Game Theoretic Power Allocation Research Articles

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

Coalition Game Theoretic Power Allocation Strategy for Multi-Target Detection in Distributed Radar Networks

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm.

Power Allocation for Cooperative Jamming Against a Strategic Eavesdropper Over Parallel Channels

This paper considers a friendly interferer allocating jamming power to eavesdropping channels to increase the level of secrecy of a wireless network. The friendly interferer has access to limited power, while the eavesdropper may not have the ability to attack all channels simultaneously. When all channels used for secret communication are under the threat of eavesdropping attacks, the optimal power allocation policy results from solving a convex optimization problem. In this case, the optimal policy is unique and can be obtained via a water-filling scheme. When the eavesdropper can not attack all channels, the eavesdropper should behave strategically and may select targets probabilistically. We propose a non-zero-sum game that helps the friendly interferer predict and concentrate on the targets selected by the eavesdropper. Under certain conditions, we prove that there exists a unique Nash equilibrium (NE) strategy pair, which has a threshold type structure. We provide conditions under which the eavesdropper’s equilibrium strategy is deterministic. We devise a strategy iteration algorithm to compute an equilibrium power allocation strategy. We present examples showing that the game-theoretic power allocation strategy performs better than the conservative power allocation strategy that assumes every channel to be under attack.

Accelerated IoT Anti-Jamming: A Game Theoretic Power Allocation Strategy

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques.

Non-Cooperative Game Theoretic Power Allocation Strategy for Distributed Multiple-Radar Architecture in a Spectrum Sharing Environment

This paper investigates the problem of non-cooperative game theoretic power allocation (NGTPA) for distributed multiple-radar architectures in a spectrum sharing environment, where multiple radars coexist with a communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the power consumption of each radar by optimizing the transmission power allocation, which is constrained by a predefined signal-to-interference-plus-noise ratio requirement for target detection and a maximum interference tolerant limit for communication system. Since each radar is rational and selfish to maximize its own utility, we utilize the non-cooperative game theoretic technique to tackle the distributed power allocation problem. Taking into consideration the target detection performance and received interference power at the communication receiver, a novel utility function is defined and employed as the optimization criterion for the NGTPA strategy. Furthermore, the existence and uniqueness of the proposed game’s Nash equilibrium point are analytically proved. An iterative power allocation algorithm with low computational complexity and fast convergence is developed, where the optimal value of each radar’s transmission power is simultaneously updated at the same time step. Numerical simulations are provided to verify the analysis and evaluate the performance of the proposed strategy as a function of the system parameters. It is shown that the distributed algorithm is effective for power allocation and could protect the communication system with limited implementation overhead.

Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network

We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash equilibrium. The mathematical analysis based on Karush–Kuhn–Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

Interference alignment and game-theoretic power allocation in MIMO Heterogeneous Sensor Networks communications

Interference alignment (IA) can eliminate the interference among wireless nodes. In a multi-input multi-output (MIMO) heterogeneous sensor network (HSN), nodes need to send out their sensed information, and secondary nodes can coexist with primary nodes without generating any interference by using the IA technology. However, few works have considered the sum utility of secondary nodes. In this paper, we not only eliminate the interference by IA, but also maximize the sum utility of secondary nodes by using a game-theoretic power allocation algorithm in Heterogeneous Sensor Networks. Without losing generality, consider there are one primary node and K secondary nodes in the network. Assuming perfect channel knowledge at the primary node, the rate of the primary node is maximized by a water-filling algorithm and the interference from secondary nodes to the primary node is aligned into the unused spatial dimension. Also, an improved maximum signal-to-interference-plus-noise algorithm using channel reciprocity is proposed for the secondary nodes to eliminate the interference between secondary nodes. In addition, the sum rate of the secondary nodes is maximized by a non-cooperative game-theoretic power allocation algorithm. Simulation results show the effectiveness of the proposed algorithms in terms of suppressed interference and maximized sum utility of secondary nodes.

A coalitional game‐based relay load balancing and power allocation scheme in decode‐and‐forward cellular relay networks

AbstractIn this paper, a game theoretic relay load balancing and power allocation scheme is proposed for downlink transmission in a decode‐and‐forward orthogonal frequency division multiple access‐based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game‐based scheme can considerably improve the average sum‐spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays. Copyright © 2015 John Wiley & Sons, Ltd.

Game theoretic power allocation for fading MIMO multiple access channels with imperfect CSIR

In this paper, we formulate the power allocation (PA) problem for uplink of MIMO fading multiple access channels using a game theoretic framework with channel estimation error at receiver side. We investigate the effect of the error on derived PA policy and on performance of the system. Furthermore, it is assumed that the CSI at transmitter side is imperfect i.e., each transmitter is informed with the statistics of the channels instead of the instantaneous channel states. We use lower bound of mutual information between each user and the base station to derive a utility function for the concave game, regarding the channel estimation error. Following the formulation of problem, existence and uniqueness of the game's equilibrium are investigated and it is proved that the proposed game has a unique Nash equilibrium (NE). Also a primal-dual subgradient based best-response algorithm is designed to find the NE. The algorithm updates the policy of each player regarding the given policy of other players, at each iteration. Finally, simulation results of the achieved PA policy are illustrated to investigate the effect of channel estimation error. In particular, simulations show that the overall system performance will be improved when channel estimation error in modeling the PA game is considered.

Game-Theoretic Beamforming and Power Allocation in MIMO Cognitive Radio Systems with Transmitter Antenna Correlation

Multi-input multioutput (MIMO) technique provides a promising solution to enhance the performance of wireless communication systems. In this paper, we consider antenna correlation at the transmitter in practical cognitive MIMO systems. What is more, a game-theoretic framework is conducted to analyze the optimum beamforming and power allocation such that each user maximizes its own rate selfishly under the transmitting power constraint and the primary user (PU) interference constraint. The design of the cognitive MIMO system is formulated as a noncooperative game, where the secondary users (SUs) compete with each other over the resources made available by the PUs. Interestingly, as the correlation parameter grows, the utility degrades. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can converge quickly and clearly outperforms the strategy without game.

Game-Theoretic Joint Power Allocation and Beamforming for Cognitive MIMO Systems with Finite Feedback

In the paper, we consider the imperfect channel state information (CSI) in practical cognitive MIMO systems. We first analyze the feedback of quantified CSI from the primary user (PU) and propose a joint power allocation and beamforming algorithm via game theory. Compared with the game under the condition of perfect CSI, new utility function and cost function are constructed under imperfect CSI. We analyze the error introduced from the uniformly quantified CSI and obtain new constraints. Besides, existence of the Nash equilibrium in case of both perfect CSI and imperfect CSI are proven. We propose a new iterative algorithm to reach the Nash equilibrium (NE). Simulation results show that the proposed algorithm can converge quickly.

Distributed Power Allocation for Multiuser Two-Way Relay Networks Using Stackelberg Game

Recently, two-way relay networks have been regarded as a promising technique that can improve bandwidth utilization. In this paper, the power allocation problem for multiuser two-way relay networks with amplifyand-forward protocol is investigated. In order to describe the self-interestedness of nodes in two-way relay networks, a two-level Stackelberg game is introduced to jointly optimize the benefits of the source pair and the relay nodes, where the relay nodes are modeled as leaders and the source pair is modeled as a follower. To facilitate the power allocation process, a distributed game-theoretic power allocation algorithm is proposed. Then, the existence and optimization of the Stackelberg equilibrium for the proposed power allocation algorithm is proven. The convergence of the presented algorithm is also analyzed by proving that price update is a standard function. Simulation results indicate that the proposed power allocation algorithm can improve energy utilization by jointly optimizing the utilities of both source pair and relay nodes.

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.

Experimental evaluation of game theoretic power allocation in MIMO ad-hoc networks

Multiple input multiple output (MIMO) communication systems in an ad-hoc network can provide high spectral efficiency. Several resource allocation methods have been presented and experimentally demonstrated to improve performance in a resource limited environment. Recently, a game theoretic method has been published with promising results. The goal of this paper is to present simulation and experimental results for this game theoretic technique.