The rapid development of deep space exploration missions has led to the continuous expansion of interplanetary networks (IPNs) for enhanced data transfer capacities and reliability. In this paper, we propose a novel deep reinforcement learning (DRL) framework for optimizing the scaling of IPN topologies (i.e., the placement of relay satellites), such that the routing and data scheduling of interplanetary data transfer (IP-DT) in the scaled IPN achieves maximized performance gain. Our proposal leverages graph neural networks (GNNs) to extract topological correlations within an IPN and hereby learns progressive local rewriting policies for approaching the optimal solution. Extensive simulations verify the effectiveness and robustness of our proposal, showing that the learned relay satellite placements facilitate higher reliability (in terms of data delivery ratios) and lower end-to-end latency for different IPN scenarios, when compared with the existing benchmarks.
Several 5G/6G applications are anticipated to require low latency and high service availability. Latency constraints are expected to be satisfied by utilizing edge computing servers termed multi-access edge computing (MEC) servers. MEC can provide many benefits, including improved network performance, increased reliability, and reduced costs. However, the location of MEC servers can have a significant impact on the reliability of the services they support. In this paper, we study the impact of combination of server location and the physical topology on service availability of connections between the user equipment (UE) and MEC. We concentrate on applications that utilize multi-connectivity between the UE and MEC. We focus on evaluating the availability of services supported by MEC servers located at different positions within the radio access network, such as at macro base stations, at the access sites, or at a central location as well as the effects of the backhaul topology deployed, e.g., ring or tree.
For better network performance and reliability, multi-connectivity (MC) paths are established to take advantage of path diversity for dependable packet delivery. However, MC can sometimes allocate excessive resources without considering the actual availability of the primary path. To address this inefficiency, we propose a risk-aware backup path allocation strategy that accounts for different risks that could impact the primary path's availability. Our approach includes estimating the number of users affected by specific risks and assigning riskdisjoint backup paths to user groups. This allocation method considers both the availability of primary and backup paths, as well as the shared use of backup resources among users. By grouping users according to disjoint risks and allocating backup paths accordingly, our method optimizes resource usage. The simulation results demonstrate that our approach incorporates the risks associated with the allocation of the backup path, enhances the resilience of the network.
In this paper, we focus on adaptive image processing and transmission for aerial imagery tasks. An unmanned aerial vehicle (UAV) engages in these tasks, namely capturing and processing aerial photographs, and transmitting the processed image data to the back-end system at a base station (BS) for further analysis. Specifically, task-oriented semantic features and wireless channel state information (CSI) are leveraged to adjust the resolutions and compression ratios in processing image blocks. The processed image data is then transmitted at optimal rates by selecting appropriate modulation schemes. To maximize task performance while minimizing latency, we propose an adaptive algorithm based on deep reinforcement learning (DRL) that intelligently selects the image processing modes and data modulation schemes. Experimental results on an aerial image classification task show that the proposed adaptive approach significantly improves the accuracy across various signal-to-noise ratios, outperforming traditional image processing and transmission methods.
Ensuring reliable audio and video communication during disaster events is a critical area of study for the scientific community. The effectiveness of first aid in such situations is critical and can be life-saving. In such a scenario, the use of Unmanned Aerial Vehicle (UAV) networks is able to guarantee the provisioning of connectivity in areas severely involved in emergency and disaster events and where the communication infrastructures are seriously compromised. Anyway, this type of network - known as Flying Ad-Hoc Networks (FANET) - has to take into account different issues related to resource constraints, especially in terms of bandwidth and energy. To address these challenges, in this paper, a recruiting strategy inspired by ant colony behavior is proposed. This strategy has been designed in order to consider multiple metrics such as distance, bandwidth, energy, and delay. Integrating such a resource-aware recruiting strategy into the FANET permits the displacement of additional UAVs when needed due to a surge in users' requests or an increasing burden imposed on the existing ones. This approach allows the network to adapt to user demands, guaranteeing effective task completion during critical missions.
