Spectrum Access and Power Control for Cognitive Satellite Communications: A Game-Theoretical Learning Approach

Abstract

With the increasing scarcity of spectrum resources in satellite and terrestrial communications, spectrum sharing becomes a promising option. In this paper, we investigate the spectrum access and power control problem in multibeam-based cognitive satellite communication network. Differ from the most existing spectrum access problems in terrestrial networks, we consider not only the interference between cognitive users, but also the co-channel interference from multi-beam satellite communication system to cognitive users. We formulate a spectrum access and power control game, and it is proved to be an ordinal potential game. The sufficient conditions for cognitive users not to interfere with each other are given, and the upper bound of the aggregation interference experienced by all cognitive users is deduced theoretically. Then, based on the trial and error (TE) algorithm, we propose a learning-based distributed spectrum access algorithm, which statistically converges to the best Nash equilibrium(NE). Furthermore, to simplify the coefficients design and improve the convergence performance, we propose an improved learning-based distributed spectrum access algorithm. Simulation results show that the average network throughput of the proposed game is close to the best, which validates the game-theoretic solution. Simulation results also show that the improved learning-based distributed spectrum access algorithm is superior to the original algorithm in terms of convergence speed, throughput performance, and practicability, which confirms the effectiveness of the improved algorithm. INDEX TERMS Cognitive satellite communication, power control and spectrum access, ordinary potential game, trial and error learning algorithm.

A Distributed Collaborative Game-Theoretic Approach in Cognitive Satellite Communication Networks

“…The distance between the transmitter and receiver of each terrestrial cognitive user is set to d = 20m [35]. In all simulations, the transmission loss between terrestrial cognitive users is calculated by ideal attenuation formulation L = 32.5 + 20 lg D (km) + 20 lg F (M Hz), where F is set to 14000 [27]. It is assumed that the cognitive users are interfered by three co-channel beams, the interference coefficient to beam b is set to h = [0.3, 0.2, 0.1], respectively [47].…”

“…• It is shown that the joint channel access and power optimization scheme achieves better system throughput performance than only considering channel access, which verifies the effectiveness of the formulated game. It is noted that our earlier work [27] studies the spectrum sharing in cognitive satellite communication in the interweave mode, in which only channel selection is optimized (terrestrial cognitive users are non-cooperative and always transmit with the maximum power), thus significantly reducing the system throughput. Besides, the algorithm proposed in [27] converges slowly and does not always converge to the optimal NE.…”

“…It is noted that our earlier work [27] studies the spectrum sharing in cognitive satellite communication in the interweave mode, in which only channel selection is optimized (terrestrial cognitive users are non-cooperative and always transmit with the maximum power), thus significantly reducing the system throughput. Besides, the algorithm proposed in [27] converges slowly and does not always converge to the optimal NE. Motivated by all these observations, this work extends pure channel selection to joint channel selection and power control, which is also not investigated in dynamic spectrum access based on distributed frameworks [26], [28]- [35].…”

“…As the joint channel access and power control problem now formulated as an exact potential game, it is another essential work to achieve the NE. Many distributed algorithms can be applied to achieve the NE of the potential game, including best response [28], stochastic-learning automata [35], noregret learning [30], Q-learning algorithm [43] and trial and error learning [27]. However, it is still not an easy work to achieve the NE solution in the channel access and power optimization game due to the following reasons: i) The strategies of channel access are discrete, while the power control strategies may be continuous.…”

“…Most existing algorithms converge to a general NE, such as best/better response [28], no regret learning [30], stochastically learning automata [35], and reinforcement learning [43]. Although the trial and error algorithm (TE) can statistically converge to the optimal NE [27], it needs lots of iterations and will deviate from the optimal NE with a certain probability. Moreover, finding the optimal NE in a two-dimensional strategy space is even more difficult.…”