Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/199730
Title: Multi-Operator Spectrum Sharing for Small Cell Networks: A Matching Game Perspective
Authors: Tachporn Sanguanpuak;Sudarshan Guruacharya;Nandana Rajatheva;Mehdi Bennis;Matti Latva-Aho
Year: 2017
Publisher: IEEE
Abstract: One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other's spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.
URI: http://localhost/handle/Hannan/199730
volume: 16
issue: 6
More Information: 3761,
3774
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7888596.pdf1.74 MBAdobe PDF
Title: Multi-Operator Spectrum Sharing for Small Cell Networks: A Matching Game Perspective
Authors: Tachporn Sanguanpuak;Sudarshan Guruacharya;Nandana Rajatheva;Mehdi Bennis;Matti Latva-Aho
Year: 2017
Publisher: IEEE
Abstract: One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other's spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.
URI: http://localhost/handle/Hannan/199730
volume: 16
issue: 6
More Information: 3761,
3774
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7888596.pdf1.74 MBAdobe PDF
Title: Multi-Operator Spectrum Sharing for Small Cell Networks: A Matching Game Perspective
Authors: Tachporn Sanguanpuak;Sudarshan Guruacharya;Nandana Rajatheva;Mehdi Bennis;Matti Latva-Aho
Year: 2017
Publisher: IEEE
Abstract: One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other's spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.
URI: http://localhost/handle/Hannan/199730
volume: 16
issue: 6
More Information: 3761,
3774
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7888596.pdf1.74 MBAdobe PDF