Samer Lahoud

Radio Access Network Sharing in 5G: Strategies and Benefits

In a shared radio access network, a user can be served through the network of his home operator or the network of another service operator in the sharing system. Consequently, when the home operator is unable to serve its user, and there is more than one available service operator, a selection decision must be made. The decision must consider the satisfaction of three main agents: the user, his home operator and the service operator. This paper presents a strategic algorithm for the access selection decision in a multi-operator wireless network. It is based on a cost function that combines the requirements of the user, its home operator profit and the offered QoS of the service operator. This cost function takes into account the operators’ strategies for cooperation. This work focuses on the service operator strategy and proposes two strategies: a pricing strategy that consists of increasing the service cost, and a sharing strategy that consists of limiting the amount of shared resources. Simulation results prove the efficiency of the proposed algorithm and show how sharing between operators brings benefits in terms of user acceptance and profits as well. In addition, results show that the service operator strategy affects the access selection decision and the cooperation benefits; a pricing strategy can guarantee high profits for the service operator and can improve its client acceptance.

Centralized and distributed RRH clustering in Cloud Radio Access Networks

Cloud Radio Access Network (C-RAN) is a promising technology to improve user quality of service and reduce network capital and operating costs. The key concept behind C-RAN is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH), and to pool BBUs from multiple sites into a single geographical point. Moreover, to achieve statistical multiplexing gain, RRHs should be efficiently clustered: many RRHs may be mapped into a single BBU. In this article, RRH clustering is formulated as a coalition formation game where RRHs collaborate and organize themselves into disjoint independent clusters, in a way to optimize network throughput, power consumption, and handover frequency. An optimal centralized solution, based on exhaustive search, is presented. We also propose a distributed algorithm, based on the merge-and-split rule, to form RRH clusters. Simulation results show that our centralized solution adapts to network load conditions and outperforms the no-clustering method, where only one RRH is assigned to each BBU, and the grand coalition method, where all RRHs are assigned to a single BBU. More importantly, our distributed algorithm achieves very close performance to the optimal solution, with significantly lower computational complexity.