Small cell networks offer a promising and viable approach to meeting the increasing demand for high-data-rate wireless applications. With the expected increase in the number of small cell deployments, energy efficiency (EE) is a crucial system design parameter that demands consideration from an eco-sustainability perspective. One way to improve EE is to switch off small cell base stations (BSs) or to keep them in energy-saving mode while preserving the quality of service (QoS) experienced by users. With “bits/joule” as the metric, we aim to optimize EE with the introduction of several levels of sleep depths. Using a stochastic geometry-based heterogeneous cellular network (HCN) model, we derive coverage probability, average achievable rate, and EE in heterogeneous K-tier wireless networks with different sleep modes for small cells. Then, we try to maximize EE under 1) a random sleeping policy and 2) a strategic sleeping policy, with constraints on both coverage probability and wake-up times. Due to the nonconvexity of EE, we propose an alternative low-complexity near-optimal solution by maximizing the lower bound of EE. We use an alternating iterative approach to solve the resulting multivariable optimization problem. Simulation results confirm the effectiveness of the scheme. With improvements of approximately 30% in EE with random sleeping policy, simulation indicates that instantaneous EE can be further improved by 15% with a strategic sleeping policy.

Corruption of photopleythysmograms (PPGs) by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this paper, we propose an automated, two-stage PPG data processing method to minimize the effects of motion artifacts. The technique is based on our prior work related to motion artifact detection (stage 1) [R. Krishnan, B. Natarajan, and S. Warren, ``Analysis and detection of motion artifacts in photoplethysmographic data using higher order statistics,'' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 2008), Las Vegas, Nevada, Apr. 2008, pp. 613-616] and motion artifact reduction (stage 2) [R. Krishnan, B. Natarajan, and S. Warren, ``Motion artifact reduction in photoplethysmography using magnitude-based frequency domain independent component analysis,'' in Proc. 17th Int. Conf. Comput. Commun. Network, St. Thomas, Virgin Islands, Aug. 2008, pp. 1-5]. Regarding stage 1, we present novel and consistent techniques to detect the presence of motion artifact in PPGs given higher order statistical information present in the data. We analyze these data in the time and frequency domains (FDs) and identify metrics to distinguish between clean and motion-corrupted data. A Neyman-Pearson detection rule is formulated for each of the metrics. Furthermore, by treating each of the metrics as observations from independent sensors, we employ hard fusion and soft fusion techniques presented in [Z. Chair and P. Varshney, ``Optimal data fusion in multiple sensor detection systems,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 1, no. 1, pp. 98-101, Jan. 1986] and [C. C. Lee and J. J. Chao, ``Optimum local decision space partitioning for distributed detection,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 25, no. 7, pp. 536-544, Jul. 1989], respectively, in order to fuse individual decisions into a global system decision. For stage two, we propose a motion artifact reduction method that is effective even in the presence of severe subject movement. The approach involves an enhanced preprocessing unit consisting of a motion detection unit (MDU, developed in this paper), period estimation unit, and Fourier series reconstruction unit. The MDU identifies clean data frames versus those corrupted with motion artifacts. The period estimation unit determines the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal by utilizing the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude-based FD independent component analysis unit. This helps reduce motion artifacts present at the frequencies of the reconstruction components. Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method.

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This article provides a comprehensive review of these hybrid works. These works can be classified into two categories: 1) algorithmic contributions, where DRL and GNN complement each other with an objective of addressing each other’s shortcomings and 2) application-specific contributions that leverage a combined GNN-DRL formulation to address problems specific to different applications. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

This paper demonstrates how reactive power injection from distributed generators can be used to mitigate the voltage/VAR control problem of a distribution network. Firstly, power flow equations are formulated with arbitrarily located distributed generators in the network. Since reactive power injection is limited by economic viability and power electronics interface, we formulate voltage/VAR control as a constrained optimization problem. The formulation aims to minimize the combined reactive power injection by distributed generators, with constraints on: 1) power flow equations; 2) voltage regulation; 3) phase imbalance correction; and 4) maximum and minimum reactive power injection. The formulation is a nonconvex problem thereby making the search for an optimal solution extremely complex. So, a suboptimal approach is proposed based on methods of sequential convex programming (SCP). Comparing our suboptimal approach with the optimal solution obtained from branch and bound method, we show the trade-off in quality of our solution with runtime. We also validate our approach on the IEEE 123 node test feeder and illustrate the efficacy of using distributed generators as distributed reactive power resource.

As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend information-theoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term context-based semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.