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 use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the wide availability of cellular infrastructure.Estimating the number of devices (network load) is vital for efficient allocation of the available resources, especially for managing the random access channel (RACH) of the network. This paper considers a two-priority RACH and proposes two network load estimators: a maximum likelihood (ML) estimator and a reduced complexity (RCML) variant. The estimators are based on a novel model of the random access behavior of the devices coupled with a flexible analytical framework to calculate the involved probabilities. Monte Carlo simulations demonstrate the accuracy of the proposed estimators for different network configurations. Results depict increased estimation accuracy using non-uniform preamble selection probabilities compared to the common uniform probabilities at no extra computational cost.

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 article, we address the problem of delay-causing time synchronization (DC-TS) attacks against wide-area damping controllers (WADCs). To enhance smart grid stability against such threats, we present a realistic and secure design procedure for WADCs. To this end, we follow a methodology that utilizes the state-space model of the entire grid to design a periodic observer-based event-triggered controller by formulating the problem as a set of linear matrix inequalities, solved by the looped-Lyapunov functional (LLF) technique. The event-triggered scheme applied in this design procedure improves communication efficiency. Plus, the periodic sampled-data approach makes the design better suited to the operational reality of digital systems and their constraints. As such, the contributions of this work include developing an event-triggered mechanism to reduce unnecessary data transmissions, applying LLF for less conservative stability analysis, and utilizing the Guardian map theorem and Rekasius substitution to assess the WADCs resilience under DC-TS attacks. We conducted extensive simulations on the Kundur two-area and New England 39-bus systems to validate our approach. These simulations, along with comparisons to existing methods and tests on the RT-Lab real-time platform, demonstrate the superior performance of our WADC in maintaining grid stability and improving damping under considered attacks.

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.