Efficient resource allocation in bandwidth-sharing 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.

Max-min fair allocation for resources with hybrid divisibilities

This paper is devoted to studying the processes of resource allocation in dynamic resource networks. In such networks, the capacities of the arcs depend on time. Resource allocation in the network occurs in discrete time. The resource of each vertex is distributed only between adjacent vertices according to some rules. The study of the processes of resource redistribution in such networks is carried out. The main goal is to develop methods for finding the limit state (distribution) of a resource in a dynamic resource network. It is shown that the approach based on the construction of an auxiliary network is also applicable to reduce the problem of resource allocation in a dynamic network to a similar problem in an auxiliary network. Theorems on the existence of a limit state on an auxiliary graph are proved for strongly regular periodic dynamical networks. To find the limit states, one can use the approaches which are developed for the shortest path problem in dynamic networks.

We consider the proportional fair rate allocation in an 802.11 WLAN that supports multi-user MIMO (MU-MIMO) transmission by one or more stations. We characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities. While a number of features carry over from the case without MU-MIMO, in general neither flows nor stations need to be allocated equal airtime when MU-MIMO is available.

Proportional fair resource allocation in vehicle-to-infrastructure networks for drive-thru Internet applications

It has been analytically proved that both MaxNet and SumNet can achieve max-min fair allocation in static networks. In SumNet networks, such as the TCP algorithm of the current Internet, the source rate is controlled by congestion signal which is the sum of signals from all of the congested links along the path from the source to the destination. In MaxNet networks, only the most congested link generates the control signal to dictate the source rate. This paper investigates the practical aspects of both schemes, specifically, the convergence speed and fairness tracking capability under transient network conditions. We have shown that the stability of SumNet's max-min fairness heavily depends on the network load. Within the stable ranges of operating points, SumNet's max-min fair allocation is obtained at the cost of response speed. An enhanced approach is proposed to improve the stability of SumNet to meet the max-min fairness criterion. We have shown both analytically and by simulation that under stable conditions, Maxnet has faster convergence speed and better fairness tracking capability than SumNet in a highly volatile environment.

Multipath networks allow that each source-destination pair can have several different paths for data transmission, thus they improve the performance of increasingly bandwidth-hungry applications and well cater for traffic load balancing and bandwidth usage efficiency. This paper investigates fair resource allocation for users in multipath networks and formulates it as a multipath network utility maximisation problem with several fairness concepts. By applying the Lagrangian method, sub-problems for users and paths are derived from the resource allocation model and interpreted from an economic point of view. In order to solve the model, a novel rate-based flow control algorithm is proposed for achieving optimal resource allocation, which depends only on local information. In the presence of round-trip delays, sufficient conditions are obtained for local stability of the delayed algorithm. As for the end-to-end implementation in Internet, a window-based flow control mechanism is presented since it is more convenient to implement than rate-based flow control.

In this paper, we address the uplink resource scheduling problem in single carrier frequency-domain multiple access systems. In particular, we focus on the efficiency and fairness trade-offs in scheduling and resource allocation for wireless cellular networks. We present an efficient implementation method that translates these scheduling problems into set partitioning problems that are well-studied in the literature. Then, we discuss a family of utility functions that enable us to investigate the performance of different frequency domain schedulers such as the sum-rate maximization, proportional fair, and max-min fair schedulers. We use the price of fairness as a metric to analytically quantify these trade-offs. Based on the intuition that fairness of resource allocation in cellular radio networks corresponds to the prioritization of cell-edge user rates, we demonstrate that the proportional fair scheduler significantly improves fairness among users, and increases the rates offered to the cell-edge and median users when compared to the sum-rate maximization scheduler. This comes at the cost of reducing the cell-center user rates and the aggregate user rate. We present the steps on how to take into account the practical implementation constraints, in particular, those related with the discrete Fourier transform implementation, in the problem formulation. Simulation results that illustrate these trade-offs are also presented. We conclude that this type of analysis can provide guidelines for the network operators to control the efficiency and fairness trade-off as the data traffic grows.

In this paper, a cross-layer resource allocation mechanism is proposed for wireless multimedia service.In particular, a game theory based on quality of service (QoS) for multimedia users is introduced to deal with the fairness of network resource allocation in wireless networks.Moreover, the channel states of wireless users are additionally regarded under the cross-layer design in WiMAX environment.In details, the bargaining solution is adopted to discover the efficient and fair resource allocation strategy for multimedia service in considering QoS in the peak signal-to-noise ratio (PSNR) and the channel states in the carrier-to-interface ratio (CINR).The proposed mechanism is illustrated and evaluated by simulation results of transmitting video sequences in WiMAX environment.

Efficient and fair resource allocation associated with congestion control in multi-hop cognitive radio networks (CRN) is a challenging problem. In this paper, we consider their mutual relationship through formulating a cross-layer optimization problem which addresses both the aggregate utility maximization and energy efficiency. By primal-dual method, the optimal solution is obtained via a distributed algorithm while keeping the outage probability on primary links below the allowable thresholds. The attraction of our proposed algorithm is that we utilize the broadcast property of wireless medium for message passing in power control and preserve the existing TCP stack for congestion control mechanism.

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.

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.

5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.

Theoretical analysis and experiments have shown that TCP can become oscillatory and prone to instability in high bandwidth-delay-product networks. XCP was proposed to overcome these shortcomings by advertising an explicit window adjustment from the routers to the sources. However, XCP may under-utilize the bandwidth in a multi-bottleneck network and cause some flows to receive an arbitrarily small fraction of their max-min fair rates. Using solid control theoretical analysis and design, API-RCP has solved this potential problem of XCP successfully. Why API-RCP can achieve max-min fair bandwidth allocation and full link utilization in steady state in multi-bottleneck networks? To address this question, we made a simple theoretical analysis and then verify it by OPNET® simulations. We also propose a controller design scheme to optimize the system performance with minimum control errors. We demonstrate the improved performance through comparison between API-RCP and TCP/RED in a multi-bottleneck network.