A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network

Power management is one of the vital issue in wireless sensor networks, where the lifetime of the network relies on battery powered nodes. Transmitting at high power reduces the lifetime of both the nodes and the network. One efficient way of power management is to control the power at which the nodes transmit. In this paper, a virtual multiple input multiple output wireless sensor network (VMIMO-WSN)communication architecture is considered and the power control of sensor nodes based on the approach of game theory is formulated. The use of game theory has proliferated, with a broad range of applications in wireless sensor networking. Approaches from game theory can be used to optimize node level as well as network wide performance. The game here is categorized as an incomplete information game, in which the nodes do not have complete information about the strategies taken by other nodes. For virtual multiple input multiple output wireless sensor network architecture considered, the Nash equilibrium is used to decide the optimal power level at which a node needs to transmit, to maximize its utility. Outcome shows that the game theoretic approach considered for VMIMO-WSN architecture achieves the best utility, by consuming less power.Comment: 12 pages, 8 figure

Stochastic Channel Selection in Cognitive Radio Networks

Abstract — In this paper, we investigate the channel selection strategy for secondary users in cognitive radio networks. We claim that in order to avoid the costly channel switchings, a secondary user may desire an optimal channel which maximizes the probability of successful transmissions, rather than consis-tently adapting channels to the random environment. We propose a stochastic channel selection algorithm based on the learning automata techniques. This algorithm adjusts the probability of selecting each available channel and converges to the -optimal solution asymptotically. I

Distributed Discrete Power Control for Bursty Transmissions over Wireless Data Networks

Distributed power control is an important issue in wireless networks. Recently, due to the bursty nature of data communication, packet switching is used in cellular systems. In addition, majority of previous power control algorithm assume that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and QUALCOMM's proposal to the IS-95 standard use a finite number of discretized power levels. These motivate the need to investigate solutions for distributed discrete power control for bursty transmission. We first note that, by simply discretizing the previously proposed continuous power adaptation techniques will not suffice. This is because, a simple discretization does not guarantee convergence and uniqueness. On the other hand, the conventional analytical model based on mean values may be too optimistic and the analysis assuming that the data subscribers are always transmitting may be too pessimistic to evaluate the system performance. Therefore, we propose a probabilistic power adaptation algorithm and analyze its theoretical properties along with the numerical behavior for bursty transmission. We approximate the discrete power control iterations by an equivalent ordinary differential equation (ODE) to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium may exist are also studied. Experimental results are presented for several cases and the impact of data burstiness on the proposed algorithm is also concerned