Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning

Lv, Xinrong; Li, Youming; Wu, Yongqing; Wang, Xiaoli; Liang, Hui

doi:10.1109/access.2019.2920724

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…VI methods approximate the exact Bayesian posterior distribution with a tractable variational density [26]- [29], while MC techniques obtain approximate samples from the Bayesian posterior distribution [30]- [32]. Among other applications to communications systems, Bayesian learning was studied for network allocation in [33]- [35], for massive MIMO detection in [36]- [38], for channel estimation in [39]- [41], for user identification in [42], and for multi user detection in [43], [44]. Extensions to Bayesian meta-learning have been investigated in [20].…”

Section: Related Workmentioning

confidence: 99%

Calibrating AI Models for Wireless Communications via Conformal Prediction

Cohen,

Park,

Simeone

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A wellcalibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence levels to decisions that are likely to be correct, and low confidence levels to decisions that are likely to be erroneous. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. Conformal prediction transforms probabilistic predictors into set predictors that are guaranteed to contain the correct answer with a probability chosen by the designer. Such formal calibration guarantees hold irrespective of the true, unknown, distribution underlying the generation of the variables of interest, and can be defined in terms of ensemble or time-averaged probabilities. In this paper, conformal prediction is applied for the first time to the design of AI for communication systems in conjunction with both frequentist and Bayesian learning, focusing on the key tasks of demodulation, modulation classification, and channel prediction. For demodulation and modulation classification, we apply both validation-based and cross-validation-based conformal prediction; while we investigate the use of online conformal prediction for channel prediction. For each task, we evaluate the probability that the set predictor contains the true output, validating the theoretical coverage guarantees of conformal prediction, as well as the informativeness of the predictor via the average predicted set size.

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Section: Related Workmentioning

confidence: 99%

Calibrating AI Models for Wireless Communications via Conformal Prediction

Cohen,

Park,

Simeone

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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“…This paper introduces the use of Bayesian meta-learning to enable both adaptation and monitoring for the tasks of demodulation and equalization. Unlike prior works that considered either frequentist meta-learning [6], [19], [20], [21], [22], [23], [24], [25], [26] or Bayesian learning [40], [41], [42], [43], the proposed Bayesian meta-learning methodology enables both resource-efficient adaptation and a reliable quantification of uncertainty. To further improve the efficiency of Bayesian metalearning we propose the use of active meta-learning, which reduces the number of required meta-training data from previously received frames.…”

Section: Contributionsmentioning

confidence: 99%

Bayesian Active Meta-Learning for Reliable and Efficient AI-Based Demodulation

Cohen¹,

Park²,

Simeone³

et al. 2022

IEEE Trans. Signal Process.

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Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in communication networks are adaptation and monitoring. Adaptation refers to the need to adjust the operation of an AI module depending on the current conditions; while monitoring requires measures of the reliability of an AI module's decisions. Classical frequentist learning methods for the design of AI modules fall short on both counts of adaptation and monitoring, catering to one-off training and providing overconfident decisions. This paper proposes a solution to address both challenges by integrating meta-learning with Bayesian learning. As a specific use case, the problems of demodulation and equalization over a fading channel based on the availability of few pilots are studied. Meta-learning processes pilot information from multiple frames in order to extract useful shared properties of effective demodulators across frames. The resulting trained demodulators are demonstrated, via experiments, to offer better calibrated soft decisions, at the computational cost of running an ensemble of networks at run time. The capacity to quantify uncertainty in the model parameter space is further leveraged by extending Bayesian meta-learning to an active setting. In it, the designer can select in a sequential fashion channel conditions under which to generate data for meta-learning from a channel simulator. Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.

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“…In [11], a novel joint channel impulse response estimation and impulsive noise mitigation algorithm based on compressed sensing theory is proposed. In this algorithm, both the channel impulse response and the impulsive noise are treated as a joint sparse vector.…”

Section: A R Fereydouni a Charmin* H Vahdati H Nasir Aghdammentioning

confidence: 99%

“…The basis of the wavelet transform is based on two basic functions shown in Equation (11). In which they are called the mother wavelet function and the basic scale function, respectively [18].…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

Channel Estimation and Carrier Frequency Offset Compensation in Orthogonal Frequency Division Multiplexing System Using Adaptive Filters in Wavelet Transform Domain

Fereydouni

Charmin

Vahdaty

et al. 2020

IJE

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In this paper, a combination of channel, receiver frequency-dependent IQ imbalance, and carrier frequency offset estimation under short cyclic prefix length is considered in orthogonal frequency division multiplexing system. An adaptive algorithm based on the set-membership filtering algorithm is used to compensate for these impairments. In short CP length, per-tone equalization structure is used to avoid inter-symbol interference. Due to CFO impairment and IQ imbalance in the receiver, we will expand the PTEQ structure to a two-branch structure. This structure has high computational complexity, so using the set-membership filtering idea with variable step size while reducing the average computation of the system can also increase the convergence speed of the estimates. On the other hand, applying wavelet transform on each branch of this structure before applying adaptive filters will increase the estimation speed. The proposed algorithm will be named SMF-WP-NLMS-PTEQ. The results of the simulations show better performance than the usual adaptive algorithms. Besides, estimation and compensation of channel effects, receiver IQ imbalance and carrier frequency offset under short CP can be easily accomplished by this algorithm.

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Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning

Cited by 15 publications

References 42 publications

Calibrating AI Models for Wireless Communications via Conformal Prediction

Calibrating AI Models for Wireless Communications via Conformal Prediction

Bayesian Active Meta-Learning for Reliable and Efficient AI-Based Demodulation

Channel Estimation and Carrier Frequency Offset Compensation in Orthogonal Frequency Division Multiplexing System Using Adaptive Filters in Wavelet Transform Domain

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