Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.
@cite_18: We present a novel approach for distributed load balancing in heterogeneous networks that use cell range expansion (CRE) for user association and almost blank subframe (ABS) for interference management. First, we formulate the problem as a minimization of an @math -fairness objective function with load and outage constraints. Depending on @math , different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a near-potential game, in which the potential function is the @math -fairness function. The optimal pure Nash equilibrium (PNE) of the game is found by using distributed learning algorithms. We propose log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. We give a detailed proof of convergence of learning algorithms for a near-potential game. We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS.
@cite_14: This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has sensing, communication, computation and moving capabilities to complete sensing tasks. Here the agents need to relocate themselves, from their initial random locations, to their optimal configuration. The proposed algorithm is based on game theory control where a collection of distributed agents use their local information to make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Maximum Likelihood estimation scheme is used to estimate the unknown parameters of a Gaussian Mixture Model (GMM). Then in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents' utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Simulation results are provided to verify the performance of the proposed algorithm.
@cite_26: Abstract The focus of this paper is on the enhancement of Log Linear Learning (LLL) and Q-learning (QL) in game theory and their applications in multi-robot control. We first propose a modified Binary Log-Linear Learning (BLLL) algorithm that can achieve a better performance and higher learning rate when is compared to standard BLLL. However, due to a number of assumptions, practical applicability of a LLL-based algorithm is limited. To relax this limitation we then propose a modified QL algorithm that can achieve the same performance but with the price of lower learning rate. The algorithms proposed are tested numerically.
@cite_8: 
@cite_9: Different from the existing symmetric social relation graph, we first utilize a new asymmetric social weighted graph to study the problem of resource allocation for Device-to-Device (D2D) wireless social networks. We propose a social-aware cluster-based game theoretical scheme for resource allocation in which clusters are formatted based on the new defined weighted social interference degree and the physical interference graph. In the proposed cluster-based game scheme, we shrink action spaces of players (clusters) through a determined channel allocation rule. Simulation results show that the proposed cluster action selection algorithm has a higher convergence speed and a better social network interference performance.
@cite_24: We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.
@cite_19: To reap the benefits of dense small base station (SBS) deployment, innovative backhaul solutions are needed for scenarios in which high-speed ground backhaul links are either unavailable or limited in capacity. In this paper, a novel backhaul scheme that relies on unmanned aerial vehicles (UAVs) as an on-demand flying network is proposed. The design of the aerial backhaul scheme is formulated as a network formation game among UAVs that seek to form a multi-hop backhaul network in the sky. To solve this game, a myopic network formation algorithm which reaches a pairwise stable network upon convergence, is introduced. The proposed network formation algorithm enables the UAVs to form the necessary multi-hop backhaul network in a decentralized manner thus adapting the backhaul architecture to the dynamics of the network. Simulation results show that the proposed network formation algorithm achieves substantial performance gains in terms of both rate and delay reaching, respectively, up to 380 and 410 compared to the formation of direct communication links with the gateway node (for a network with 15 UAVs).
@cite_27: Multi-agent Systems (MASs) have found a variety of industrial applications from economics to robotics, owing to their high adaptability, scalability and applicability. However, with the increasing complexity of MASs, multi-agent control has become a challenging problem to solve. Among different approaches to deal with this complex problem, game theoretic learning recently has received researchers' attention as a possible solution. In such learning scheme, by playing a game, each agent eventually discovers a solution on its own. The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log linear learning and reinforcement learning. Each algorithm proposed in this paper, relaxes and imposes different assumptions to fit a class of MAS problems. Numerical experiments are also conducted to verify each algorithm's robustness and performance.
Compared with other algorithms, novel algorithm SPBLLA has more advantages in learning rate. Various algorithms have been employed in the UAV networks in search of the optimal channel selection @cite_9 @cite_19 , such as stochastic learning algorithm @cite_24 . The most widely seen algorithm--LLA is an ideal method for NE approaching @cite_8 @cite_18 . The BLLA has been employed by @cite_14 , which is modified from LLA to update strategies in each iteration to converge to the NE. However, only a single agent is allowed to alter strategies in one iteration. In large-scale scenarios, more iterations are required, which makes BLLA inefficient. It is obvious that more UAVs altering strategies in one iteration would be more efficient. To achieve it, the works in @cite_26 and @cite_27 have provided a novel synchronous algorithm. However, there exist superabundant restrictions that make the algorithm impractical in most scenarios. Compared with the formers, SPBLLA has fewer constraints and can achieve synchronous operation, which can significantly improve the computational efficiency.