Non-cooperative Game Theoretic Approach Research Articles

Energy Splitting for Swipt in Qos-Constraint Mtc Network: A Non-Cooperative Game Theoretic Approach

This paper studies the emerging wireless energy harvesting algorithm dedicated for machine type communication (MTC) in a typical cellular network where one transmitter (e.g. the base station, a hybrid access point) with constant power supply communicates with a set of users (e.g. wearable devices, sensors). In the downlink direction, the information transmission and power transfer are conducted simultaneously by the base station. Since MTC only transmits several bits control signal in the downlink direction, the received signal power can be split into two parts at the receiver side. One is used for information decoding and the other part is used for energy harvesting. Since we assume that the users are without power supply or battery, the uplink transmission power is totally from the energy harvesting. Then, the users are able to transmit their measured or collected data to the base station in the uplink direction. Game theory is used in this paper to exploit the optimal ratio for energy harvesting of each user since power splitting scheme is adopted. The results show that this proposed algorithm is capable of modifying dynamically to achieve the prescribed target downlink decoding signal-to-noise plus interference ratio (SINR) which ensures the high reliability of MTC while maximizing the uplink throughput.

Wireless powered Public Safety IoT: A UAV-assisted adaptive-learning approach towards energy efficiency

Public Safety Networks (PSN) provide resilient communication paradigms under disaster recovery scenarios. In this context, the increased integration of Internet of Things (IoT) architectures can further support critical and massive information flows. In this paper, we propose a framework that combines Unmanned Aerial Vehicle (UAV)-support with wireless powered communication (WPC) techniques to further improve energy efficiency in a distributed non-orthogonal multiple access (NOMA) PSN. The IoT devices form coalitions by initially choosing their role (coalition head or coalition member) in the network independently and in a distributed fashion, following the theory of Minority Games (MG). Subsequently, the member nodes act as stochastic learning automata to associate with a coalition head using a reinforcement learning technique. Towards extending the PSN's lifetime, we utilize a harvest-transmit-store WPC mechanism, where the IoT nodes harvest energy from the mobile UAV before transmitting their information. The UAV optimal positioning in the Euclidean 3D space is determined through an optimization problem of maximizing the coalition head's total energy availability, as these nodes play a critical role within the PSN acting as emergency gateways. Finally, a non-cooperative game-theoretic approach is adopted to determine the optimal uplink transmission power of each IoT node in a distributed manner and the existence of a unique Nash equilibrium is shown. The performance evaluation of the proposed framework is achieved via modeling and simulation, and the numerical results demonstrate its energy efficiency, robustness, and scalability.

Investigating the Social Efficiency of Merchant Transmission Planning Through a Non-cooperative Game-Theoretic Framework

Merchant transmission planning is considered as a further step toward the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare.

Game theoretic frequency reuse approach in OFDMA femtocell networks

AbstractIn this paper, we initially dealt with the issue of spectrum allocation among macro (or “licensed”) and femto (or “unlicensed”) users in an orthogonal frequency division multiple access femtocell network of noncooperative game theoretic frequency reuse approach. We formulate the difficulty based on spectrum bidding. Here, individual secondary users (SUs) create an auction for the amount of bandwidth and every primary user can share the frequency band among SUs by itself according to the intelligence from SUs without lowering its own performance. Here, we consider that the bidding is a noncooperative game and one of its solutions is a Nash equilibrium. The femto base stations are grouped into different cluster for mitigating the undesired interference among them. The game theoretical method deals with the intercluster frequency clashes. We exemplified a link between utility function and the number of players by noncooperative game theoretic approach to guide the spectrum sharing decision at the cell edges. The convergence of the development mechanism is rigorously scrutinized and extensive numerical outcomes are presented to illustrate their potential merits.

Incomplete CSI Based Resource Optimization in SWIPT Enabled Heterogeneous Networks: A Non-Cooperative Game Theoretic Approach

Heterogeneous small cell network 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 a heterogeneous small cell network. 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 heterogeneous small cell network 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 total interferences experienced by users on each subchannel. Iterative algorithms of power optimization and subchannel allocation are proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in the heterogeneous small cell network.

A game-theoretical approach for modeling competitions in a maritime supply chain

ABSTRACTTo accommodate the structural changes in logistics, competitions along a maritime supply chain are more intensive than before. This paper adopts a two-stage noncooperative game-theoretical approach to model the horizontal and vertical interactions among liners and ports. In the first stage, Bertrand game is applied to model the interactions between two liners. When the shipping line decides which port to call, a multinomial Logit model is applied. In the second stage, Bertrand game is applied to model the competitions between two ports. Nash equilibrium is derived by solving the Bertrand games. A numerical example is provided as a case study.

An Energy-Efficient Resource Allocation and Interference Management Scheme in Green Heterogeneous Networks Using Game Theory

In heterogeneous networks (HetNets), energy-efficient resource allocation and intercell-interference management are important issues. In this paper, we address these issues using a two-level dynamic scheme. First, we assign the MUs with the optimum number of subchannels that achieves an operator's required balance between macro users' satisfaction and maximization of network efficiency. Then, the remaining subchannels are left to be shared by a number of small cells. In the latter step, a transmit power adaptation method using a noncooperative game-theoretic approach is developed to reduce cochannel interference in the whole network. The problem is formulated by allowing multiple neighboring small cells to share each subchannel (i.e., universal frequency reuse in the small cell level). We fully characterize the pricing factor in the penalty part of the utility function. The existence and uniqueness of the Nash equilibrium (NE) are analyzed and proved. Then, a distributed iterative algorithm based on the fixed-point theorem is proposed to attain the equilibrium of the game. Simulation results are presented to show the effectiveness of the proposed scheme in different network topologies.

Strategic exploration of battery waste management: A game-theoretic approach.

Electronic waste or e-waste is the fastest growing stream of solid waste today. It contains both toxic substances as well as valuable resources. The present study uses a non-cooperative game-theoretic approach for efficient management of e-waste, particularly batteries that contribute a major portion of any e-waste stream and further analyses the economic consequences of recycling of these obsolete, discarded batteries. Results suggest that the recycler would prefer to collect the obsolete batteries directly from the consumer rather than from the manufacturer, only if, the incentive return to the consumer is less than 33.92% of the price of the battery, the recycling fee is less than 6.46% of the price of the battery, and the price of the recycled material is more than 31.08% of the price of the battery. The manufacturer's preferred choice of charging a green tax from the consumer can be fruitful for the battery recycling chain.

Energy Efficiency and Interference Neutralization in Two-Hop MIMO Interference Channels

The issue of energy-aware resource allocation in an amplify-and-forward (AF) relay-assisted multiple-antenna interference channel (IC) is considered. A novel interference neutralization (IN) scheme is proposed for relay design and, based on the IN relay matrix design, two algorithms are developed to jointly allocate the users' transmit powers, beamforming (BF) and receive filters. The first algorithm considers a competitive scenario and employs a noncooperative game-theoretic approach to maximize the individual energy efficiency (EE) of each communication link, defined as the ratio of the achievable rate over the consumed power. The resulting algorithm converges to a unique fixed point, has limited complexity, and can be implemented in a distributed fashion. The second algorithm employs fractional programming tools and sequential convex optimization to centrally allocate the users' transmit powers, BF, and receive filters for global energy efficiency (GEE) maximization. The resulting algorithm is guaranteed to converge and has limited computational complexity. Numerical results show that the competitive IN design achieves virtually the same performance as the cooperative design if IN is feasible, while the gap is small if perfect IN is not achievable.

A distributed demand-side management framework for the smart grid

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios.We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand.

A Non-cooperative Game Theoretic Approach to Energy-efficient Power Control in Wireless Sensor Networks

Aiming at the limited energy characteristics of wireless sensor networks, we apply game theory to solve the power control problem to reduce energy consumption in wireless sensor networks. In this paper, a distributed power control algorithm based on non-cooperative game theory under incomplete information is proposed, which adopts Signal-to-Interference Noise Ratio (SINR) as utility function. The purpose of power control algorithm for noncooperative game is to achieve the largest utility by optimal power control strategy, thus improve the total network energy efficiency. Moreover, Bayesian Nash equilibrium theorem is introduced to study the existence and uniqueness proof of Nash equilibrium algorithm. Simulation results show that there exist points for each of the cost functions considered, which give the maximum net utility given the strategies taken by all other nodes as fixed. And the proposed algorithm is efficient and can achieve better performance.

Energy-Aware Competitive Power Control in Relay-Assisted Interference Wireless Networks

Competitive power control for energy efficiency maximization in wireless interference networks is addressed, for the scenarios in which the users' SINR can be expressed as either (a) γ = (αp)/(φp + ω), or (b) γ = (αp + βp <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )/(φp + ω), with p the user's transmit power. The considered SINR expressions naturally arise in relay-assisted systems. The energy efficiency is measured in bit/Joule and is defined as the ratio of a proper function of the SINR, divided by the consumed power. Unlike most previous related works, in the definition of the consumed power, not only the transmit power, but also the circuit power needed to operate the devices is accounted for. A non-cooperative game theoretic approach is employed and distributed power control algorithms are proposed. For both SINR expressions (a) and (b), it is shown that the competitive power allocation problem always admits a Nash equilibrium. Moreover, for the SINR (a), the equilibrium is also shown to be unique and the best-response dynamic is guaranteed to converge to such unique equilibrium. For the two-user case, the efficient computation of the Pareto frontier of the considered game is addressed, and, for benchmarking purposes, a social optimum solution with fairness constraint is derived.

Analysis of cognitive radio scenes based on non-cooperative game theoretical modelling

A non-cooperative game theoretical approach for analysing opportunistic spectrum access (OSA) in cognitive radio (CR) environments is proposed. New concepts from evolutionary game theory are applied to spectrum access analysis in order to extract rules of behaviour for an emerging environment. In order to assess OSA scenarios of CRs, two oligopoly game models are reformulated in terms of resource access: Cournot and Stackelberg games. Five CR scenes are analysed: simultaneous access of unlicensed users (‘commons regime’) with symmetric and asymmetric costs, with and without bandwidth constraints and sequential access (‘licensed against unlicensed’). Several equilibrium concepts are studied as game solutions: Nash, Pareto and the joint Nash–Pareto equilibrium. The latter captures a game situation where players are non-homogeneous users, exhibiting different types of rationality – Nash and Pareto. This enables a more realistic modelling of interactions on a CR scene. An evolutionary game equilibrium detection method is used. The Nash equilibrium indicates the maximum number of channels a CR may access without decreasing its payoff. The Pareto equilibrium describes a larger range of payoffs, capturing unbalanced as well as equitable solutions. The analysis of the Stackelberg modelling shows that payoffs are maximised for all users if the incumbents are Nash oriented and the new entrants are Pareto driven.

The optimal quantity discount that a supplier should offer

Quantity discounts are a useful mechanism for coordination. Here we investigate such discounts from the supplier's perspective, both from a non-cooperative game-theoretical approach and a joint decision model. Taking into account the price elasticity of demand, this analysis aids a sole supplier in establishing an all-unit quantity discount policy in light of the buyer's best reaction. The Stackelberg equilibrium and Pareto optimal solution set are derived for the non-cooperative and joint-decision cases, respectively. Our research indicates that channel efficiency can be improved significantly if the quantity discount decision is made jointly rather than non-cooperatively. Moreover, we extend our model in three directions: (1) the product is transported by a private fleet; (2) the buyer may choose to offer her customers a different percentage discount than that she obtained from the supplier; and (3) the case of heterogeneous buyers. Numerical case studies are employed throughout the paper to illustrate the practical applications of the models presented and the sensitivity to model parameters.

A non-cooperative game-theoretic approach to channel assignment in multi-channel multi-radio wireless networks

Channel assignment in multi-channel multi-radio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to consider the interference characteristics of the nodes in the network especially when nodes are in different collision domains. This work views the problem of channel assignment in multi-channel multi-radio networks with multiple collision domains as a non-cooperative game where the objective of the players is to maximize their individual utility by minimizing its interference. Necessary and sufficient conditions are derived for the channel assignment to be a Nash Equilibrium (NE) and efficiency of the NE is analyzed by deriving the lower bound of the price of anarchy of this game. A new fairness measure in multiple collision domain context is proposed and necessary and sufficient conditions for NE outcomes to be fair are derived. The equilibrium conditions are then applied to solve the channel assignment problem by proposing three algorithms, based on perfect/imperfect information, which rely on explicit communication between the players for arriving at an NE. A no-regret learning algorithm known as Freund and Schapire Informed algorithm, which has an additional advantage of low overhead in terms of information exchange, is proposed and its convergence to the stabilizing outcomes is studied. New performance metrics are proposed and extensive simulations are done using Matlab to obtain a thorough understanding of the performance of these algorithms on various topologies with respect to these metrics. It was observed that the algorithms proposed were able to achieve good convergence to NE resulting in efficient channel assignment strategies.

Network formation under mutual consent and costly communication

We consider two different approaches to describe the formation of social networks under mutual consent and costly communication. First, we consider a network-based approach; in particular Jackson–Wolinsky’s concept of pairwise stability. Next, we discuss a non-cooperative game-theoretic approach, through a refinement of the Nash equilibria of Myerson’s consent game. This refinement, denoted as monadic stability, describes myopically forward looking behavior of the players. We show through an equivalence that the class of monadically stable networks is a strict subset of the class of pairwise stable networks that can be characterized fully by modifications of the properties defining pairwise stability.

Crystallized Rate Regions for MIMO Transmission

When considering the multiuser SISO interference channel, the allowable rate region is not convex and the maximization of the aggregated rate of all the users by the means of transmission power control becomes inefficient. Hence, a concept of the crystallized rate regions has been proposed, where the time-sharing approach is considered to maximize the sumrate. In this paper, we extend the concept of crystallized rate regions from the simple SISO interference channel case to the MIMO/OFDM interference channel. As a first step, we extend the time-sharing convex hull from the SISO to the MIMO channel case. We provide a non-cooperative game-theoretical approach to study the achievable rate regions, and consider the Vickrey-Clarke-Groves (VCG)mechanism design with a novel cost function. Within this analysis, we also investigate the case of OFDM channels, which can be treated as the special case of MIMO channels when the channel transfer matrices are diagonal. In the second step, we adopt the concept of correlated equilibrium into the case of two-user MIMO/OFDM, and we introduce a regret-matching learning algorithm for the system to converge to the equilibrium state. Moreover, we formulate the linear programming problem to find the aggregated rate of all users and solve it using the Simplex method. Finally, numerical results are provided to confirm our theoretical claims and show the improvement provided by this approach.

Pareto-Efficient and Goal-Driven Power Control in Wireless Networks: A Game-Theoretic Approach With a Novel Pricing Scheme

A Pareto-efficient, goal-driven, and distributed power control scheme for wireless networks is presented. We use a non-cooperative game-theoretic approach to propose a novel pricing scheme that is linearly proportional to the signal-to-interference ratio (SIR) and analytically show that with a proper choice of prices (proportionality constants), the outcome of the noncooperative power control game is a unique and Pareto-efficient Nash equilibrium (NE). This can be utilized for constrained-power control to satisfy specific goals (such as fairness, aggregate throughput optimization, or trading off between these two goals). For each one of the above goals, the dynamic price for each user is also analytically obtained. In a centralized (base station) price setting, users should inform the base station of their path gains and their maximum transmit-powers. In a distributed price setting, for each goal, an algorithm for users to update their transmit-powers is also presented that converges to a unique fixed-point in which the corresponding goal is satisfied. Simulation results confirm our analytical developments.

A Non-cooperative Game theoretic Approach to Dust and Sand Storm in North East Asia

A Non-cooperative Game theoretic Approach to Dust and Sand Storm in North East Asia

Business Acquisition - A Non-Cooperative Game Theoretic Approach

Game theory is an intuitive tool that has long been used in defining the business strategies in Corporate America. This paper posits the applicability of the game theory framework to a bidding war concerning an asset sale. The paper treats a bankruptcy auction process as a non-cooperative game and argues that game theory can be used to predict the winner of the auction. Using the example of the dog fight between rivals in the airline industry scrambling over the carcass of an ailing competitor to gain control of a strategically important market, the analysis proves that it would be the market leader that would have gained the most from the transaction and would be willing to pay a premium for assets that in the hands of competition could only harm its leading position in the highly competitive industry. A specific example has been used to portray similar situations when it may be sensible for the industry leader to outbid competition to sustain its leadership position. The analysis is useful for consultants advising sellers in similar settings to negotiate for a better deal or buyers to justify paying a premium for future gains. One may also conclude that game theory can form a strong basis when negotiating transactions to justify a market leader paying an extra premium to rationalize an acquisition.