Reza Barazideh

Emerging wireless services with extremely high data rate requirements, such as real-time extended reality applications, mandate novel solutions to further increase the capacity of future wireless networks. In this regard, leveraging large available bandwidth at terahertz frequency bands is seen as a key enabler. To overcome the large propagation loss at these very high frequencies, it is inevitable to manage transmissions over highly directional links. However, uncoordinated directional transmissions by a large number of users can cause substantial interference in terahertz networks. While such interference will be received over short random time intervals, the received power can be large. In this work, a new framework based on reinforcement learning is proposed that uses an adaptive multi-thresholding strategy to efficiently detect and mitigate the intermittent interference from directional links in the time domain. To find the optimal thresholds, the problem is formulated as a multidimensional multi-armed bandit system. Then, an algorithm is proposed that allows the receiver to learn the optimal thresholds with very low complexity. Another key advantage of the proposed approach is that it does not rely on any prior knowledge about the interference statistics, and hence, it is suitable for interference mitigation in dynamic scenarios. Simulation results confirm the superior bit-error-rate performance of the proposed method compared with two traditional time-domain interference mitigation approaches.

Impulsive noise (IN) and Doppler shift can significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM)-based underwater acoustic (UWA) communication systems. In this paper, we propose a receiver structure that deals efficiently with both these channel impairments in a coded OFDM-based UWA system. First, an analog nonlinear preprocessor (ANP) is proposed to efficiently detect and mitigate IN in an analog domain. The proposed ANP exhibits intermittent nonlinearity when there is impulsivity. Next, the impact of IN on a two-step Doppler shift compensation approach is quantified. Specifically, the ability of the ANP to improve the robustness of Doppler shift compensation in the presence of IN is highlighted. The performance improvement of the proposed receiver is due to the fact that unlike the other nonlinear methods, the ANP is implemented in the analog domain where the outliers are still broadband and distinguishable. The simulation results also demonstrate the superior bit-error-rate (BER) performance of our approach relative to classic approaches that use blanking and/or clipping for IN mitigation.

Impulsive noise is a major impediment to orthogonal frequency-division multiplexing (OFDM) based underwater acoustic (UWA) communications. In this work, we evaluate the performance of a memoryless analog nonlinear preprocessor (MANP) that is used to mitigate outliers. The proposed MANP exhibits intermittent nonlinearity only in the presence of the impulsive noise and suppresses the power of outliers based on their amplitudes. Since the outliers are distinguishable in the analog domain prior to anti-aliasing filtering, the MANP outperforms its digital counterparts in all scenarios. Experimental results using data collected in an under-ice environment, demonstrate the superior BER performance of our approach relative to classical nonlinear approaches such as blanking and clipping.

Efficient removal of impulsive noise (IN) from received signal is essential in many communication applications. In this paper, we propose a two stage IN mitigation approach for orthogonal frequency-division multiplexing (OFDM)-based communication systems. In the first stage, a deep neural network (DNN) is used to detect the instances of impulsivity. Then, the detected IN is blanked in the suppression stage to alleviate the harmful effects of outliers. Simulation results demonstrate the superior bit error rate (BER) performance of this approach relative to classic approaches such as blanking and clipping that use threshold to detect the IN. We demonstrate the robustness of the DNN-based approach under (i) mismatch between IN models considered for training and testing, and (ii) bursty impulsive environment when the receiver is empowered with interleaving techniques.

It is well known that the performance of OFDM-based Powerline Communication (PLC) systems is impacted by impulsive noise. In this work, we propose a practical blind adaptive analog nonlinear filter to efficiently detect and mitigate impulsive noise. Specially, we design an Adaptive Canonical Differential Limiter (ACDL) which is constructed from a Clipped Mean Tracking Filter (CMTF) and Quartile Tracking Filters (QTFs). The QTFs help to determine a real-time range that excludes outliers. This range is fed into the CMTF which is responsible for mitigating impulsive noise. The CMTF is a nonlinear analog filter and its nonlinearity is controlled by the aforementioned range. Proper selection of this range ensures the improvement of the desired signal quality in impulsive environment. It is important to note that the proposed ACDL behaves like a linear filter in case of no impulsive noise. In this context, the traditional matched filter construction is modified to ensure distortionless processing of the desired signal. The performance improvement of the proposed ACDL is due to the fact that unlike other nonlinear methods, the ACDL is implemented in the analog domain where the outliers are still broadband and distinguishable. Simulation results in PRIME (OFDM-based narrowband PLC system) demonstrate the superior BER performance of ACDL relative to other nonlinear approaches such as blanking and clipping in impulsive noise environments.

Asynchronous and cyclostationary impulsive noise can severely impact the bit-error-rate (BER) of OFDM-based powerline communication systems. In this paper, we analyze an adaptive nonlinear analog front end filter that mitigates various types of impulsive noise without detrimental effects such as self-interference and out-of-band power leakage caused by other nonlinear approaches like clipping and blanking. Our proposed Adaptive Nonlinear Differential Limiter (ANDL) is constructed from a linear analog filter by applying a feedback-based nonlinearity, controlled by a single resolution parameter. We present a simple practical method to find the value of this resolution parameter that ensures the mitigation of impulsive without impacting the desired OFDM signal. Unlike many prior approaches for impulsive noise mitigation that assume a statistical noise model, ANDL is blind to the exact nature of the noise distribution, and is designed to be fully compatible with existing linear front end filters. We demonstrate the potency of ANDL by simulating the OFDM-based narrowband PLC compliant with the IEEE standards. We show that the proposed ANDL outperforms other approaches in reducing the BER in impulsive noise environments.