Multi-Objective Optimization of Power Allocation in Interference-Limited Wireless Networks
Keywords:
Power Control, Interference Management, Game Theory, Multi-Objective Optimization, Energy Efficiency, Adaptive Pricing, Nash Equilibrium, Variational Inequality, Wireless Networks, QoS.Abstract
In dense deployments, the challenge of interference management is a primary issue, as turning up the power for one link will increase the data rates of that link, but will also reduce the data rates of other links in the vicinity. Traditional power-control strategies tend to seek to optimize one or only a few metrics, such as throughput or energy efficiency, or interference suppression, and therefore may lead to operating points which cannot meet multiple network requirements simultaneously. This paper presents a multi-objective non-cooperative game theoretic approach to distributed power allocation in an interference-limited wireless network. The approach proposed in this paper takes into account the achievable rate, energy efficiency, the externality of interference and Quality-Of-Service (QoS) protection for each transmitter-receiver pair in their individual utility functions. One important aspect is an adaptive interference pricing mechanism where the price of interference is continuously adjusted based on the difference between measured interference and a pre-defined interference budget. In this mechanism, users can adapt their power decisions to the current interference situation without having to optimize the power in the central unit. The equilibrium obtained is mathematically described by a variational inequality (VI) and allows the study of existence, uniqueness and convergence of the equilibrium. A projected distributed power-update mechanism is further developed to obtain the equilibrium iteratively. Numerical experiments are used to assess the performance of the network, computational complexity, parameter sensitivity and individual contributions of utilities. The results obtained show that the proposed framework can improve power consumption reduction without sacrificing throughput and QoS requirements while minimizing unnecessary power consumption and aggregate interference. The study also uses the term convergence robustness for the parameter space studied, which is validated by experiments, and not for an arbitrary parameter space as is done in many studies.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
