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.

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.

In this article, we propose an index modulation system suitable for optical communications, where the intensity of the light is modulated along the time, space and frequency dimensions, building a Frequency-Hopping Spatial Multi-Pulse Position Modulation (FH-SMPPM). We analyze its performance from the point of view of its efficiency in power and spectrum, and its implementation complexity and required receiver latency. We analyze its error probability in the case of the non-turbulent free-space optical (FSO) channel. We derive formulas for the average symbol and bit error probabilities, and the simulation results show that they are tight enough for a wide range of signal-to-noise ratios and system parameters. We compare FH-SMPPM with other proposed index modulated systems of the same nature, and we highlight its distinctive advantages, like the flexibility to build an appropriate waveform under different constraints and objectives. Moreover, FH-SMPPM shows to be better performing in BER/SER and/or offer advantages in efficiency with respect to those alternatives, thus offering the possibility to be adopted for a variety of contexts.

In this work, we consider a sparse code multiple access uplink system, where J users simultaneously transmit data over K subcarriers, such that J > K, with a constraint on the power transmitted by each user. To jointly optimize the subcarrier assignment and the transmitted power per subcarrier, two new iterative algorithms are proposed, the first one aims to maximize the sum-rate (Max-SR) of the network, while the second aims to maximize the fairness (Max-Min). In both cases, the optimization problem is of the mixed-integer nonlinear programming (MINLP) type, with non-convex objective functions, which are generally not tractable. We prove that both joint allocation problems are NP-hard. To address these issues, we employ a variant of the block successive upper-bound minimization (BSUM) framework, obtaining polynomial-time approximation algorithms to the original problem. Moreover, we evaluate the algorithms' robustness against outdated channel state information (CSI), present an analysis of the convergence of the algorithms, and a comparison of the sum-rate and Jain's fairness index of the novel algorithms with three other algorithms proposed in the literature. The Max-SR algorithm outperforms the others in the sum-rate sense, while the Max-Min outperforms them in the fairness sense.

In this paper, we analyze the association of a user terminal in a two-tier network (i.e., macrocells and millimeter wave small cells). We assumed a decoupled wireless access where a user terminal has the liberty to choose different base stations (BSs) in uplink and downlink based on the received power and the channel quality. A practical blockage model where a human body is a blockage to millimeter wave (mmW) is considered. An in-depth simulation study is done to explore the effectiveness of decoupled wireless access in a crowded environment. In addition to that, a detailed analysis on the intuitiveness and the mathematical tractability of the blockage model used is also provided. In the end, few research questions on the efficacy of decoupled wireless access are raised in this paper.