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The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security challenges compared to traditional RANs. Many existing studies overlook these security requirements of the O-RAN networks. To gain deeper insights into the O-RAN system and security, this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications. We then delve into specifications of O-RAN security threats and requirements, aiming to mitigate security vulnerabilities effectively. By providing a comprehensive understanding of O-RAN architecture, use cases, and security considerations, this work serves as a valuable resource for future research in O-RAN and its security. [ABSTRACT FROM AUTHOR]

The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks. [ABSTRACT FROM AUTHOR]

In recent years, the demand for vehicular edge computing (VEC) has grown rapidly due to the increasing need for low-latency and high-throughput applications such as autonomous driving and smart transportation systems. Nevertheless, offering VEC services in rural locations remains a difficulty owing to a lack of network facilities. We tackle this issue by taking advantage of high-altitude platforms (HAPs) and rate-splitting multiple access (RSMA) techniques to propose an HAP-assisted RSMA-enabled VEC system, which can enhance connectivity and provide computational capacity in rural locations. We also introduce a deep deterministic policy gradient (DDPG)-based framework that optimizes the allocation of resources and task offloading by jointly considering the offloading rate, splitting rate, transmission power, and decoding order parameters. Via results from extensive simulations, the proposed framework shows superior performance in comparison with conventional schemes regarding task success rate and energy consumption. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation forecasting (PVGF) is an attractive research topic for efficient energy management in smart city. In addition, the long short-term memory recurrent neural network (LSTM/RNN) has been actively utilized for predicting various time series tasks in recent years due to its outstanding ability to learn the feature of sequential time-series data. Although the existing forecasting models were obtained from learning the sequential PVGF data, it is observed that irregular factors made adverse effects on the forecasting results of very-short-term PVGF tasks, thus, the entire forecasting performance was deteriorated. In this regard, multiscale LSTM-based deep learning which is capable for forecasting very-short-term PVGF is proposed for efficient management. The model concatenates on two different scaled LSTM modules to overcome the deterioration that is originated from the irregular factors. Lastly, experimental results present the proposed framework can assist to forecast the tendency of PVGF amount steadily. [ABSTRACT FROM AUTHOR]

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful. [ABSTRACT FROM AUTHOR]

With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy cannot. Then, heuristic algorithms are finally designed for obtaining approximated optimal solutions in progressive policy. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an ACK-based reliable multicast protocol for directional antennas referred to as spatially pipelined ACK aggregation for reliable multicast (SPARM). To resolve the problems of ACK implosion and ACK collection latency, the SPARM exploits (1) spatial reuse and (2) pipelining of ACK aggregation. In the SPARM, receivers sequentially aggregate their ACKs while the sender multicasts a data frame to the next beam, both removing the ACK implosion problem and reducing ACK collection latency. Also, we prove that the aggregation process does not interfere with the sender multicasting to the next beam. Performance evaluation shows that the proposed SPARM has full reliability and outperforms the existing schemes with respect to the throughput by about 200%. [ABSTRACT FROM PUBLISHER]

Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario. [ABSTRACT FROM AUTHOR]