Flows in strongly regular periodic dynamic resource networks

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.

Problem statement. Due to their mobility, flexibility, ease of deployment, and low cost, Autonomous Aerial Vehicle (AAV) play an important role in future wireless networks. However, their pratical implementation faces challenges, including energy constraints, dynamic channel variations, interference management, and the need for efficient resource allocation to ensure seamless connectivity for ground users. Traditional optimization methods often fail to adapt to these complexities in real-time, limiting the effectiveness of AAV-assisted wireless networks. The aim of the work provides a comprehensive review of resource allocation in AAV-assisted wireless networks, focusing on power and bandwidth optimization strategies, as well as the key challenges in ensuring efficient and reliable communication. Methods: used in this study include a systematic review of existing literature, analyzing optimization approaches such as Game Theory, Artificial Intelligence for efficient resource allocation in AAV-assisted wireless networks. Novelty: this study analyzes resource allocation challenges in AAV-assisted network, focusing on the interdependence of power and bandwidth allocation. It explores optimization techniques like Game Theory, and Artificial Intelligence. Results. The analysis in this paper demonstrates that Game Theory and Artificial Intelligence based approaches significantly improve resource allocation efficiency. Additionally, the study identifies key challenges, including heterogeneous density network, security concerns, and complex channel modeling, providing insights for future research. Practical / Theoretical Relevance: This study advences the theoretical understanding of resource allocation in AAV-assisted wireless networks by integrating optimization strategies from Game Theory and Artificial Intelligence. Pratically, it provides insight into enhancing network efficiency, adaptability and security, making AAV-based communication more viable for real-world applications, such as disaster recovery, remote areas coverage, and IoT data collection.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

Cooperative dynamic free-space optical (FSO) networks exploit the fact that atmospheric losses are distance dependent to enhance the performance of FSO networks. This enhancement is achieved by sharing the resources of shorter links among different nodes in the network. In this paper, two joint transceiver placement and resource allocation schemes are proposed to optimally place FSO redundant transceivers based on optimal resource allocation in cooperative dynamic FSO networks. Specifically, one scheme increases reliability and capacity, while the other increases reliability and fairness of cooperative dynamic FSO networks during severe weather conditions. The schemes are formulated as multi-objective and bi-level integer linear programming problems and solved using an exhaustive search to obtain optimal solutions. The numerical results reveal that higher reliabilities can be achieved with enhanced capacities and fairness using the first and second schemes, respectively. Furthermore, these improvements are achieved by using many fewer numbers of FSO redundant transceivers than those of random placement.

We consider the quickest flow problem in dynamic networks with a single source s and a single sink t: given an amount of flow F, find the minimum time needed to send it from s to t, and the corresponding optimal flow over time. We introduce new mathematical formulations and derive optimality conditions for the quickest flow problem. Based on the optimality conditions, we develop a new cost-scaling algorithm that leverages the parametric nature of the problem. The algorithm solves the quickest flow problem with integer arc costs in O(nm log(n2/m) log(nC)) time, where n, m, and C are the number of nodes, arcs, and the maximum arc cost, respectively. Our algorithm runs in the same time bound as the cost-scaling algorithm by Goldberg and Tarjan [10, 11] for solving the min-cost flow problem. This result shows for the first time that the quickest flow problem can be solved within the same time bound as one of the fastest algorithms for the min-cost flow problem. As a consequence, our algorithm will remain one of the fastest unless the quickest flow problem can be shown to be simpler than the min-cost flow problem.

With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computing and caching resources. In fact, vehicular networks are nonstationary, and how to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important, however, really challenging. In this paper, we propose a general framework that can enable fast-adaptive edge resource allocation for dynamic vehicular environment. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs). We combine hierarchical reinforcement learning with meta learning, which makes our proposed framework available to quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. The extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios. This is consistent with the real-world situations and can significantly improve the performance of resource allocation in dynamic vehicular networks.

The advent of sixth-generation (6G) mobile networks introduces unprecedented demands for ultra-low latency, terabit-per-second throughput, and dynamic resource allocation across heterogeneous services such as URLLC, eMBB, and mMTC. Traditional resource management approaches lack the adaptability and predictive intelligence required to operate under such conditions. This study proposes a comparative analysis of deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures—for intelligent resource allocation in 6G environments. Synthetic traffic patterns and benchmark datasets were used to emulate realistic 6G workloads, and models were evaluated using key performance metrics: accuracy, latency, throughput, energy efficiency, and fairness index. Simulations were conducted in Python using TensorFlow and PyTorch frameworks within SDN-based virtualized environments to replicate programmable 6G infrastructures. The proposed hybrid system, integrating CNN, RNN, and Transformer modules, achieved latency reduction of 38%, throughput improvement of 22%, energy savings of 26%, and fairness index gain of 18% compared to baseline models. These findings demonstrate the viability of deep learning for scalable, adaptive, and sustainable resource allocation in next-generation mobile networks. The study contributes a reproducible simulation framework, a performance benchmark across models, and a foundation for future research in federated learning, edge AI, and 6G deployments.

A deep reinforcement learning approach is applied, for the first time, to solve the routing, modulation, spectrum and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment - compatible with OpenAI's Gym - was designed and implemented to emulate the operation of MCF-EONs. The new environment processes the agent actions (selection of route, core and spectrum slot) by considering the network state and physical-layer-related aspects. The latter includes the available modulation formats and their reach and the inter-core crosstalk (XT), an MCF-related impairment. If the resulting quality of the signal is acceptable, the environment allocates the resources selected by the agent. After processing the agent's action, the environment is configured to give the agent a numerical reward and information about the new network state. The blocking performance of four different agents was compared through simulation to 3 baseline heuristics used in MCF-EONs. Results obtained for the NSFNet and COST239 network topologies show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability concerning the best-performing baseline heuristic methods.

Link prediction aims to find the missing link in current networks or estimate the likelihood the link will appear in the future. In many real-world scenarios, networks can be massive and drastically evolving. Many recent works concentrated on how to solve the procession of massive data, which cares less about the effects of the temporal information. In some dynamic networks, the edge that appears a long time ago will have fewer effects on the appearance of edges in the future. The accuracy of link prediction in dynamic networks can be further improved by adding temporal information in prediction measures. In this paper, we mainly explore the effects of the link temporal information in the graph stream link prediction. We design a graph stream-based framework to solve link prediction problems in dynamic networks which can provide a convenient platform to implement different link prediction methods for dynamic networks, and evaluate these methods on a fair basis. Then, we proposed the Time Decay-based Link Prediction (TDLP) method to improve the efficient of link prediction problem in the dynamic networks. TDLP extends the neighborhood-based measure with time decay functions in graph stream scenarios which can handle temporal information and massive data. Experiment results demonstrate that the accuracy of link prediction for dynamic networks can be efficiently improved in our TDLP method.

Efficient spectrum and resource allocation remains a fundamental challenge in fifth generation (5G) wireless networks due to heterogeneous traffic requirements, spectrum scarcity, and dynamic interference conditions. Conventional scheduling and optimization techniques struggle to meet the stringent quality of service (QoS) demands of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This paper presents an improved deep reinforcement learning (DRL)-based optimization framework for adaptive spectrum and resource allocation in 5G networks. The proposed model integrates Deep Q-Network (DQN), Double DQN (DDQN), and DDQN with Prioritized Experience Replay (PER) to enhance convergence speed, stability, and scalability. A realistic simulation environment combining ray-traced channel data and packet-level traffic modeling was developed to evaluate system performance. Results demonstrate that the proposed DRL framework achieves up to 7% throughput improvement over proportional fair scheduling, reduces average latency by 10–12%, and lowers URLLC violation rates by approximately 15% under high-interference and heterogeneous traffic scenarios. These findings confirm the feasibility of DRL as a practical and scalable solution for real-time 5G resource management.

This article investigates resource allocation in ATM networks and emphasizes bandwidth allocation. Resource allocation methods are categorized, and static and dynamic allocation strategies are compared. Dynamic allocation uses actual traffic behavior, while static allocation uses only the reference traffic condition given a priori. Examples of dynamic allocation strategies are available bit rate flow control, dynamic connection admission control, and dynamic VP bandwidth control. Dynamic resource allocation is shown to be promising for situations where the a priori reference model is unclear.

The sixth‐generation (6G) wireless networks are envisioned to deliver unprecedented capabilities, including ultra‐high data rates, ultra‐low latency, and massive connectivity. Among the emerging technologies, the terahertz (THz) spectrum is a key enabler for achieving terabit‐per‐second transmission speeds. However, THz communication faces significant obstacles, such as severe path loss, frequent signal blockage, and high channel variability. To address these challenges, this paper introduces an AI‐driven resource allocation and beamforming in 6G terahertz networks (AI‐DRAB‐6G‐THz) that integrates dynamic resource allocation and adaptive beamforming for enhanced performance in 6G THz networks. The framework employs deep reinforcement learning (DRL) to intelligently manage spectrum and power resources in real time, thereby optimizing both spectral efficiency and energy consumption. Simultaneously, a neural network–based beamforming model predicts optimal beam angles and alignment strategies by learning from user mobility patterns and channel state information (CSI). A MATLAB‐based 0–1000 rounds simulation environment, incorporating a realistic THz channel model and environmental constraints, is developed to assess system performance. Evaluation across multiple key metrics, including throughput, latency, spectral efficiency, and beam alignment accuracy, demonstrates that the proposed AI‐driven approach significantly outperforms traditional heuristic methods. Overall, this work underscores the feasibility and effectiveness of integrating AI into the physical layer of 6G systems, paving the way for intelligent, adaptive, and energy‐efficient wireless communication in future 6G networks.

Cross-layer based asymmetric resource allocation in relay-aided cognitive radio networks (CRN) is proposed in this paper. Existing cross-layer schemes on relay-aided CRN are based on symmetric resource allocation (RA) where the transmission duration is assumed to be symmetric between secondary base station (BS) to relay station (RS) and RS to mobile station (MS) links. This may not be realistic and hence our proposed model considers asymmetric resource allocation (ARA) in which the transmission durations are asymmetric. Very little work has been done in ARA for relay-aided networks and most of them considered only power allocation. The queue stability and quality of service (QOS) requirements are other considerations that have a significant impact on RA and hence are considered in this work. The QOS requirements can be brought in terms of the maximum delay threshold and the minimum throughput requirements. The interference threshold is evaluated from the QOS of the primary radio network. Based on the queue state information, channel state information and QOS, resources are allocated to the secondary radio network. Our proposed ARA differs from existing ARA schemes in terms of additionally considering the subcarrier pairing for resource allocation. This work evolves an optimal cross-layer based ARA considering the above mentioned factors into account. The proposed ARA includes subcarrier allocation, subcarrier pairing and power allocation. Simulation results show that the proposed ARA scheme increases the system throughput and serves the edge users satisfactorily.

We consider a multiagent resource allocation problem where individual users intend to route traffic by requesting the help of entities across a network, and a cost is incurred at each network node that depends on the amount of traffic to be routed. We propose to study contract-based network resource allocation. In our model, users and nodes in the network make contracts before nodes route traffic for the users. The problem is an interesting self-interested negotiation problem because it requires the complete assembly of a set of distinct resources, and there are multiple combinations of distinct resources that could satisfy the goal of negotiation. First, we characterize the network allocation problem and show that finding optimal allocations is NP-complete and is inapproximable. We take both Nash equilibrium and pairwise Nash equilibrium as the solution concepts to characterize the equilibrium allocations. We find that, for any resource allocation game, Nash equilibrium and pairwise Nash equilibrium always exist. In addition, socially optimal allocations are always supported by Nash equilibrium and pairwise Nash equilibrium. We introduce best-response dynamics in which each agent takes a myopic best-response strategy and interacts with each other to dynamically form contracts. We analyze the convergence of the dynamics in some special cases. We also experimentally study the convergence rate of the dynamics and how efficient the evolved allocation is as compared with the optimal allocation in a variety of environments.