Abstract: Satellite communication systems (SCSs) used for tactical purposes require robust security and anti-jamming capabilities, making frequency hopping (FH) a powerful option. However, the current FH systems face challenges due to significant interference from other devices and the considerable path loss inherent in satellite communication. This misalignment leads to inefficient synchronization, crucial for maintaining reliable communication. Traditional methods, such as those employing long short-term memory (LSTM) networks, have made improvements, but they still struggle in dynamic conditions of satellite environments. This paper presents a novel method for synchronizing FH signals in tactical SCSs by combining serial search and reinforcement learning to achieve coarse and fine acquisition, respectively. The mathematical analysis and simulation results demonstrate that the proposed method reduces the average number of hops required for synchronization by 58.17% and mean squared error (MSE) of the uplink hop timing estimation by 76.95%, as compared to the conventional serial search method. Comparing with the early late gate synchronization method based on serial search and use of LSTM network, the average number of hops for synchronization is reduced by 12.24% and the MSE by 18.5%. I. INTRODUCTION Satellite communication systems (SCSs) can transmit information over long distances without being limited by geographical boundaries. This technology has become essential in both military and civilian applications, such as command and control, meteorology, remote sensing, and video broadcasting. Generally, a SCS consists of a spacebased backbone network, a space-based access network, and a ground backbone network.

Abstract-- In replacing fossil fuels with renewable energy resources for carbon neutrality, the unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To address this issue, a reinforcement learning (RL) technique is introduced in this paper. For RL, a graph convolutional network (GCN) and a bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters, based on cooperative game theory. The flexible and reliable DC nanogrid is suitable for integrating renewable energy for a distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid cluster, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of an electric vehicle (EV) is executed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as GCN-convolutional neural network (CNN) layers for deep Q-learning network (DQN), GCN-LSTM layers for deep recurrent Q-learning network (DRQN), GCN-Bi-LSTM layers for DRQN, and GCN-Bi-LSTM layers for proximal policy optimization (PPO), are used for simulations. Power management of nanogrid clusters with P2P power trading is simulated on a distribution test feeder in real time, and the proposed GCN-Bi-LSTM-PPO technique achieving the lowest electricity cost among the RL algorithms used for comparison reduces the electricity cost by 36.7%, averaging over nanogrid clusters. Keywords: Deep reinforcement learning, P2P power trading, Nanogrid, Power management, Renewable energy I.INTRODUCTION The widespread use of distributed energy resources (DERs) has significantly altered how energy is generated, transported, and used along the energy pipeline. A more decentralized and open electrical network is made possible with increased number of prosumers--individuals who produce and consume energy simultaneously. As a result of this context, new opportunities and difficulties for power systems have emerged. Peer-to-peer (P2P) power trading is a novel paradigm of distribution systems with a utility grid (UT) related to carbon neutrality and renewable energy generation [1]. P2P power trading has become a viable alternative for prosumers looking to actively participate in the energy market. Moreover, P2P trading gives end users more flexibility, increases possibilities to use clean energy, and aids in the transition to a low-carbon energy system. In addition to this, the other participants in the power market can also profit by lowering the peak electricity demand, lowering operating and maintenance expenses, and enhancing the dependability of the electrical system.