A Novel Collaborative Partner Selection algorithm Based on Nash Bargaining Game and Hungarian Method for Wireless Networks

Mechanism for cooperative partner selection: Dual-factor theory perspective

Bandwidth allocation and admission control scheme based on Nash bargaining solution for WiMAX systems

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

Heterogeneous networks (HetNets) composed of macrocells and small cells are expected to improve the transmission performance of users significantly. Designing efficient resource allocation schemes, specifically, bandwidth allocation and power allocation schemes for HetNets is of particular importance for it may affect user quality of service (QoS) and network performance severely. In this paper, we consider the resource allocation problem of a HetNet composed of a macrocell and a number of femtocells and propose a resource characteristic and user QoS oriented bandwidth and power allocation algorithm for the femto base stations (FBSs) which share spectrum with the macro base station. By taking into account the service requirements of the femto user equipments (FUEs) and the bandwidth resource characteristics of the network, various bandwidth resource allocation schemes are proposed. Particularly, in the case of slightly insufficient bandwidth resource, we propose a bankruptcy game based bandwidth resource allocation algorithm for the FBSs, and solve the bandwidth allocation problem by means of Shapley value method. To stress the tradeoff between data rate and power consumption, we examine the energy efficiency of the FBSs and formulate the power allocation problem as a multi-objective optimization problem with the objectives of maximizing the energy efficiency of all the FBSs. The optimal power allocation strategy can be obtained by solving the optimization problem via ideal point method and generic algorithm. Simulation results demonstrate the efficiency of the proposed algorithm.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values.

Network function virtualization represents a revolutionary approach to network service deployment. This software-oriented approach for virtual network functions (VNFs) deployment enables more flexible and dynamic network services to meet diversified demands. To minimize the execution time of all VNFs in service function chains, VNF scheduling must be addressed. In this paper, we improve upon the flexible job-shop model by introducing the process of bandwidth allocation. First, we propose a multilayer encoding genetic algorithm to solve the VNF scheduling model. In addition, we design a distributed method for bandwidth allocation based on the Nash bargaining solution. Finally, by combining the genetic algorithm with distributed bandwidth allocation, we present a heuristic algorithm that solves the VNF scheduling problem in one stage. Using a multilayer encoding genetic algorithm, we simplify the constraints of the VNF scheduling problem and reduce its time complexity. At the same time, our Nash game solution refines the granularity of bandwidth allocation to further reduce the transmission delay between VNFs. The effectiveness of our proposed heuristic algorithm is verified through numerical evaluation. Compared with existing approaches, our method exhibits shorter scheduling time and reduces CPU time by 45% in simulated scenarios.

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits, e.g., data rates. In this paper, we propose a bargaining game to perform the power allocation for the selfish cooperative communication networks. In our system, two partner nodes can act as a source as well as a relay for each other, and each node is with an energy constraint to transmit one frame. Consider a selfish node is willing to seek cooperative transmission only if the data rate achieved through cooperation will not lower than that achieved through noncooperation by using the same amount of energy. The energy-efficient power allocation problem can be modeled as a cooperative game. We proved that there exists a unique Nash bargaining solution (NBS) for the game by verifying that the game is indeed a bargaining problem. Thus, the two objectives, i.e., system efficiency and user fairness specified in the selfish networks can be achieved. Simulation results show that the NBS scheme is efficient in that the performance loss of the NBS scheme to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.

This paper proposes a bargaining-based jammer power allocation scheme for multi-source multi-destination wireless network in the presence of a friendly jammer and a malicious node which eavesdrops erratically. We formulate the erratic behavior of the eavesdropper as a novel model where the eavesdropper wiretaps the message of the legitimate sources with a certain probability in a time slot. Moreover, in order to obtain a fair and efficient solution, the jammer power allocation problem is modeled as a Nash bargaining game under the constraint of maximum transmit power of a friendly jammer, which is a convex optimization problem. Then, the closed form of the Nash bargaining solution (NBS) is derived, and a simple but effective centralized algorithm is proposed. Besides, we find that the even power allocation solution and the sum-secrecy-rate optimal solution are the special cases of the NBS, when the bargaining power is properly selected. Simulation results demonstrate that the NBS achieves a good performance in terms of both effectiveness and fairness.

Military-civilian scientific and technological collaborative innovation (MCSTCI) is an important intersection of civil-military integrated development strategies and innovation-driven development strategies, and has become a brand-new model for Chinese key scientific and technological research. The selection of cooperative partner between military and civilian innovation entities is not only the primary segment to carry out MCSTCI activities, but also an important foundation for achieving complementary advantages and win-win cooperation. Therefore, on the basis of summarizing and combing the evaluation indicators, a partner selection model based on interval-valued intuitionistic fuzzy set multi-attribute decision-making is proposed. Firstly, we improve the traditional scoring function, and combine the new scoring function and entropy weight method to determine the weight of each evaluation indicator. Secondly, considering the impact of decision-makers’ risk attitudes, a method of transforming evaluation matrix based on hesitancy distribution is proposed. Finally, the grey correlation and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is used to mark and rank the candidates, and the innovation entities with the highest score is selected as the collaborative innovation partner. An example of a military equipment manufacturing enterprise in Beijing for partner selection in order to carry out joint technological research on a certain anti-ship missile weapon shows that the model is more comprehensive and flexible in solving fuzzy multi-attribute decision-making problems, and can assess and select cooperative partners for MCSTCI scientifically and objectively.

This paper proposes a cooperation strategy among rational nodes in a wireless cooperative relaying network as an effort to solve two basic problems, i.e., when to cooperate and how to cooperate. First, a symmetric system model comprising two users and an access point (AP) is presented. In this model, each user plays an equal role and acts as a source as well as a potential relay and has the right to decide the amount of bandwidth it should contribute for cooperation. Second, referring to the cooperative game theory, the above problems are formulated as a two-person bargaining problem. Then, a cooperation bandwidth allocation strategy based on the Nash bargaining solution is proposed, in which if a derived condition is satisfied, users will cooperatively work, and each will share a certain fraction of its bandwidth for relaying; otherwise, they will independently work. Simulation results demonstrate that when cooperation takes place, users benefit from the proposed strategy in terms of utility, and those with longer distance to the AP should spend more bandwidth to cooperate with others.

With the drastic increase of space information network (SIN) traffic and the diversity of network traffic types, the optimal allocation of the scarce network resources is of great significance for optimizing the SIN system capability. In this paper, we propose a multi-resource management method for multi-tier SIN using the cooperative Nash bargaining solution. Since the original problem is a non-convex problem, we firstly make logarithmic transition, and then find a tightest lower bound function to convert the initial problem into a convex one. In order to carry out the optimal bandwidth and power allocation in SIN, we construct a joint bandwidth and power allocation (JBPA) algorithm. Simulation results show the performance improvement of the JBPA scheme and the convergence of JBPA algorithm.

A review of VE integration-related literature reveals that although considerable research has been undertaken focusing on selection of cooperation partners, development of infrastructures (mechanisms and tools) to support VE management and coordination, insufficient attention has been devoted to the problem of creating the environment where these processes take place (i.e., the environment to enable an efficient and effective dynamic integration, offering strategies for dynamically align the virtual enterprise with business to support dynamic reconfiguration). The concept of A/VE we are addressing is broader, more embracing and more dynamic than the concepts of Virtual Enterprise or Extended Enterprise found in the literature, as these do not require the dynamic integration we defend for agility, and for which we propose the implementation of a Market of Resources. This chapter introduces the concept of a Market of Resources as an environment to cope with the A/VE model requirements (i.e., an environment for Agile/Virtual Enterprise integration and business alignment) identifying the relevantrequisites related with A/VE design and integration, and defining its participants. It also illustrates the technical requirements to support the Market of Resources and how exiting technologies support the main processes of the Market of Resources.Request access from your librarian to read this chapter's full text.

In relay-assisted cellular networks, when and how to cooperate for potential relay nodes is an important issue, particularly from the energy-aware perspective. Moreover, addressing the dynamic cooperative partner-selection problem with incomplete private information is a challenging issue, particularly in the competitive and changing environments. In this paper, we model the energy-aware dynamic competition of cooperative partner selection as an evolutionary game. Specifically, the replicator dynamics are applied to model the adaptation of strategic interactions while considering the dynamic nature in time dependence, and the evolutionarily stable strategy (ESS) is referred to as the robust solution of the proposed game. Moreover, we prove the uniqueness of the resulting ESS, guarantee the convergence to the ESS, and analyze the convergence behavior under the replicator dynamics from different perspectives. In practical terms, we further develop a cooperative partner-selection evolutionary algorithm based on the Q-learning approach, which only requires local knowledge for the potential relay nodes. In addition, we extend our observation by studying the dynamic cooperative partner selection with delay and discuss the properties of the resulting ESS.

In this study, the power control problem in cognitive radio networks with the spectrum underlay model is considered. The secondary users (SUs) can access the spectrum belonging to primary users (PUs) only if the interference imposed on the PUs is below a certain threshold. All network nodes are assumed to be equipped with multiple antennas. Using game theory approach and the primal decomposition method, a low complexity distributed algorithm based on Nash bargaining solution (NBS) is proposed for spectrum sharing and power allocation for multiple-input multiple output cognitive radio networks. The proposed algorithm is configured in two iterative algorithms, Jacobi and Gauss-Seidel algorithms, and their performance is investigated through simulations. The convergence of the proposed algorithm is further proved analytically. Simulation results show that proposed algorithm achieves an effective tradeoff between fairness and efficiency.