Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks
In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users’ quality-of-service requirements. Due to the lack of inter-cluster communications, clusters compete with one another in order to improve the overall network’s energy efficiency. This inter-cluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and up to 41% of reduced fractional transfer time compared to conventional approaches.
- To Maximizing the energy efficiency of wireless small cell networks, dynamic clustering and switching will be developed
- Proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load
In the past decade, wireless services have evolved from traditional voice and text messaging to advanced applications such as video streaming, multimedia file sharing, and social networking. Such bandwidth-intensive applications increase the load of existing wireless cellular systems and potentially lead to increased energy consumption. The deployment of low-cost and high-capacity small cells over existing cellular networks has been introduced as a promising solution to offload the macro cellular traffic to small cell networks
- System 1: Optimal deployment strategy.
- Optimal deployment strategy’s Goals:
- Low power operation
- Effective collision avoidance
- Simple implementation
- Efficient at both low and high data rates
- Tolerant to changes on the network
- Spectral efficiency
- System 2: Stochastic programming approach
- Stochastic programming approach
- Reduce latency and reduce energy consumption
- Minimize overhearing problem.
- More coordination of network nodes
- High efficiency
- Energy conservation is high for moderate traffic.
- Lack of security.
- Collision and interference occurrences
- A dynamic mechanism is proposed to group locally coupled small cell base stations (SBSs) into clusters based on location and traffic load.
- Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance
- Distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information.
- In small cell networks, performing dynamic approaches for switching BSs ON and OFF may require the knowledge of the entire network to operate effectively which incurs significant overhead. Therefore, coordination mechanisms with minimum overhead are needed to group BSs into clusters within which BSs can smartly and locally coordinate their transmissions. Unlike previous studies, we investigate not only location-based clustering methods, but we also consider the effects of BS capabilities to dynamically handle traffic, and further compare the performance of centralized and decentralized clustering solutions.
Software Tool description
- Programming Language : C / MATLAB code
- Platform : Windows / Linux
- Tool : MATLAB 8.1
- Reduce the energy consumption compare to other function
- Lifetime efficiency
- Reduces the overall network traffic
- Video streaming
- Multimedia file sharing
- Social networking
- Macro and micro cellular network
- System : Pentium Dual Core.
- Hard Disk : 120 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 1GB.
- Operating system : Windows 7.
- Coding Language : MATLAB
- Tool : MATLAB R2013A
Sumudu Samarakoon Student Member, IEEE, Mehdi Bennis Senior Member, IEEE, Walid Saad Senior Member, IEEE and Matti Latva-aho Senior Member, “Dynamic Clustering and ON/OFF Strategies for Wireless Small Cell Networks”, IEEE Transactions on Wireless Communications, 2016.