Link Selection Research Articles

DT-DDQNR: A digital twin assisted direct-to-cell satellite network intelligent routing algorithm

This paper presents a novel routing algorithm designed for direct-to-cell satellite constellations in satellite networks. The algorithm aims to ensure stable Quality of Service (QoS) for users despite the highly dynamic nature of satellite links and network conditions. It comprises two key components: a terrestrial-satellite link selection model and an inter-satellite multi-hop routing decision model. To enhance decision-making, the algorithm incorporates a mechanism that interacts with large-scale digital twin constellations, offering agents with high-precision state data inputs and feedback for training and application support. The terrestrial-satellite link selection process is modeled as a constraint satisfaction problem, maximizing the Link Quality Indicator (LQI). Meanwhile, inter-satellite data forwarding decisions are determined utilizing an enhanced dynamic reward mechanism and a re-designed neural network in the DDQN model. Simulation results demonstrate the superiority of the Digital Twin-Assisted Double Deep Q-Network Routing (DT-DDQNR) algorithm over traditional routing algorithms in terms of overall network QoS performance, offering a promising solution for optimizing data transmission in integrated terrestrial-satellite networks.

Secure transmission of IRS-UAV buffer-aided relaying system with delay constraint

This paper investigates the secure communication between legitimate users in the presence of eavesdroppers, where the Intelligent Reflective Surface-Unmanned Aerial Vehicle (IRS-UAV) and Buffer-Aided (BA) relaying techniques are utilized to enhance secrecy performance. By jointly optimizing the link selection strategy, the UAV position, and the reflection coefficient of the IRS, we aim to maximize the long-term average secrecy rate. Specifically, we propose a novel buffer in/out stabilization scheme based on the Lyapunov framework, which transforms the long-term average secrecy rate maximization problem into two per-slot drift-plus-penalty minimization problems with different link selection factors. The hybrid Particle Swarm Optimization-Artificial Fish Swarm Algorithm (PSO-AFSA) is adopted to optimize the UAV position, and the IRS reflection coefficient optimization problem is solved by iterative optimization in which auxiliary variables and standard convex optimization algorithms are introduced. Finally, the delay constraint is set to ensure the timeliness of information packets. Simulation results demonstrate that our proposed scheme outperforms the comparison schemes in terms of average secrecy rate. Specifically, the addition of BA improves the average secrecy rate by 1.37 bps/Hz, and the continued optimizations of IRS reflection coefficients and UAV positions improve the average secrecy rate by 2.46 bps/Hz and 3.75 bps/Hz, respectively.

Outage-guaranteed transmission for IRS-assisted FSO systems.

Intelligent reflecting surface (IRS)-assisted free space optical (FSO) communication can overcome line-of-sight and bring spatial diversity, which is considered to be a good complement to direct FSO links. This paper is interested in understanding the role of IRS-assisted FSO links when coexisting with direct FSO links. Due to the diverse nature of existing FSO channel models and the reliability requirements of IRS-assisted FSO systems, this paper aims to investigate the transmission problems of link selection and power configuration under the quality of service guarantee for link outage, which can be applicable to different channel models. Despite the inherent complexity of the problems, transmission schemes, i.e., sum rate transmission (SRT) and rate fairness transmission (RFT) are developed, which can achieve the respective optimal performance. Also, some insights for transmission schemes are provided. The results verify the efficacy of the proposed transmission schemes. The performance of the system may be improved by 40% and 41% for SRT and RFT, respectively.

Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.

Optimizing resource allocation in UAV-assisted ultra-dense networks for enhanced performance and security

The deployment of unmanned aerial vehicles (UAVs) in ultra-dense networks (UNDs) has significantly advanced network capabilities in 5G/6G environments, addressing coverage enhancement and security concerns. Our research presents a deep reinforcement learning (DRL) based approach designed to manage the increasing data traffic demands and limited communication resources in UAV-assisted UNDs. Traditional DRL methodologies often struggle with challenges like low sample efficiency and energy wastage, which can indirectly impact network security and stability. To address these concerns, we introduce the Stabilizing Transformers based Potential Driven Reinforcement Learning (STPD-RL) framework. STPD-RL optimizes critical network operations such as transmission link selection and power allocation, directly contributing to improved energy efficiency and robust network performance. Initially, we have refined the potential driven experience replay and implemented it into resource allocation in UAV-assisted UDN for the inaugural time. By assigning a potential energy function to each state in experience replay, users can employ intrinsic state supervision to learn from a spectrum of good and bad experiences. Subsequently, we have employed stabilizing transformers to hasten the learning trajectory for resource allocation policies, thereby enhancing the stability of model training. Furthermore, we have integrated potential driven experience replay and stabilizing transformers within the Proximal Policy Optimization algorithm, thus formulating our uniquely tailored STPD-PPO. In simulations with many users and base stations, STPD-PPO outperformed traditional PPO in metrics such as entropy loss, policy loss, and value loss. Results suggest that our STPD-PPO surpasses traditional DRL algorithms in several respects, including convergence rate, energy efficiency, total power consumption, and exploration capacity.

User Cooperation and Link Selection in Hybrid VLC/RF Systems With NOMA

User Cooperation and Link Selection in Hybrid VLC/RF Systems With NOMA

An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network.

Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.

Which Link Matters? Maintaining Connectivity of Uncertain Networks Under Adversarial Attack

This paper studies connectivity maintenance in uncertain networks under adversarial attack, where a defender conceals crucial links to prevent the largest connected component from being decomposed by an attacker. In contrast with its static counterpart, connectivity maintenance in uncertain networks involves additional probing on links to determine their existence. Therefore, by modeling an uncertain network as a random graph with each link associated with an existence probability and a probing cost, our goal is to design a defensive strategy for link selection that maximizes the expected size of the largest remaining connected component with the minimum expected probing cost, and moreover, the strategy should be independent of the attacking patterns. To this end, we first unravel the computational complexity of the problem by proving its NP-hardness, and then propose optimal defensive strategies based on dynamic programming and multi-objective optimization. Due to the prohibitive computational cost of optimality, two approximate defensive strategies are further designed to pursue decent performance with quasilinear complexity, in which the first one is a heuristic approach that quantifies the link vulnerability through an analogy from the degree centrality of a vertex in static networks to the connectivity weight of a link in uncertain networks, and the second one is an adaptive greedy policy incorporating the minimax rule from game theory, which minimizes the possible loss suffered by the defender in a worst-case scenario and has a constant approximation ratio. Extensive experiments on both synthetic and real-world network datasets under diverse attacking patterns demonstrate the superiority of the proposed strategies over baselines.

Hybrid FSO/RF Communications in Space–Air–Ground Integrated Networks: A Reduced Overhead Link Selection Policy

Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.

Retracted: Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Retracted: Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Intelligent Backhaul Link Selection for Traffic Offloading in B5G Networks

Intelligent Backhaul Link Selection for Traffic Offloading in B5G Networks

Performance Evaluations for RIS-Aided Satellite Aerial Terrestrial Integrated Networks With Link Selection Scheme and Practical Limitations

Performance Evaluations for RIS-Aided Satellite Aerial Terrestrial Integrated Networks With Link Selection Scheme and Practical Limitations

Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy.

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.

Selective tightening algorithm for the optimization of pipeline network designs in the energy industry

Energy industries face the challenge on how to design networks to gather flows from unconventional assets due to the relative short lifetime of the wells. The optimal design of the network of pipelines and processing facilities is a challenging problem from both combinatorial and nonlinear viewpoints. To date, real instances of this problem cannot be solved to global optimality unless simplifying assumptions are made. We propose a systematic algorithm to address the optimal planning of gathering networks in a multiperiod horizon. It relies on the strategic selection of links on which fluid-dynamic equations are imposed. By relaxing constraints, the resulting mixed-integer programming models are still complex from the combinatorial standpoint, but they can be solved in reasonable computational times. The algorithm progressively adds constraints to the potential arcs, seeking for the global optimal solution by selectively tightening relaxations. Results show that computational times can be reduced by two orders of magnitude.

Communication and Energy Efficient Decentralized Learning Over D2D Networks

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile devices to collaboratively train artificial intelligence networks without the centralized parameter server. However, a densely connected network will cause large learning latency and energy consumption due to the limited computation and communication resources. In addition, link selection and aggregation weight have a significant impact on the learning performance. To cope with these challenges, we propose a joint computing power adjustment, wireless resource allocation, link selection, and aggregation weight adaptation mechanism to improve both communication and energy efficiencies. Specifically, the learning performances including the convergence rate, per-iteration learning latency, and per-iteration energy consumption are first analyzed. Then, an optimization problem is formulated to minimize the total learning cost, which is defined as the weighted sum of total learning latency and energy consumption. Given a network topology, the computing power and wireless resource allocation are optimized by the alternating optimization algorithm. Moreover, the optimal aggregation weight is obtained by semidefinite programming. With respect to link selection, we propose a tabu search based meta-heuristic algorithm to approximately achieve feasible solutions with a low computational complexity. Finally, extensive experiments demonstrate that the proposed link selection algorithm can significantly reduce the learning cost under the given learning accuracy requirement.

Reuse-based online joint routing and scheduling optimization mechanism in deterministic networks

Deterministic networks plan the entire network traffic and calculate the scheduling time to meet the critical traffic requirements of specific domains, enabling real-time and deterministic interaction of massive data. However, in dynamic industrial automation scenarios where devices undergo changes, existing mechanisms face challenges in quickly responding to dynamic transmission demand changes caused by rapid traffic migration. To address this issue, this paper proposes a reuse-based online scheduling mechanism that utilizes dynamic path planning of flows and coordinated scheduling of time slots to achieve deterministic transmission of dynamic flows. In the offline phase, the mechanism proposes a backbone link selection and a scalable intelligent routing strategy, constructs a set of routing and scheduling co-design constraints, and generates an offline scheduling table using an iterative scheduling algorithm. In the online scheduling phase, a reuse-based online scheduling algorithm is proposed to achieve rapid scheduling and deterministic transmission of dynamic real-time flows. It utilizes the offline scheduling results and the period offset of migrated flows. The reuse of offline scheduling results reduces computation time and expands the solution space. Experimental results demonstrate that the proposed mechanism achieves a maximum increase in scheduling success rate of 37.3% and reduces time costs by up to 66.6% compared to existing online scheduling algorithms.

Experimental evaluation of transmission link selection in IEEE 802.11be multi-link operation

Experimental evaluation of transmission link selection in IEEE 802.11be multi-link operation

Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs

In this work, we enhance the total network throughput performance of a FANET system consisting of multiple UAVs and a ground control station (GCS) by optimizing the multihop routing structure and total power budget required for communication in the presence of inter-UAV interference. To achieve this, an optimization problem is formulated to maximize the network throughput within a given power budget. However, due to the non-convex and integer nature of the problem, it is NP-hard. Hence, it is dealt with differently by solving two daughter problems iteratively, i.e., (1) power allocation (PA) and (2) optimal link selection (LS) for the given solution of PA. These subproblems are solved iteratively by utilizing alternating optimization to obtain the global optimal solution of the main problem. The study presents various numerical results, which validate the effectiveness of the proposed routing structure and provide insights into the impact of interference on system parameters.

An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation

In order to address a problem in the research related to the low stability and communication efficiency issues in the generation of optical communication constellation network topology, there is a critical component for sensing the interaction among satellites. This paper makes a novel contribution by proposing a multi-constraint optimization algorithm for optical communication constellation network topology generation. The proposed method significantly improves the existing systems by considering multiple attributes that influence the establishment of inter-satellite links and reducing the impact of subjective factors. This unique approach involves calculating the entropy weight of each attribute using the information entropy method based on the degree of change in each indicator. Subsequently, the weights of the indicators are corrected to obtain the objective weight of each attribute. The comprehensive weight of the link, computed based on the initial link attribute values and weights, serves as the decision basis for link selection, thereby forming the satellite network topology. Upon evaluation, the proposed method has shown remarkable superiority over the compared schemes in terms of communication efficiency and stability.

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

Highly robust networks can resist attacks, as when some UAVs fail, the remaining UAVs can still transmit data to each other. In order to improve the robustness of a multi-UAV network, most methods construct the network by adjusting the positions of the UAVs and adding a large number of links. However, having a large number of links greatly consumes communication resources and increases serious signal interference. Therefore, it is necessary to study a method that can improve robustness and reduce the number of links. In this paper, we propose a method that consists of combining formation control and link selection, which can work in a distributed manner. For formation control, our method keeps the UAVs compact in the obstacle environment through an improved artificial potential field. The compact formation enables UAVs to have a large number of neighbors. For link selection, reinforcement learning is used to improve the robustness of the network and reduce the number of network edges. In the simulation of the 3D urban environment, three failure modes are used to verify the robustness of the network. The experimental results show that even if the number of links is reduced by 20%, the networks designed by our method still have strong robustness.