Power Control in Networks With Heterogeneous Users: A Quasi-Variational Inequality Approach
arXiv:1308.4840 · doi:10.1109/TSP.2015.2452231
Abstract
This work deals with the power allocation problem in a multipoint-to-multipoint network, which is heterogenous in the sense that each transmit and receiver pair can arbitrarily choose whether to selfishly maximize its own rate or energy efficiency. This is achieved by modeling the transmit and receiver pairs as rational players that engage in a non-cooperative game in which the utility function changes according to each player's nature. The underlying game is reformulated as a quasi variational inequality (QVI) problem using convex fractional program theory. The equivalence between the QVI and the non-cooperative game provides us with all the mathematical tools to study the uniqueness of its Nash equilibrium (NE) points and to derive novel algorithms that allow the network to converge to these points in an iterative manner both with and without the need for a centralized processing. A small-cell network is considered as a possible case study of this heterogeneous scenario. Numerical results are used to validate the proposed solutions in different operating conditions.
14 pages, 4 figures, submitted to IEEE Trans. Signal Process
References in corpus (5)
- Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part I: Nash Equilibria
- Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part II: Algorithms
- Energy-Efficient Precoding for Multiple-Antenna Terminals
- Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints
- Game Theory for Signal Processing in Networks
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- Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints
- Energy-Delay Efficient Power Control in Wireless Networks
- Non-convex Generalized Nash Games for Energy Efficient Power Allocation and Beamforming in mmWave Networks
- Uncertainty in Multi-Commodity Routing Networks: When does it help?
- Energy Efficient Competitive Resource Allocation in MIMO networks
- Distributed Optimization of Hierarchical Small Cell Networks: A GNEP Framework