Power Allocation Method Based on Two-Stage Stackelberg Game in Wireless Networks

Recently, as it can increase the capacity of wireless networks greatly through spatial diversity by taking advantage of antennas on other nodes, cooperative communication (CC) has been obtaining more and more attention. However, as the selfish nature, the wireless node may be unwilling to serve as relay node if they can't get the corresponding reward. In this paper, we constructs a real double-auction scenario between source nodes and relay nodes instead of idealized truthful market which may obtain relatively lower system performance. We consider the system performance involving (1) successful source-relay pairs, (2) system capacity and (3) social welfare (SW). We transform the double auction-based optimal relay assignment problem into Maximum Matching (MM) and Maximum Weighted Matching (MWM) problem respectively and solve them using corresponding algorithms. Extensive experiments show that this mechanism can achieve higher system efficiency than truthful auction.

Due to the large uncertainty of signal reception, the transmission speed of the wireless communication network resource allocation method is low. Based on social relations, a dynamic allocation method for wireless communication network resources is designed. Establish a wireless ad hoc network composed of a random number of nodes to determine the upper limit of its security capacity, and the nodes at the edge need to undertake more communication transmission loads on average. According to the similarity between users, a triple closed model is used to measure the social trust relationship in wireless communication networks. Considering the social trust and encounter frequency among users, the optimal relay node is selected for the wireless network communication link. The sum of data transmission rates under each allocation scheme is calculated and sorted in descending order, and resources are dynamically allocated based on the principle of maximum overall network throughput. Compared with other schemes through simulation, the dynamic allocation method of wireless communication network resources based on social relations can increase the network transmission speed and improve the communication performance.

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.

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.

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

In wireless networks many nodes contend for available resources creating a challenge in resource allocation. With shared resources, fairness in allocation is a serious issue. Fairness metrics have been defined to measure the fairness level of resource decisions in allocations. Therefore, fairness metrics significantly influence network and node performances emphasizing the need for due diligence to fairness metrics. It can be seen that fairness metrics in many of the allocation strategies and algorithms in the literature are not analyzed in depth to show the overall fairness of allocations from different perspectives. Hence, we propose a series of fairness metrics for resource allocation in wireless networks, which evaluate individual, system, short and long term fairness. Our metrics are general enough to be adapted in either multiple or single resource sharing scenarios. Algorithms using these metrics for channel allocation problem in peer-to-peer wireless regional area network is proposed and simulated, and the results show that our metrics in conjunction with smart channel allocation strategies guarantee both the fairness and performances.

Recent research has shown that the cooperative diversity can efficiently combat the fading effects caused by the hostile wireless channels just as the multiple-input multiple-output (MIMO) antenna systems do. Besides, due to the inherent cost and size constraints by many small area wireless networks, e.g. wireless ad hoc networks and wireless sensor networks, in such environments, it would be almost impossible to deploy the antenna array in a mobile node. However, cooperative diversity can provide an economical and viable solution by sharing each mobile node’s single-antenna with others to form a virtual antenna array, while it still can maintain significant performance gains similar to MIMO systems. Meanwhile, the adaptive modulation is a proven technique which can significantly increase the spectral efficiency when perfect channel estimation can be achieved, and many wireless standards have adopted it as an implementation option. It also has been shown that combining these two techniques can greatly raise the system efficiency. Nevertheless, in the multiple nodes cooperative communication networks, the randomness of demodulation process at each relay node makes the task of correctly selecting the forwarding relay nodes very complicated. In view of this problem, a novel adaptive node selection algorithm is proposed for the destination node to effectively select the forwarding relay nodes to help the source node transfer the signals. In this paper, the decode-and-forward (DF) protocol is employed as the cooperative diversity signaling, and the constant-power variable-rate M-ary phase shift keying (M-PSK) is adopted as the adaptive modulation scheme. At the destination node, the maximal-ratio-combining (MRC) method is used to combine all of the signals from diversity branches with the main path transmitted directly from the source node. Because of the random phenomenon of the demodulation process, the outage requirement is used as the baseline in the first stage of the proposed node selection algorithm to choose the maximal transmission rate. Hence the maximal overall system throughput in terms of rate can be achieved. In the meantime, the first candidate forwarding sets of the relay nodes which collectively meet the required outage probability are also been chosen. Then in the second stage, we can further refine the number of the relay nodes among the first candidate forwarding sets by using the following criterion — maximum average rate per node. It will screen out the first candidate sets with the minimum number of the relay nodes. If only one candidate set is left at the end of the second stage, it will be the end of the proposed algorithm. However, if the result of the screening still has more than one set, then the final election among the second candidate sets would be the one which can give the minimum outage probability. In the simulation results, our proposed node selection algorithm can even further improve the spectral efficiency while the deleteriously fading effect induced by the wireless channels can also be effectively removed.

Discounted repeated games are currently being used to model the conflicts that arise between the nodes in a wireless network, such as distributed resource allocation, interference management or defending the network against attacks. In current literature, it is frequent that authors devise a specific strategy that performs well only for their concrete problem, thus, it would be desirable to have a generic algorithm that allows learning strategies for such games. However, current learning algorithms focus on average payoff repeated games, and we show analytically that there are important differences that prevent us from using such algorithms for discounted repeated games. In this work, we aim to fill this gap and we propose LEWIS, a lightweight, online learning algorithm specifically designed for these games, that deals with imperfect and incomplete information and is able to return a good payoff. We test LEWIS on two settings based on current literature problems to show that it has a good performance, hence, being a promising method to learn how to play a discounted repeated game in wireless networks.

In this paper, an efficient QoS aware resource allocation algorithm is proposed for wireless relay networks. This algorithm is yielded by considering G/G/1 queue for each subcarrier and using effective capacity concept to model the transmission delay in wireless relay networks. The objective is to minimize the transmission power considering support of QoS parameters. The constant user arrival rate is assumed throughout the paper. An extended Kalman filter also is used to estimate the residual times and vacation times of the relays and subcarriers. Based on the channel state information and queue state information, proposed algorithm assigns proper subcarriers to the relays. Then, the optimum subcarriers and relays are allocated for optimum power efficient transmission to the users.

We consider the problem of resource allocation for data transfer between the base station and the users within a cell of a wireless telecommunication network with infinite data queues for each user. The aim is to study the tradeoff between the conflicting objectives of maximizing the system throughput and the quality of service (QoS) to an individual user. Using a policy improvement approach based on Markov decision processes, we develop an intuitive and easy-to-implement index policy. We also demonstrate its superior performance over the existing proportional fair metric algorithm through simulation experiments.

In a wireless communications network, the movement of mobile users presents significant technical challenges to providing efficient access to the wired broadband network. In this paper, we construct a new analytical/numerical model that characterizes mobile user behavior and the resultant traffic patterns. The model is based on a semi-Markov process representation of mobile user behavior in a general state-space. Using a new algorithm for parameter estimation of a general Hidden Semi-Markov Model (HSMM), we develop an efficient procedure for dynamically tracking the parameters of the model from incomplete data. We then apply our integrated model to obtain estimates of the computational and bandwidth resources required at the wireless/wired network interface to provide high performance wireless Internet access and quality-of-service to mobile users. Finally, we develop a threshold-based admission control scheme in the wireless network based on the velocity information that can be extracted from our model.

Recently, cooperative communication is shown to be a promising approach to achieve spatial diversity. The performance improvement by cooperative communication heavily depends on selecting suitable relay node. Therefore, designing effective relay assignment algorithm becomes critical in wireless cooperative networks. Although many studies focus on relay assignment problem, none of them concerns the interference problem, produced by relay nodes. In this paper, we investigate how the interference impacts the relay assignment problem. In addition, we give a relay assignment algorithm with interference mitigation for cooperative communication.

This article addresses cooperative video summary delivery over wireless networks which inherits advantages from both cooperative communication and video summarization. In this work, a novel decode-process-and-forward (DPF) scheme is proposed for video summary transmission, where a relay node with video processing capability is involved to generate a concise version of the summary frame, called summary of summary (SoS). The SoS information is effectively consumed by the destination side to enhance its error concealment capability, leading to an improved video reconstruction quality. This article proposes a generic crosslayer optimization framework for cooperative video summary transmission, which jointly considers the source coding, relay processing parameters, power allocation, and error concealment strategy to achieve the best video quality. The problem is solved by using Lagrangian relaxation and dynamic programming. Experimental results show that the proposed scheme significantly outperforms existing cooperative and non-cooperative transmission schemes.

Future wireless systems are being designed for extremely high data rates and are directly contributing to the global energy consumption. This trend is undesirable not only due to the environmental concerns, but to cost as well since energy costs are becoming a significant part of the operating expenditures for the telecom operators. Recently, energy efficient wireless systems have become a new research paradigm. Cooperative communication has shown good potential in improving coverage, providing robust radio links, reducing infrastructure cost, and has the possibility to reduce the total system energy consumption. This paper looks at possible deployment strategies for wireless networks that can reduce the energy consumption. We look at the tradeoff between the number of relay nodes and the number of base stations and their implications on the total energy consumption of wireless networks. The obtained results show that adaptive resource allocation between the base station and the relay node is an efficient way of reducing the energy consumption of the system. Furthermore, this reduction in energy consumption increases with increasing the system target spectral efficiency.