Distributed Beamforming for Small Cell Networks via Generalized Nash Equilibrium
- Resource Type
- Conference
- Authors
- Chen, Yuhe; Han, Leixin; Wang, Jiaheng; Xia, Liang; Gao, Xiqi
- Source
- 2023 IEEE/CIC International Conference on Communications in China (ICCC) Communications in China (ICCC), 2023 IEEE/CIC International Conference on. :1-6 Aug, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Macrocell networks
Base stations
Array signal processing
Simulation
Quality of service
Pricing
Interference
small cell networks
distributed optimization
game theory
generalized Nash equilibrium
variational inequality
- Language
A dense network which deploys a great number of small cell base stations (SBSs) in the existing cellular communication system can effectively improve the system capacity, but it inevitably brings interference between different devices and affects the users’ quality of service (QoS). This paper focuses on a distributed beamforming optimization problem for multiple-input single-output (MISO) downlink small cell networks. Beamforming vectors of macrocell base station (MBS) and SBSs are jointly optimized to maximize the achievable rate of each base station (BS) as well as considering the QoS of the macrocell user equipments (MUEs). Beamforming strategies of all BSs are coupled in the QoS constraints, which renders the original problem amenable to solve via the generalized Nash equilibrium problem (GNEP) framework. In this paper, zero-forcing method is used to eliminate intra-cell interference and the existence and uniqueness conditions of Nash equilibrium (NE) are provided with the help of variational inequality (VI) theory. A distributed iteration algorithm is proposed based on pricing mechanism and the simulation results demonstrate that the algorithm can converge to the NE and guarantee the QoS requirements.