Wireless communication systems operating in industrial environments are often subject to bursty impulsive noise and frequency-selective fading that significantly degrade their performance. Under these conditions, conventional orthogonal frequency division multiplexing (OFDM) receivers struggle to maintain reliable communication. In this paper, we address these challenges by introducing a reliable receiver design consisting of two key components. First, we develop an adaptive memory-aware noise covariance estimation method using neural networks to dynamically predict the noise level for each received symbol. Second, we propose a channel state information (CSI) enhancement method using two-dimensional average pooling and interpolation (2DAPI) to smooth out inaccuracies in CSI caused by bursty impulsive noise. We further derive a theoretical bit error rate (BER) performance approximation and incorporate a lower bound benchmark for rigorous evaluation. The results of our extensive simulations demonstrate the extreme power efficiency of the proposed receiver, which requires less than 10% of the transmit power as compared to conventional OFDM receivers that use linear minimum mean square error (LMMSE) equalizers and least squares (LS) CSI estimation to achieve the same BER. In addition, performance analyses across various modulation schemes and fading models confirm the reliability and adaptability of our design in diverse operational environments.

With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model's identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.

In this paper, we consider a cell-free massive multiple input multiple output (CF-MMIMO) communication system, where users are supported by access points (AP) in conjunction with intelligent reflecting surfaces (IRS) mounted on unmanned aerial vehicles (UAVs). Although aerial IRS (aIRS) offers agile support for expanding network coverage in CF communication systems, the effective operation of such a complex network necessitates a channel state information (CSI) acquisition scheme that exhibits low run-time computational complexity. We propose an artificial intelligence (AI)-based approach to design and develop an efficient channel prediction scheme for CSI acquisition in the CF-MMIMO network supported by aIRS considered. Simulation results demonstrate the effectiveness of the proposed scheme in predicting channel gains across a wide range of signal-to-noise ratios (SNR) while maintaining low computational complexity during real-time operations.

The problem of relay selection is pivotal in the realm of cooperative communication. However, this issue has not been thoroughly examined, particularly when the background noise is assumed to possess an impulsive characteristic with consistent memory as observed in smart grid communications and some other wireless communication scenarios. In this paper, we investigate the impact of this specific type of noise on the performance of cooperative Wireless Sensor Networks (WSNs) with the Decode and Forward (DF) relaying scheme, considering Symbol-Error-Rate (SER) and battery power consumption fair-ness across all nodes as the performance metrics. We introduce two innovative relay selection methods that depend on noise state detection and the residual battery power of each relay. The first method encompasses the adaptation of the Max-Min criterion to this specific context, whereas the second employs Reinforcement Learning (RL) to surmount this challenge. Our empirical outcomes demonstrate that the impacts of bursty impulsive noise on the SER performance can be effectively mitigated and that a balance in battery power consumption among all nodes can be established using the proposed methods.

In this paper, we derive accurate outage probability and bit error rate expressions for a high altitude platform station (HAPS) assisted terrestrial communication system. In particular, HAPS is deployed as a relay node to assist two ground stations for data transmission. Each ground station-to-HAPS communication link works on a hybrid radio frequency (RF) and free space optics (FSO) mode. Selection combining is performed at the HAPS and the destination ground station to select either RF or FSO link based on the instantaneous channel signal-to-noise ratio (SNR). The derived expressions provide insights on system design and assist analyzing HAPS-terrestrial integrated network supported by hybrid RF/FSO system. The accuracy of the derived outage probability and bit error rate expressions are validated with extensive computer simulations.

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. To overcome the detrimental effects of such impulsive interference, knowledge of impulsive noise parameters is generally required by the available mitigation techniques. This work considers a machine learning perspective for the estimation of the impulsive noise parameters in communication systems under the influence of Middleton class-A noise. Precisely, we consider a deep learning approach and design a deep neural network (DNN) that classifies a set of received symbols according to the parameters of the impulsive noise affecting them. It is sown that the classification accuracy greatly depends on the number of symbols fed into the neural network as well as the number of considered states in the classification, where the proposed approach can reach a testing accuracy of more than 99%.

In this study, the authors investigate the physical layer security performance over Rayleigh fading channels in the presence of impulsive noise, as encountered, for instance in smart grid environments. For this scheme, secrecy performance metrics are considered with and without destination assisted jamming at the eavesdropper's side. Specifically, they derive analytical expressions for the secrecy outage probability (SOP), at the legitimate receiver. Finally, numerical results are provided to verify the accuracy of the authors' derivations. From the obtained results, it is verified that the SOP, without destination assisted jamming, is flooring at high signal‐to‐noise‐ratio values and that it can be significantly improved with the use of jamming.

We address the distributed estimation of a scalar Gaussian source in wireless sensor networks. The sensor nodes transmit their noisy observations, using the amplify-and-forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario.

In this paper, we consider the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme over bursty impulsive noise channel. As compared to existing literature, here, we generalize the performance analysis to the multi-relay scenario with and without considering error propagation from the relays. For this scheme, we evaluate the bit error rate (BER) performance in the presence of Rayleigh fading with a maximum a posteriori (MAP) receiver. From the obtained results it is seen that, similar to single relay scheme, the proposed MAP receiver attains the lower bound derived for multi-relay DF CR scheme also, and performs significantly better than the conventional schemes developed for additive white Gaussian noise (AWGN) channel and memoryless impulsive noise channel. Moreover, the performance improvement of optimal MAP receiver over the memoryless receivers is substantial with increasing the number of relays.

Relay selection among available relays is crucial in improving the performance of cooperative relaying. In this paper, we address the relay selection for a multi-relay decode-and-forward (DF) cooperative relaying scheme over channels impaired by bursty impulsive noise in the presence of Rayleigh fading. For this scheme, we propose a novel best relay selection technique which is based on both the channel state information of source-relay and relay-destination links and the state of the impulsive noise that affect those links. Then, we provide a end-to-end bit error rate (BER) expression for the proposed scenario using BPSK modulation. Finally, the accuracy of the provided analytical results and the effectiveness of the proposed best relay selection algorithm under different network topology is discussed. It is shown that, by using the bursty impulsive noise characteristics, the proposed relay selection algorithm performs significantly better than the conventional relay selection algorithm which is optimized for additive white Gaussian noise environment.

In this paper, we consider the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme over channels impaired by bursty impulsive noise. Although the Middleton class-A model and the Bernoulli-Gaussian model give good results to generate a sample distribution of impulsive noise, they fail in replicating the bursty behavior of impulsive noise, as encountered for instance within power substations. To deal with that, we adopt a two-state Markov-Gaussian process for the noise distribution. For this channel, we evaluate the bit error rate performance of direct transmission (DT) and a DF relaying scheme using M-ary phase shift keying (M-PSK) modulation in the presence of Rayleigh fading with a maximum a posteriori (MAP) receiver. From the obtained results, it is seen that the DF CR scheme in bursty impulsive noise channel still achieves the space diversity and performs significantly better than DT under the same power consumption. Moreover, the proposed MAP receiver attains the lower bound derived for DF CR scheme and leads to large performance gains compared with the conventional receiving criteria, which were optimized for additive white Gaussian noise channel and memoryless impulsive noise channel.