Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

In the large-scale dynamic low-Earth orbit (LEO) satellite networks, the conventional TCP-based single-path transmission encounters challenges, such as prolonged propagation delay, frequent connection failures, and suboptimal resource utilization. In this article, we propose an integrated multipath network coding (IMPNC) transmission scheme. This scheme leverages multiple paths for end-to-end transmission to achieve bandwidth aggregation and redundant backup. The multipath transmission is facilitated by multipath quick UDP Internet connection (MPQUIC) protocol to adapt to the limited satellite bandwidth and caching resources. The proposed approach involves encoding packets at nodes along the paths, addressing the significant out-of-order problem arising from variable delays on different paths. Additionally, we present a software-defined networking (SDN)-based domain clustering architecture, which offers a more streamlined control approach, reducing overall complexity. Furthermore, we formulate the domain clustering problems as mixed-integer nonlinear programming and the coding-based routing problem as a Steiner tree problem. Evaluation results demonstrate that the proposed scheme effectively reduces the latency over 25.1%, enhances bandwidth utilization by 19.6%, and ensures reliable data transmission by reducing retransmission probability by 4.1%.

With digitization and globalization in the era of 5G and beyond, research on high-throughput satellites (HTS) to increase communication capacity and improve flexibility is becoming essential. To achieve efficient resource utilization and dynamic traffic demand matching, the multi-dimensional resource management (MDRM) problem of the HTS communication system has been studied in this paper. Since the MDRM problem is a non-convex mixed integer problem, we decompose it into two tractable sub-problems. First, the beam-domain resource configuration problem is formed to enable on-demand coverage. Next, the user-domain resource allocation problem is modeled to enable on-demand communication. Considering the two-domain optimization problem, a two-stage framework is developed based on the combination of self-supervised learning and deep reinforcement learning. Specifically, in the first stage, a maximum co-channel interference based self-supervised learning method is proposed to perform traffic demand matching through demand awareness. In the second stage, a soft frequency reuse based proximal policy optimization approach is presented to further increase the system capacity through interference coordination. The simulation results demonstrate that our proposed two-stage algorithm outperforms the benchmark schemes in terms of spectrum efficiency and demand satisfaction.

Internet of Things (IoT) is a key enabler for many industrial applications. Through the IoT, the safety risk analysis and early warning management of the underground mining can effectively reduce the frequency of accidents and failures, to save the loss of personnel and property. Therefore, the safety warning of underground mining based on IoT is of great significance. However, underground industrial IoT requires the deployment of a large number of energy-constrained sensors and sensing units, and the wireless signals they send are lost due to data collisions, consuming node energy and reducing energy efficiency. Therefore, real-time reliable transmission of sensing data under energy-constrained conditions is critical for construction safety warnings in harsh industrial monitoring environments. Existing transmission schemes such as Wireless Hart, WiFi, etc., due to high energy consumption, cannot be directly applied to underground mining monitoring environments with energy-efficient requirements. Moreover, the competition-based ZigBee transmission protocol with low power consumption cannot guarantee the real-time reliable transmission of packets. Aiming at the real-time and reliability problems of data transmission under energy-limited conditions, a multi-radio multi-channel deterministic transmission scheme based on time slot division is proposed in this paper. Firstly, for the tree hybrid topology of monitoring networks, a joint multi-channel multi-slots scheduling problem is formulated, which is proved NP-Hard. Then, based on the greedy strategy, a lightweight pseudopolynomial time transmission scheduling scheme is proposed. It is also proved that under the premise that this problem can be scheduled, the heuristic algorithm can obtain the optimal result under the tree hybrid topology. Finally, a lot of practical experiments were conducted to verify the performance of the transmission scheme. The experimental results illustrate that compared with the traditional transmission scheme, the proposal has lower packet loss rate and transmission delay, which effectively reduces the energy consumption of the sensing devices.

The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and $\epsilon$ -Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results.

Optimization-based radio resource management (RRM) has shown significant performance gains on high-throughput satellites (HTSs). However, as the number of allocable on-board resources increases, traditional RRM is difficult to apply in real satellite systems due to its intense computational complexity. Deep reinforcement learning (DRL) is a promising solution for the resource allocation problem due to its model-free advantages. Nevertheless, the action space faced by DRL increases exponentially with the increase of communication scale, which leads to an excessive exploration cost of the algorithm. In this paper, we propose a recursive frequency resource allocation algorithm based on long-short term memory (LSTM) and proximal policy optimization (PPO), called PPO-RA-LOOP, where RA means resource allocation and LOOP means the algorithm outputs actions in a recursive manner. Specifically, the PPO algorithm uses LSTM network to recursively generate sub-actions about frequency resource allocation for each beam, which significantly cuts down the action space. In addition, the LSTM-based recursive architecture allows PPO to better allocate the next frequency resource by using the generated sub-actions information as a prior knowledge, which reduces the complexity of the neural network. The simulation results show that PPO-RA-LOOP achieved higher spectral efficiency and system satisfaction compared with other frequency allocation algorithms.