Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.

A Novel Resource Allocation Method in Ultra-Dense Network Based on Noncooperation Game Theory

In ultra-dense networks(UDNs), large-scale deployment of femto base stations is an important technique for improving the network throughput and quality of service(QoS). In this paper, a multidimensional resource allocation algorithm based on noncooperation game theory is proposed to manage the resource allocation in UDNs, including transmission point association, user channels, and power. The algorithm derives a multidimensional resource optimization model and converts into a noncooperation game model according to the analysis of transmission point association user channel and power allocation. The algorithm includes two phases: transmission point association, and channel and power allocation. Then, feasible domain and discrete variables relaxation approaches are introduced to derive an approximate optimal multidimensional resource allocation solution with low complexity. Simulation results show that this method has some advantages in suppressing interference and improves the overall system throughput, while ensuring the QoS of femtocell users.

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

An RVT-based power allocation method for dynamic LEO-MEO links

Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.

Multicell power allocation method based on game theory for inter-cell interference coordination

As a new technology, coordinated multipoint （CoMP） transmission is included in LTE-Advanced study item. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. Under this background, a novel power allocation game model is established to mitigate inter-cell interference with cellular coordination. In the light of cellular cooperation relationship and centralized control in eNodeB, the power allocation in each served antenna unit aims to make signal to interference plus noise ratio （SINR） balanced among inter-cells. Through the proposed power allocation game algorithm, the users＇ SINR can reach the Nash equilibrium, making it feasible to reduce the co-frequency interference by decreasing the transmitted power. Numerical results show that the proposed power allocation algorithm improves the throughput both in cell-center and cell-edge. Moreover, the blocking rate in cell-edge is reduced too.

Game Theoretic Analysis of Joint Rate and Power Allocation in Cognitive Radio Networks

Spectrum sharing is an essential enabling functionality to allow the coexistence between primary user (PU) and cognitive users (CUs) in the same frequency band. In this paper, we consider joint rate and power allocation in cognitive radio networks by using game theory. The optimum rates and powers are obtained by iteratively maximizing each CU’s utility function, which is designed to guarantee the protection of primary user (PU) as well as the quality of service (QoS) of CUs. In addition, transmission rates of some CUs should be adjusted if corresponding actual signal-to-interference-plus-noise ratio (SINR) falls below the target SINR. Based on the modified transmission rate for each CU, distributed power allocation is introduced to further reduce the total power consumption. Simulation results are provided to demonstrate that the proposed algorithm achieves a significant gain in power saving.

Coalitional game based resource allocation in D2D-enabled V2V communication

The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one feasible cellular user(FCU)can share its RB with multiple V2V pairs.The problem is first formulated as a nonconvex mixed-integer nonlinear programming(MINLP)problem with constraint of the maximum interference power in the FCU links.Using the game theory,two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection,where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation.The successive convex approximation(SCA)is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links.Finally,numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

Resource allocation scheme based on game theory in heterogeneous networks

As an effective solution for indoor coverage and service offioading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. This study investigates the resource block （RB） and power allocation in heterogeneous networks （HetNets）. Specifically, the concern here is to maximize the signal to interference-plus-noise ratio （SINR） of macrocell and energy efficiency of femtocell while providing the finite interference. In this paper, the system model is divided to two layers, in which the macro base station and clusters constitute the first layer network; femtocells in cluster make up the second layer network. Because of the different model structures, different game theories are used in different layers. Stackelberg game is used in the first layer, and non-cooperation game is used in the second layer. Meanwhile RB and power levels stand for the actions that are associated with each player in the game. The problem of resource allocation is formulated as a mixed integer programming problem. In order to minimize the complexity of the proposed algorithm, the resource allocation task is decomposed into two sub problems： a RB allocation and a power allocation. The result is compared with the traditional methods, the analysis illustrates the proposed algorithm has a better performance regarding SINR and energy efficiency of the heterogeneous networks.

Opportunistic admission and resource allocation for slicing enhanced IoT networks

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.

Game Theoretic Subcarrier and Power Allocation for Wireless OFDMA Networks

This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s payoff as a function of the achieved data-rate.The fairness resource allocation problem can then be modeled as a cooperative bargaining game.The objective of the game is to maximize the aggregate payoffs for the users.To search for the Nash bargaining solution(NBS) of the game,a suboptimal subcarrier allocation is performed by assuming an equal power allocation.Thereafter,an optimal power allocation is performed to maximize the sum payoff for the users.By comparing with the max-rate and the max-min algorithms,simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.

Energy efficient resource allocation in non-cooperative multi-cell OFDMA systems

A non-cooperative game is proposed to perform the sub-carrier assignment and power allocation for the multi-cell orthogonal frequency division multiple access（OFDMA） system.The objective is to raise the spectral efficiency of the system and prolong the life time of user nodes.This paper defines a game player as a cell formed by the unique base station and the served users.The utility function considered here measures the user＇s achieved utility per power.Each individual cell＇s goal is to maximize the total utility of its users.To search the Nash equilibrium（NE） of the game,an iterative and distributed algorithm is presented.Since the NE is inefficient,the pricing of user＇s transmission power is introduced to improve the NE in the Pareto sense.Simulation results show the proposed game outperforms the water-filling algorithm in terms of fairness and energy efficiency.Moreover,through employing a liner pricing function,the energy efficiency could be further improved.

Spectrum efficiency sub-carrier and energy efficiency power allocation for downlink multi-user CoMP in multi-cell system

Coordinated multi-point transmission/reception （CoMP） was proposed currently as an effective technology to improve cell-edge throughput in next-generation wireless systems. Most of the existing work discussed clustering methods mostly to maximize the edge user throughput while neglecting the problem of energy efficiency, such as those algorithm clustering base stations （BSs） of better channel condition and BSs of worse channel condition together. In addition, BSs usually increase the transmit power to achieve higher throughput without any considering of interference caused to other users, that may result in energy waste. The authors focus on the throughput maximizing problem while fully considering energy saving problem in CoMP systems. A coefficient is defined to describe the fitness of clusters. Then a sub-carrier allocation algorithm with clustering method is put forward for CoMP downlink, which can save the transmit power of BS and increase the throughput. Furthermore a power allocation scheme is proposed based on non-cooperation game; in which the transmit power is decreased by BSs generally to reach the Nash equation （NE）. Simulation shows that the proposed sub-carrier allocation scheme and power allocation algorithm are better than the existing ones on users＇ throughput while consumes much less energy.

Price-Based Power Allocation in Two-Tier Spectrum Sharing Heterogeneous Cellular Networks

Attributable to the using of the same spec-trum resources,heterogeneous cellular networks have serious interference problems,which greatly restricts the performance of the network.In this paper,the price-based power allocation for femtocells underlaying a macrocell heterogeneous cellular network is investigated.By ex-ploiting interference pricing mechanism,we formulate the interference management problem as a Stackelberg game and make a joint utility optimization of macrocells and femtocells.Specially,the energy consumption of macrocell users and the transmission rate utility of femtocell users are considered in this utility optimization problem.In the game model,the macrocell base station is regarded as a leader,which coordinates the interference from femtocell users to the macrocell users by pricing the inter-ference.On the other hand,the femtocell base stations are modelled as followers.The femtocell users obtain their power allocation by pricing.After proving the existence of the Stackelberg equilibrium,the non-uniform and uniform pricing schemes are proposed, and distributed interference pricing algorithm is proposed to address uniform interference price problem. Simulation results demonstrate that the proposed schemes are effective on interference management and power allocation.

Power allocation for MIMO-OFDM systems with multi-user decoupling and scheduling

A power allocation scheme for multi-user multiple-input multiple-output orthogonal frequency division multiplexing （MI- MO-OFDM） systems with channel state information （CSI） on transmitter and receiver is pressed. Multi-user lower allocation can be decoupled into single user lower allocation throughout null space mapping of multi-user channel and lower allocation can be performed throughout spatial-spectral water-filling for per user.To deal with more users in system and fading correlation,scheduling is oerformed to maintain the gain of power allocation.The proposed scheme can substantially improve system＇s spectral efficiency with low complexity.Simulation results validate the accuracy of theoretic analyses.

Resource Allocation for Space-Air-Ground Integrated Networks:A Comprehensive Review

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

Bargaining Game Theoretic Power Control in Selfish Cooperative Relay Networks

Wireless cooperative communications require appropriate power allocation （PA） between the source and relay nodes. In selfish cooperative communication networks, two partner user nodes could help relaying information for each other, but each user node has the incentive to consume his power solely to decrease its own symbol error rate （SER） at the receiver. In this paper, we propose a fair and efficient PA scheme for the decode-and-forward cooperation protocol in selfish cooperative relay networks. We formulate this PA problem as a two-user cooperative bargaining game, and use Nash bargaining solution （NBS） to achieve a win-win strategy for both partner users. Simulation results indicate that the NBS is fair in that the degree of cooperation of a user only depends on how much contribution its partner can make to decrease its SER at the receiver, and efficient in the sense that the SER performance of both users could be improved through the game.

Capacity Allocation of Hybrid Power System with Hot Dry Rock Geothermal Energy,Thermal Storage,and PV Based on Game Approaches

This study utilizes hot dry rock(HDR)geothermal energy,which is not affected by climate,to address the capacity allocation of photovoltaic(PV)-storage hybrid power systems(HPSs)in frigid plateau regions.The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage(TS)plant.An HPS consisting of an HDR plant,a TS plant,and a PV plant is proposed.Game approaches are introduced to establish the game pattern model of the proposed HPS as the players.The annualized income of each player is used as the payoff function.Furthermore,non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS.Finally,the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin,Qinghai,China as a case study.The results show that in the proposed non-cooperative game approach,the players focus only on the individual payoff and neglect the overall system optimality.The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player.Thereby,the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity(EFC)of PV plants,which in turn indirectly guarantees the reliability of grid operation.

面向密集场景的空天地网络资源分配算法

空天地网络具有覆盖范围大、吞吐量高、弹性强等优点。该文针对大量用户并发接入、网络负载不均衡所引发的网络拥塞、服务质量恶化等问题,提出一种面向密集场景的资源分配算法。首先以用户需求为中心,根据不同类型用户任务的偏好来构建用户效用函数,然后基于匹配博弈的网络选择算法和结合对偶上升法的功率控制算法来实现负载均衡,优化资源分配方案。实验表明,相较于传统策略,所提策略整体用户接入率至少提高35%,时延和吞吐量方面性能提升超过50%;在密集场景下,能更有效地均衡负载,提升网络性能。

超密集网络中基于多基站博弈均衡的分布式无线资源管理算法

移动边缘计算和超密集网络技术在扩大移动设备计算能力和增加网络容量方面有明显的优势.然而,在两者融合的场景下,如何有效降低基站之间的同信道干扰,减少任务传输的时延和能耗是一个重要研究课题.本文设计了一个基于多基站博弈均衡的分布式无线资源管理算法.将小基站之间的无线资源管理问题转化为博弈问题,提出一种基于奖励驱动的策略选择算法.基站通过迭代不断更新其策略的选择概率,最终优化子信道分配和发射功率的调控.仿真结果表明,我们的算法在提高信道利用率和降低任务处理的时延和能耗方面具有优势.

A bargaining game theoretic method for virtual resource allocation in LTE-based cellular networks

In this paper, we study the virtual resource(VR) allocation problem in LTE-based wireless network virtualization(WNV). A practical network scenario, where multiple virtual wireless service providers(WSPs)request the VR from a unique mobile network operator(MNO) is considered. Our objective is two folds. The first is to guarantee the minimum rate requirements of the MNO and the WSPs. The second is to distribute the system rate among the MNO and the WSPs in the Pareto optimal manner. To this end, an efficient VR allocation scheme based on bargaining game theory is proposed, and the Nash bargaining solution(NBS) method is used to solve the proposed game problem. The proposed game problem is proved to be a convex optimization problem. By using standard convex optimization method, the global optimal NBS of the game is obtained in closed form. The effectiveness of the proposed VR allocation game is testified through numerical results.