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The next generation wireless networks demand full coverage from the sky to the deep ocean, in which the water-air cross-boundary communication is a critical component in a coordinated uncrewed vehicular network that connects both uncrewed aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). In such a network, optical wireless communication (OWC) is an emerging and promising technology to implement direct high-speed wireless communication through the water-to-air interface. However, due to the high directionality of optical beams and the dynamics of the harsh oceanic environments, it faces significant challenges to achieve reliable and robust seamless cross-boundary communication, as the waves cause beam deflection and the mobility of the transceivers makes the link worse or even results in link disconnections. To deal with these challenges, in this paper, we investigate both static and mobile application scenarios to enable reliable water-air direct optical communications via the deep reinforcement learning (DRL) approaches. In the static scenario, we propose an active alignment method and derive the optimal beamwidth to satisfy the requirement of bit error rate (BER) and improve the link availability. To cope with the oceanic environment-induced channel dynamics, in the mobile scenario, we design a DRL-based UAV control strategy by incorporating the extended Kalman filter (EKF) prediction technique to enhance reliable communication between the mobile transceivers. The numerical simulations demonstrate that the proposed schemes achieve impressive performance in terms of energy consumption and reliable communications under both the static and mobile scenarios.

Underwater wireless optical communication (UWOC) has received widespread attention recently due to its distinct advantages in terms of high bandwidth, low power consumption, low delay and confidentiality. However, unlike underwater acoustic communication systems, it suffers from constraints, such as limited communication range and misalignment in oceanic extreme environments. To perform long-distance high bandwidth transmission such as real-time video streaming, it requires relaying technology to perform reliable and robust transmission. Although pre-deploying a large number of sensor nodes is one of the possible solutions, this is impractical in response to emergency events, such as oil pipeline leakage and maritime search and rescue. To this end, in this article, we propose to leverage a swarm of autonomous underwater vehicles (AUVs) to establish reliable underwater optical communication links in a rapidly changing marine environment for real-time video transmission. We first model the optical transmission channel, and then calculate both the optical angle and the reliable communicating range that satisfy a pre-defined bit error rate (BER). We then formulate the optimization problem based on two aligned solutions while targeting on minimizing both deployment energy consumption and time latency, in which the AUV's uncertainty issue caused by localization error and hovering instability is considered. Finally, we design an adaptive optical beam and power adjustment algorithm to enhance the robustness of the optical link further. Through extensive simulations, the results demonstrate that the proposed scheme is efficient and achieves reliable and robust communication.

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

In wireless communication, unmanned-aerial-vehicle (UAV) relay seems to be an available paradigm to combat path-loss and expand signal coverage. Due to the openness of wireless channel and the inconvenient recharging, transmission security and energy efficiency are the most concerned issues for UAV-enabled communication. In this article, for an UAV-enabled two-way relay system, where two wireless terminals exchange their information assisted by a flying UAV and a malicious user tries to eavesdrop the transmitted information, the achievable secrecy energy-efficiency is formulated when the UAV speed, trajectory, allowable power and the time scheduling in the uplink and downlink transmission are considered in a comprehensive manner. To solve this non-convex problem, we decompose it into three subproblems based on the block coordinate descent (BCD) method. Then, the successive convex approximation (SCA) technique and the Dinkelbach algorithm are combined to obtain the global solution in an iterative manner. Simulation results show that the proposed schemes have higher secrecy energy-efficiency compared to the existing baseline schemes.

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.

For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hardware cost and power consumption can be reduced via hybrid precoding, while keeping almost the same spectral efficiency (SE) as the fully digital precoding scheme. However, for different hardware configuration and channel environments, system SE varies with the number of transmitted data streams at the transmitter, and hence this should be determined primarily before hybrid precoding design. In this article, for single-user massive MIMO systems, a regression model based on the support vector machine technique is proposed, through which the optimal number of data streams can be obtained automatically based on the propagation characteristics of wireless channels, the number of antennas, and RF chains. To validate the correctness of the proposed model, simulation results are presented under various parameter settings.

Accurate state of charge (SOC) estimation of a battery is critical owing to assessing endurance and, hence, a vital component of a battery management system. In this work, with this objective, we study ternary lithium-ion batteries (TLiBs) using the hybrid pulse power characterization (HPPC)-based extended Kalman filter (EKF) algorithm. We examine the construction and underlying principles of TLiBs by conducting a comparative analysis of SOC estimation techniques. In addition, we inspect the corresponding circuit model after obtaining detection parameters through some experimental tests. The HPPC test also helps identify battery parameters through open-circuit voltage. Subsequently, we develop the HPPC test-based EKF algorithm for conducting a multi-station cooperative motion system. We equip the proposed algorithm with a dynamic second-order RC equivalent circuit model, resulting in a comprehensive battery simulation model under MATLAB/Simulink. With the maximum error being about 0.051 V, the simulation results exhibit that the optimization method can significantly improve the accuracy of SOC estimation. Additionally, the initial error is reduced by almost 0.028 V in terms of convergence under the employment of the HPPC test-based EKF algorithm. Experimental results eventually justify the efficiency of the proposed algorithm in improving the accuracy of SOC estimation for TLiBs.