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.

As modern power systems evolve to handle increasingly frequent challenges, such as weather anomalies and recurrent failures, innovative radio resource allocation solutions are essential to fortify microgrids against these challenges. In this letter, we investigate transmission power allocation for distributed energy resources (DERs) in a multi-microgrid system. Leveraging narrow band internet of things’ (NB-IoT) coverage enhancement (CE) feature, the deliberate control of CE radii at the base stations influences the weighted sum rate, thus necessitating the exploration of optimal CE radii. The inherent nonconvexity of the optimization problem is addressed by employing difference-of-convex techniques along with a heuristic procedure to determine the CE radii. Furthermore, to ensure reliability and robustness in practical implementations, we establish the algorithm’s rate of convergence using strong convexity. Our numerical simulations demonstrate the algorithm’s performance against relevant benchmarks, including the upper bound benchmark derived from the Lagrange error bound.

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.

Efficient renewable resource integration is a key pillar of the evolving smart grid vision to shift from fossil fuels. Microgrids (MGs), empowered by distributed energy resources (DERs), are a promising avenue toward this goal. In MG communications, robust radio resource management is essential, requiring mature wireless technologies that accommodate growing network size and diverse services. This work demonstrates the capabilities of coverage enhancement, which is a feature of the narrow-band Internet of things to support the performance of future massive MG networks. We formulate a problem to maximize the weighted sum-rate of DERs with different quality of service classes. Our objective is to optimally allocate power levels to the users and perform frequency scheduling. A three-step approach is proposed to solve the highly nonconvex problem, where difference-of-convex tools are invoked to address the power control subproblem. Next, a low-complexity distributed heuristic is used for sub-carrier scheduling, leveraging data-driven methods and mixed-integer nonlinear programming for network compression and coordination. The base stations’ coverage enhancement radii are optimized using a distributed multi-armed-bandit-based algorithm. Furthermore, numerical simulations reveal that our integrated solution not only outperforms benchmarks based on a genetic algorithm and round-robin scheduling but also performs comparably to solutions using pure mixed-integer nonlinear programming for optimal scheduling.

Wireless sensor networks (WSNs) in smart grid (SG) applications are greatly affected by the detrimental impact of bursty impulsive noise (IN) generated by various partial discharges from aging power equipments in Smart Grid’s power substations. Additionally, the deleterious impact of fading on the radio frequency (RF) links between the sensor nodes further deteriorates the performance. To alleviate the problem of fading on the RF links, in this paper, we propose to exploit reconfigurable intelligent surfaces (RISs). In addition, to deal with the bursty IN, we consider the maximal a posteriori (MAP) decoding of the received symbols using the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The performance of the considered system is evaluated by deriving a novel closed-form expression for the bit error rate (BER). Furthermore, an asymptotic BER analysis is presented in order to highlight the diversity order achieved by the considered RIS-aided system. Finally, extensive numerical results are provided to demonstrate the significant gain that can be achieved through the proposed RIS-aided architecture for WSNs.

Industrial Control Systems (ICSs) are cyber-physical systems that offer attractive targets to threat actors due to the scale of damages, both physical and cyber, that successful exploitation can cause. As such, ICSs often find themselves victims to reconnaissance campaigns - coordinated scanning activity that targets a wide subset of the Internet - that aim to discover vulnerable systems. As these campaigns likely scan broad netblocks of the Internet, some traffic is directed to network telescopes, which are routable, allocated, and unused IP space. In this paper, we explore the threat landscape of ICS devices by analyzing and investigating network telescope traffic. Our network traffic analysis tool takes darknet traffic and generates threat intelligence on scanning campaigns targeting ICSs in the form of campaign fragments, which we leverage in new ways to get more in-depth knowledge of the cybersecurity threats. We investigate the payloads of the identified campaigns using a custom Deep Packet Inspection (DPI) technique to dissect and analyze the packets. We found 13 distinct payload templates and deduced their purpose, and by extension the campaign goals. We use machine learning to classify the sources behind the campaigns and identify threat actors such as botnets, malicious attackers, or researchers, and establish a methodology to rank our campaigns to prioritize our analysis. To conduct our analysis of the threats targeting ICSs, we have leveraged 12.85 TB (330 days) of network traffic received by our observed darknet IP space. Combining these investigative threads, we provide a thorough overview of the threat landscape targeting ICS systems.

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%.

Inherent interfering signals generated by the underlying elements found in power substations have been known to span over consecutive noise samples, resulting in bursty interfering noise samples. In the impulsive noise environments, we elaborate a space-sensitive technique using multiple-input multiple-output (MIMO), which is particularly well suited in these usually very difficult situations. We assume the availability of channel state information (CSI) at the transmitter to achieve typical MIMO system gains in ad hoc mode. In this paper, we show that more than 10 dB gains are obtained with the most efficient system that we propose for achieving smart grid application requirements. On the one hand, the results obviously illustrate that the max-dmin precoder associated with the rank metric coding scheme is especially adapted to minimize the bit error rate (BER) when a maximum likelihood (ML) receiver is employed. On the other hand, it is shown that a novel node selection technique can reduce the required nodes transmission energies.

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.

Power substations generate a significant “bursty impulse noise” that might interfere with wireless technologies working in the vicinity of power equipment. Existing wireless systems are not designed for such an environment; we propose a Maximum a Posteriori (MAP) receiver designed with Markov-Gaussian models in order to mitigate the impact of impulsive noise in substations. We study and compare different noise models implemented in the receiver and we discuss the performance of the receiver based on the characteristics of the impulsive noise. Our proposed model can be used by a MAP receiver to offer optimum performances from low signal to noise ratio (SNR). When the communication is disturbed by impulsive noise measured in the field, the MAP receiver still offers better performance than when using other models, but mainly at higher SNR.

The installation of wireless technologies in power substations requires characterizing the impulsive noise produced by the high-voltage equipment. Substation impulsive noise might interfere with classic wireless communications and none of the existing models can reliably represent this noise in wide band. Previous studies have shown that impulsive noise is characterized by series of damped oscillations with the amplitude, the duration and the occurrence times of the impulses that are random. All these characteristics make this noise time-correlated and the partitioned Markov chain remains an efficient model that can ensure the correlation between the samples. In this study, we propose to design a partitioned Markov chain to generate an impulsive noise that is similar to the noise measured in existing substations, in time and frequency domains. We configure our Markov chain to produce the impulses with the damped oscillation effect, then, we determine the probability transition matrix and the distribution of each state of the Markov chain. Finally, we generate noise samples and we study the distribution of the impulsive noise characteristics. Our Markov chain model can replicate the correlation between the measured noise samples; also the distributions of the noise characteristics are similar in the simulations and the measurements.

The channel capacity and the channel statistics are both important performance metrics to consider for appropriate design of wireless systems. In tactical mobile ad hoc networks environment, the capacity and the second order statistics are investigated based on an optimized physical layer simulation tool which includes tactical scenarios generator and propagation modeler. The possible link data rate is stemmed from the average channel capacity analysis between all pair of nodes. And based on the minimum data rate requirement for typical services (web, audio and video streaming, VoIP, videoconference), connectivity graphs are constructed. Moreover, level cross rate (ICR) and average duration of fade (ADF) are simulated as function of double mobility degree and the results show how the double mobility affects the second order channel statistics and the error probability.