Characterizing Contract-Based Multiagent Resource Allocation in Networks

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.

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.

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

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

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.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

We analyse the problem of finding an allocation of resources in a multiagent system that is as fair as possible in terms of minimising inequality between the utility levels enjoyed by the individual agents. We use the well-known Atkinson index to measure inequality and we focus on the distributed approach to multiagent resource allocation, where new allocations emerge as the result of a sequence of local deals between groups of agents agreeing on an exchange of some of the items in their possession. Our results show that it is possible to design systems that provide theoretical guarantees for optimal outcomes that minimise inequality, but also that in practice there are significant computational hurdles to be overcome: finding an optimal allocation is computationally intractable---independently of the approach chosen---and large numbers of potentially highly complex deals may be required under the distributed approach. From a methodological point of view, while much work in multiagent resource allocation relies on combinatorial arguments, here we use insights from basic calculus.

In this paper, we present an energy-per-bit minimized radio resource allocation scheme in heterogeneous networks equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. Specifically, we formulate an optimization problem related to minimization of energy-per-bit which takes a form of nonlinear fractional programming. Then we derive a parametric optimization problem out of that fractional programming and solve the original problem by using a double-loop iteration method. In each iteration, we derive the optimal resource allocation policy by applying Lagrangian duality and an efficient dual update method. In addition, we present suboptimal resource allocation algorithms using the properties of the optimal resource allocation policy. Numerical results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency.

Heterogeneous cloud small cell network (HCSNet) with energy harvesting is a promising technique in the next generation mobile communications. However, the cross- tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter-tier and intra- tier of HCSNet. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross-tier/co-tier interference mitigation, energy harvesting and incomplete channel state information. The power allocation problem in HCSNet is modeled as a non-cooperative game by introducing a time-varying cross-tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the the total interferences experienced by users on each subchannel. Iterative scheme of power optimization and subchannel allocation is proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in HCSNet.

Cognitive radio technology is proposed to improve spectrum utilization efficiency through exploiting the unused portions of spectrum in an opportunistic manner. In cognitive radio networks (CRNs), primary users (PUs) are allowed to lease out their unused spectrum to share with cognitive users (CUs). In a CRN consisting of multiple PUs and CUs, the spectrum pricing and resource allocation scheme have to be optimally designed, so that the efficient spectrum sharing among PUs and CUs can be supported and the QoS requirements of both PUs and CUs can be guaranteed. In this paper, we consider the joint spectrum pricing and resource allocation problem of a CRN with imperfect channel condition, i.e., channel state information of the PUs is not fully accessible to the CUs. To characterize the willingness of PUs to share subchannels and the intention of CU on accessing subchannels, a pricing scheme for both PUs and CUs is designed. Addressing the competition of CUs in channel accessing and power allocation, a non-cooperative Game model is formulated. Through solving the Nash equilibrium solution of the Game model, the optimal pricing and resource allocation strategy can be obtained. Numerical results demonstrate the efficiency of the proposed algorithm.

Semi-cooperative game theoretic framework for resource allocation in cognitive cellular networks

Re-establishing emergency communication and ensuring rapid response are critical for rescue operations in natural disaster scenarios, such as earthquakes, floods, and wildfires. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks have emerged as a promising solution to re-establish communication links and provide flexible computational support in these complex environments. However, the existing UAV-assisted MEC research has not fully investigated the joint optimization of task offloading and resource allocation (JTORA) problem, considering both terrain obstacles and high-interference zones. In this paper, we investigate the JTORA problem to minimize the energy consumption for communication and computation in a rate-splitting multiple access (RSMA)-based UAV-assisted mobile edge computing (MEC) network. RSMA is utilized to enhance interference management and improve spectral efficiency. We propose a proximal policy optimization (PPO)-based method to optimize the task offloading ratio, message splitting ratio, and RSMA precoding matrix for the proposed JTORA problem. Simulation results show that the proposed approach effectively enhances system efficiency and sustainability.

With the explosive growth of wireless data traffic, the energy-efficient design in wireless networks is becoming an inevitable trend. In this paper, we study the low-complexity design issue of energy-efficient radio resource allocation in downlink OFDMA networks. Distinguished from the existing works, we focus on the tradeoff between the energy efficiency and the total transmit power. The closed-form optimal solution is obtained by solving the first-order optimality condition of the energy efficiency maximization problem. Furthermore, based on the analytical solutions, we develop a low-complexity optimal algorithm for the energy-efficient resource allocation. Simulation results show that the proposed algorithm achieves the optimal energy efficiency with significantly lower computational complexity compared with the conventional iterative methods.

Consider an infrastructure-based multi-band cognitive radio network (CRN) where secondary users (SUs) opportunistically access a set of sub-carriers when sensed as idle. The carrier sensing threshold which affects the access opportunities of SUs is conventionally regarded as static and treated independently from the resource allocation in the model. In this article, we study jointly the optimization of detection threshold and resource allocation with the goal of maximizing the total downlink capacity of SUs in such CRNs. The optimization problem is formulated considering three sets of variables, i.e., detection threshold, sub-carrier assignment and power allocation, with constraints on the PUs’ rate loss and the power budget of the CR base station. Two schemes, referred to as offline and online algorithms respectively, are proposed to solve the optimization problem. While the offline algorithm finds the global optimal solution with high complexity, the online algorithm provides a close-to-optimal solution with much lower complexity and realtime capability. The performance of the proposed schemes is evaluated by extensive simulations and compared with the conventional static threshold selection algorithm specified in the IEEE 802.22 standard.