Joint Optimization Of Radio Resources Research Articles

Joint Optimization of Radio Resources and Coverage Enhancement in Massive Microgrid Networks

Joint Optimization of Radio Resources and Coverage Enhancement in Massive Microgrid Networks

Joint Optimization of Radio Resources for Uplink Multihoming Networks

Multihoming networks in which users with multiple air interfaces can simultaneously transmit data traffic over multiple radio access technologies have greatly garnered attention as a promising solution to address the explosive growth in mobile data traffic. In this paper, we propose joint resource allocation algorithms for uplink multihoming networks. Unlike existing solutions in the literature, the proposed algorithms reflect two conflicting factors (i.e., throughput and monetary cost) affecting user’s satisfaction level with respect to allocated radio resources in the process of resource allocation. Depending on the different assumptions of subchannel allocation, different joint resource allocation algorithms are developed where the main difference between the algorithms is whether they can be implemented in a distributed manner or not. Also, a diverse set of user’s service types is taken into consideration, which makes the proposed algorithms suitable for an integrated service network. Numerical results demonstrate the validity of the proposed algorithms.

Gibbsian method for the self-optimization of cellular networks

In this study, we propose and analyze a class of distributed algorithms performing the joint optimization of radio resources in heterogeneous cellular networks made of a juxtaposition of macro and small cells. Within this context, it is essential to use algorithms able to simultaneously solve the problems of channel selection, user association and power control. In such networks, the unpredictability of the cell and user patterns also requires distributed optimization schemes. The proposed method is inspired from statistical physics and based on the Gibbs sampler. It does not require the concavity/convexity, monotonicity or duality properties common to classical optimization problems. Besides, it supports discrete optimization which is especially useful to practical systems. We show that it can be implemented in a fully distributed way and nevertheless achieves system-wide optimality. We use simulation to compare this solution to today’s default operational methods in terms of both throughput and energy consumption. Finally, we address concrete issues for the implementation of this solution and analyze the overhead traffic required within the framework of 3GPP and femtocell standards.