Game Theoretic Resource Allocation Research Articles

Game-theoretic resource allocation methods for device-to-device communication

Device-to-device communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e. D2D direct communication) or multi-hop clusterbased transmission (i.e. in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.

Collaborative Sub-Channel Allocation in Cognitive LTE Femto-Cells: A Cooperative Game-Theoretic Approach

In this paper, formation of stable coalitions of users, each exploiting resources in a femto-cell, and the resource allocation in each femto-cell is investigated in a UMTS long term evolution (LTE) network. We study a downlink scenario where users collaborate to increase network throughput and, simultaneously, attempt to increase their own payoffs. Payoffs to the users are defined as the monetary equivalent of the individual users' achievable throughput in the specified coalition structure. A distributed game-theoretic resource allocation mechanism is developed whereby users autonomously decide which sub-channel in which coalition to join. If each user operates according to the proposed algorithm, the sum throughput of all links converges with probability one to its maximum feasible value.

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.

A Game Theoretic Resource Allocation Model Based on Extended Second Price Sealed Auction in Grid Computing

In resource-limited environment, grid users compete for limited resources, and how to guarantee tasks’ victorious probabilities is one of the most primary issues that a resource scheduling model cares. In order to guarantee higher task’s victorious probabilities in grid resources scheduling situations, a novel model, namely ESPSA (Extended Second Price Sealed Auction), is proposed. The ESPSA model introduces an analyst entity, and designs analyst’s prediction algorithm based on Hidden Markov Model (HMM). In ESPSA model, grid resources are sold through second price sealed auction. Moreover, to achieve high victorious probabilities, the user brokers who are qualified to participate in the auctions will predict other players’ bids and then carry out the most beneficial bids. The ESPSA model is simulated based on GridSim toolkit. Simulation results show that the ESPSA model assures a higher victorious probability and superior to other traditional algorithms. Moreover, we analyze the existence of Nash equilibrium based on simulation results, thus, any participant who changes its strategy unilaterally could not make the results better.

Urban Security: Game-Theoretic Resource Allocation in Networked Domains

Law enforcement agencies frequently must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. Since intelligent attackers may observe and exploit patterns in the allocation, it is crucial that the allocations be randomized. We cast this problem as an attacker-defender Stackelberg game: the defender’s goal is to obtain an optimal mixed strategy for allocating resources. The defender’s strategy space is exponential in the number of resources, and the attacker’s exponential in the network size. Existing algorithms are therefore useless for all but the smallest networks. We present a solution approach based on two key ideas: (i) A polynomial-sized game model obtained via an approximation of the strategy space, solved efficiently using a linear program; (ii) Two efficient techniques that map solutions from the approximate game to the original, with proofs of correctness under certain assumptions. We present in-depth experimental results, including an evaluation on part of the Mumbai road network.

Information-Constrained Resource Allocation in Multicamera Wireless Surveillance Networks

Real-time multiuser multimedia applications, such as surveillance or monitoring using multiple cameras, have recently started to be deployed over flexible and low-cost multihop wireless networks. In such multimedia systems, the various sources (cameras) share the limited network resources and collaboratively forward the captured video streams to a remote central monitor. However, existing resource allocation schemes often ignore the dynamic application-layer video and network characteristics by focusing on the steady-state or worst-case operating conditions. This may result in inefficient allocation of the network resources. In this paper, we focus on determining whether the resource allocation for wireless video surveillance systems should be performed based on steady-state or worst-case operating conditions, or whether perpetual adaptation to the dynamically changing source and network conditions is desirable. We analyze three different types of solutions that have different information requirements: a centralized optimization approach, a decentralized game-theoretic approach (which guarantees a stable allocation), and a distributed greedy approach (which perpetually adapts allocation based on the local information exchanged among the neighboring nodes). We compare these three approaches using the following four metrics: 1) the total video quality; 2) the computational complexity; 3) the required control information overhead; and 4) the timely adaptation to the network and source variation. We show that in a static network, the game theoretic resource allocation is only better than the distributed greedy approach when the network transmission rates are high. In a dynamic network, the distributed greedy approach can outperform the other two approaches significantly in terms of video quality (peak signal-to-noise ratio). This shows that resource allocation solutions for multicamera wireless surveillance networks need to explicitly consider both the dynamic source characteristics and network conditions, rather than always relying on stable, but predetermined, allocations.