Client-centric Radio Access Network Selection in Heterogeneous Networks

Oct 3, 2016, 1:00 pm2:30 pm
Engineering Quadrangle B327



Event Description

Heterogeneity of modern wireless network radio access technologies (RATs) (e.g., 3G/4G/LTE, Wi-Fi, Bluetooth, 5G) is an intrinsic part of providing seamless connectivity and network access in next-generation wireless networks. With modern mobile edge devices increasingly equipped with a variety of wireless interfaces to access these heterogeneous networks (HetNets), these edge clients are capable of dynamically switching between different access networks to optimize their performance. Traditionally, solutions for RAT selection in HetNets focus on the network-centric approach, with a centralized controller solving a global optimization, but this approach is not only non-scalable due to required signaling, but also the practical problem of non-cooperative network operators. With the increasing processing power of client edge devices (e.g., smartphones, tablets, etc), it is worth considering if and how RAT selection should be done at the client instead of in the network.
In this talk I will explore RAT selection in HetNets from a client-centric perspective under varying degrees of network-provided information, where the client wishes to maximize its throughput on its selected RAT: (1) perfect network knowledge (clients have perfect information on other client-RAT association configurations), (2) partial network knowledge (clients have time-averaged statistics for their own client-RAT channels), and (3) no network knowledge (clients have no statistics provided by the RAT). I will detail three different algorithms to solve the problem of RAT selection in HetNets under each case, and evaluate them using both measurement-driven and standards-driven simulations while comparing their performance against currently-employed algorithms for network selection. Then I will show that in the client-centric algorithms converge to equilibrium and are order-optimal in terms of achieved regret, and demonstrate that RAT selection can be performed in a client-centric manner with a minimal loss of optimality.