generalization performance is evaluated using test and validation datasets. Overfitting, when a model does well on training data but poorly on new data, may be combated through regularization methods and validation tests [16].…”

Section: Training Processmentioning

confidence: 99%

A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

S Hanoon,

F Khazal,

M Jamel

2024

ejuow

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“…− The majority of the representative works in sequential W/H and memoryless equalization models have captured linear MEs, yielding stable learning convergence while inherently obviating the suitability for real-time systems with limited computational resources. However, the recent advancements in such models, the move towards developing the Wiener structure for online cancellation of TRX self-interference, and the flexible combination of ANN with W/H models paved the way for the recursive coefficient adaption in full-duplex strategy to be an enabling linearization method for both contemporary 5G and future communications [107], [108]. − As highlighted in previous subsections, to enable the • Difficulties regarding the curse of input signal dimensionality in large bandwidths remain a challenge stability of computing algorithm relaying, some advanced pruning algorithms should be adopted to reduce the basis function orthogonalization to a tolerable level.…”

Section: • Critical Analysismentioning

confidence: 99%

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Haider

You

et al. 2022

IEEE Commun. Surv. Tutorials

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The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver's (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.

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“…In [5], the authors only considered OFDM signals with slight non-linear distortion, and in [63], the authors considered the non-linear distortion caused by the terminal power amplifier. In [64], the authors propose a CNN-based equalizer for The contribution of this chapter can be summarized as follows. We design a 1D-TRNet based receiver for the non-linearly distorted OFDM system.…”

Section: Discussionmentioning

confidence: 99%

Applications of deep learning for physical layer of wireless communication systems

Xie

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The objective of the thesis is to exploit the recent advances in deep learningbased techniques for wireless communication systems. The proposed methods aim to improve the performance and robustness of communication systems in different scenarios. The first research work proposes a deep learning-based receiver for non-orthogonal multiple access (NOMA) joint signal detection. The receiver combines channel estimation, equalization, and demodulation into an end-to-end process, which results in improved performance and robustness compared to traditional methods. The proposed method is tested with a tapped-delay line (TDL) channel model, which is commonly used in fifth generation (5G) communication systems. The second research work presents a deep learning-based receiver for orthogonal frequency-division multiplexing (OFDM) systems. The proposed receiver, named one-dimensional transmit and recovery network (1D-TRNet), is designed to be robust to non-linear clipping distortion. The 1D-TRNet receiver outperforms traditional OFDM receivers and state-of-the-art generalized approximate message passing (GAMP) receivers in terms of bit-error rate (BER) performance. The third research work proposes a deep learning-based 5G receiver, named communication transformer network (Comm-Trans Net), which is designed to be robust for different sub-types of TDL channels. The proposed receiver uses an attention mechanism to compensate for the multi-path fading effect of different OFDM subcarriers. The proposed positional encoding method for each OFDM subcarrier offsets the deep fading effect, resulting in improved performance compared to traditional MMSE channel estimation with GAMP receivers and state-of-the-art deep learning-based receivers. The fourth research work introduces a novel communication system called the diffusion model-based generative semantic communication (DM-GSC) xiii xiv system. The system utilizes the diffusion model and semantic embedding to enable efficient and robust communication in wireless networks. The proposed DM-GSC system shows promising results in robustness for different channel conditions. Overall, the thesis demonstrates the potential of deep learning-based techniques in improving the performance and robustness of wireless communication systems.The proposed methods have the potential to enhance the quality of service and enable novel communication scenarios in the future.

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Block-Structured Deep Learning-Based OFDM Channel Equalization

Cited by 8 publications

References 23 publications

A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

A Deep Learning Approach to Evaluating SISO-OFDM Channel Equalization

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Applications of deep learning for physical layer of wireless communication systems

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