An improving energy efficiency cooperation algorithm based on Nash bargaining solution in selfish user cooperative networks

A bandwidth-exchange cooperation algorithm based on the Nash bargaining solution （NBS） is proposed to encourage the selfish users to participate with more cooperation so as to improve the users＇ energy efficiency. As a result, two key problems, i.e. , when to cooperate and how to cooperate, are solved. For the first problem, a proposed cooperation condition that can decide when to cooperate and guarantee users＇ energy efficiency achieved through cooperation is not lower than that achieved without cooperation. For the second problem, the cooperation bandwidth allocations （CBAs） based on the NBS solve the problem how to cooperate when cooperation takes place. Simulation results show that, as the modulation order of quadrature amplitude modulation （QAM） increases, the cooperation between both users only occurs with a large signal-to-noise ratio （SNR）. Meanwhile, the energy efficiency decreases as the modulation order increases. Despite all this, the proposed algorithm can obviously improve the energy efficiency measured in bits-per-Joule compared with non-cooperation.

Optimal Time and Bandwidth Allocation Based on Nash Bargaining Solution for Cooperative Cognitive Systems

In this paper,we propose a cooperative spectrum sharing strategy based on the Nash bargaining solution.Specifically,the primary system leases a fraction of its transmission time to the secondary system in exchange for cooperation to improve its transmission performance.To gain access to the spectrum of the primary system,the secondary system needs to split a fraction of its transmission bandwidth to help to forward the primary signal.As a reward,the secondary system can use the remaining bandwidth to transmit its own signal.We find a unique solution for this time and bandwidth allocation using the Nash bargaining solution.Simulation results demonstrate that the performance of the primary and secondary systems can both be improved by the proposed spectrum sharing strategy.

Multi-user Symmetric Cooperation Based on Nash Bargaining Solution in Cooperative Relay Networks

This paper presents a symmetric cooperation strategy for cooperative relay networks with multiple users. The multi-user symmetric cooperation model and the relay selection algorithm are proposed. Then, the time slot allocation problem is cast into a bargaining problem, and the optimal time slot allocation solution is obtained by Nash bargaining solution （NBS）. Moreover, we also consider the implementations of the cooperation strategy, i.e., the grouping and admission control algorithm. Simulation results show that users can obtain larger rates under the symmetric cooperation strategy than the non-cooperative case.

A novel mechanism based on Nash bargaining for primary system game in cognitive radios

According to the utility function and spectrum demand of the cognitive users,a novel mechanism based on Nash bargaining for primary system game was proposed under the wireless environment of Rayleigh fading.On the basis of this mechanism,we proposed a new distributed bargaining algorithm based on Nash product;then the spectrum prices and system utilities were obtained.Theoretical analysis results showed that with a close total utility to the optimal,the Nash bargaining mechanism cannot only improve the fairness between primary systems remarkably,but also reach to the stable equilibrium in finitely repeated games.Finally,simulation results were given to demonstrate the correctness of these conclusions and the efficiency of the algorithm.

Ordering policies and coordination in a twoechelon supply chain with Nash bargaining fairness concerns

This paper investigates the ordering policies of two competitive retailers,and the coordination status of a two-echelon supply chain by considering the fairness concerns of channel members.We consider that two retailers compete with each other over price,where overstock and shortage are allowed.We assume that the demand is stochastic and considered with additive form.First,based on the Nash bargaining fairness reference point,we obtain the optimal decisions of the fairness-concerned channel members in both the centralized and the decentralized cases using a two-stage game theory.Secondly,we analyze the coordination status of the supply chain with Nash bargaining fairness concerns using ideas of optimization.Finally,numerical experiments are used to illustrate the influence of some parameters,the fairness-concerned behavioral preference of the channel members on the optimal decisions and the coordination status of supply chain.Some managerial insights are obtained.

A COMPARISON OF A SIMPLE PROCUREMENT AUCTION AND GENERALIZED NASH BARGAINING

This paper presents a comparison mechanism for a simple procurement auction and generalized Nash bargaining solution in a procurement circumstance. The buyer who hopes to fulfill a project has two options. First, she may award the project to the old provider with an efficient quality by bargaining Second, she may give the project to the firm who submits the lowest bid through a simple procurement second-price sealed-bid auction with a pre-fixed quality. We compare the expected revenue in the auction with the deterministic profit of the bargaining. In different parameters cases, we find the borderline functions between the two options with respect to the buyer＇s bargaining power and the amount of bidders. We also compare our simple procurement auction （SPA） with the optimal multidimensional bid auction by Che （1993）, and find our model protects the buyer＇s private valuation information by forfeiting some profits. Some properties of the difference between the two auctions are proposed, especially there is no revenue difference when the amount of bidders approaches infinity.

Nash bargaining solution based multi-cell and multi-user interference alignment scheme for cellular networks

In wireless cellular networks, the interference alignment （IA） is a promising technique for interference management. A new IA scheme for downlink cellular network with multi-cell and multi-user was proposed, in the proposed scheme, the interference in the networks is divided into inter-cell interference （ICI） among cells and inter-user interference （IUI） in each cell. The ICI is aligned onto a multi-dimensional subspace by multiplying the ICI alignment precoding matrix which is designed by the singular value decomposition （SVD） scheme at the base station （BS） side. The aligned ICI is eliminated by timing the interference suppression matrix which is designed by zero-forcing （ZF） scheme at the user equipment （UE） side. Meanwhile, the IUI is aligned by multiplying the IUI alignment precoding matrix which is designed based on Nash bargaining solution （NBS） in game theory. The NBS is solved by the particle swarm optimization （PSO） method. Simulations show that, compared with the traditional ZF IA scheme, the proposed scheme can obtain higher data rate and guarantee the data rate fairness of UEs with little additional complexity.

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.

Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy

As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of entities.In this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power plants.We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory.This model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining problem.The distributed alternating direction multiplier method(ADMM)is employed to address the subproblems separately.Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating entity.The results confirm the practicality and effectiveness of the strategy.

Distributed Source-Load-Storage Cooperative Low-Carbon Scheduling Strategy Considering Vehicle-to-Grid Aggregators

The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage systems.However,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation.To unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators.The uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs.Moreover,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon.This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs.Subsequently,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the system.This model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully preserved.The proposed strategy is proven to achieve the objective of low-carbon economic operation.

UAV rendezvous based on Nash bargain

Purpose–The purpose of this paper is to find a solution for the unmanned aerial vehicle(UAV)rendezvous problem,which should be feasible,optimal and not time consuming.In the existing literatures,the UAV rendezvous problem is always presented as a matter of simultaneous arrival.They focus only on the time consistency.However,the arrival time of UAVs can vary according to the rendezvous position.The authors should determine the best rendezvous position with considering UAVs’maneuver constraint,so that UAVs can construct a formation in a short time.Design/methodology/approach–The authors present a decentralized method in which UAVs negotiate with each other for the best rendezvous positions by using Nash bargain.The authors analyzed the constraints of the rendezvous time and the UAV maneuver,and proposed an objective function that allows UAVs to get to their rendezvous positions as fast as possible.Bezier curve is adopted to generate smooth and feasible flight trajectories.During the rendezvous process,UAVs adjust their speed so that they can arrive at the rendezvous positions simultaneously.Findings–The effectiveness of the proposed method is verified by simulation experiments.The proposed method can successfully and efficiently solve the UAV rendezvous problem.Originality/value–As far as the authors know,it is the first time Nash bargain is used in the UAV rendezvous problem.The authors modified the Nash bargain method and make it distributed,so that it can be computed easily.The proposed method is much less consuming than ordinary Nash bargain method and ordinary swarm intelligence based methods.It also considers the UAV maneuver constraint,and can be applied online for its fast calculation speed.Simulations demonstrate the effectiveness of the proposed method.

Calculation Model and Allocation Strategy of Network Usage Charge for Peer-to-peer and Community-based Energy Transaction Market

The emergence of prosumers in distribution systems has enabled competitive electricity markets to transition from traditional hierarchical structures to more decentralized models such as peer-to-peer(P2P)and community-based(CB)energy transaction markets.However,the network usage charge(NUC)that prosumers pay to the electric power utility for network services is not adjusted to suit these energy transactions,which causes a reduction in revenue streams of the utility.In this study,we propose an NUC calculation method for P2P and CB transactions to address holistically economic and technical issues in transactive energy markets and distribution system operations,respectively.Based on the Nash bargaining(NB)theory,we formulate an NB problem for P2P and CB transactions to solve the conflicts of interest among prosumers,where the problem is further decomposed into two convex subproblems of social welfare maximization and payment bargaining.We then build the NUC calculation model by coupling the NB model and AC optimal power flow model.We also employ the Shapley value to allocate the NUC to consumers fairly for the NUC model of CB transactions.Finally,numerical studies on IEEE 15-bus and 123-bus distribution systems demonstrate the effectiveness of the proposed NUC calculation method for P2P and CB transactions.

A Two-stage Autonomous EV Charging Coordination Method Enabled by Blockchain

Increasing electric vehicle(EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator(DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints,and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers(ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.

Security Constrained Decentralized Peer-to-Peer Transactive Energy Trading in Distribution Systems

Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.

OUTSOURCE TO AN OEM OR AN ODM？ PROFITABILITY AND SUSTAINABILITY ANALYSIS OF A FASHION SUPPLY CHAIN

Consider a fashion supply chain comprising a supplier, a contract manufacturer and a fashion brand, we examine the fashion brand＇s profit performances when the contract manufacturer is either an OEM （having no design capability） or an ODM （having design capability）. Regarding OEM, the fashion brand designs the products, outsources the manufacturing function, and has the option of outsourcing procurement function. Regarding ODM, the fashion brand buys unlabeled products from the ODM, which is charge of designing and manufacturing. In this case, buy-back contract is widely adopted so as to share the risk of demand uncertainty between the ODM and the fashion brand. We solve the wholesale pricing problems via sequential/simultaneous optimization, and derive the buy-back price via generalize Nash bargaining. We find that, fashion brand prefers contracting with an ODM when its bargaining power in buy-back negotiation is larger than a threshold, although the fashion brand＇s order size under ODM is always larger than that under OEM. Interestingly, we find that the buy-back price is decreasing in the fashion brand＇s bargaining power. We further analyze the supply chain sustainability in both ODM and OEM scenarios, fmding that the supply chain might achieve both environmental sustainability and economic sustainability in OEM scenario when the fashion brand＇s bargaining power in buy-back negotiation is small.

Bandwidth Allocation with Minimum Rate Constraints in Cluster-based Femtocell Networks

Inter-femtocell interference becomes serious when femtocells are densely deployed. To mitigate the inter-femtocell interference, this paper proposes a cluster-based bandwidth allocation algorithm. We create femtocell clusters by constructing a weighted interference graph and allocate bandwidth to each cluster based on a Nash bargaining solution(NBS). Simulation results show that the cluster-based bandwidth allocation algorithm can reduce the inter-femtocell interference and meet the minimum rate constraint of each cluster.

Cooperative mechanism on a three-echelon supply chain with remanufacturing outsourcing

This paper studies the cooperative mechanism for a three-echelon supply chain with remanufacturing outsourcing comparing a supplier,a manufacturer,and a third-party remanufacturer,wherein we take the relative fairness concerns into consideration.The Stackelberg game theory is introduced to analyze the best values for the supply chain and each member.Nash bargaining solution is used as the relative fairness-concerned reference to discuss the corresponding optimal solutions of these models.By determining and comparing the equilibrium solutions across the five models,we discover that given the Nash bargaining fairness-concerned behavior,the system profits in the completely decentralized and three cooperative scenarios are lower than they are for products in the completely centralized decision model.The results show that in the centralized channel,the optimal profit and market demand in the three-echelon supply chain are maximized.Furthermore,it turns out that a cooperative mechanism can bring great benefits to its performance.

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.

Learning and Efficiency with Search Frictions

This paper studies the allocative efficiency in a Moscarini （2005）-type equilibrium search environment with learning. It is shown that the stationary equilib- rium is efficient if and only if the Hosios condition holds no matter whether learning is about finn-specific human capital or about general human capital. However, the stationary equilibrium can never be efficient if externalities exist from unemployment. In contrast, even with externalities, the stationary equilibrium can be efficient under some modified Hosios condition if there is no uncertainty （standard Mortensen and Pissarides （1994）-type equilibrium search environment）. The key intuition is that the equilibrium can only be efficient if firm-worker matching is formed and terminated efficiently.

How to Implement the Wholesale Price Contract: Considering Competition between Supply Chains

In this paper,we develop the price competition model of two supply chains,in which each supply chain includes one core manufacturer and one retailer,respectively.The manufacturer in each supply chain sells products to the retailer through a commonly-used wholesale price contract.Each manufacturer has two options to implement the wholesale price contract:playing the Stackelberg game with the retailer and playing the bargaining game with the retailer.Based on the manufacturer's two alternative performing modes in each supply chain,we consider four combined performing modes of two competitive supply chains in the model.By comparing equilibrium results,we find that when both manufacturers choose to bargain with retailers,the sales volume increases and the sales price decreases.Moreover,the manufacturers'mode option is affected by bargaining power,product quality level,and the cost of improving product quality.Specifically,when both bargaining power and the cost of improving product quality are relatively small,both manufacturers choose to play Stackelberg game with retailers.When manufacturers'bargaining power is sufficiently large,regardless of the cost of improving product quality,both manufacturers choose to bargain with retailers.Surprisingly,when the manufacturer chooses to bargain the wholesale price with the retailer,higher product quality is not always beneficial to the retailer because the retailer may have to share part of the cost of the manufacturer.