Access class barring, data offloading, and resource allocation in heterogeneous wireless networks

• Main Author: Wang, Zehua
• Language: English
• Published: University of British Columbia 2017
• Online Access: http://hdl.handle.net/2429/60155

In future heterogeneous wireless networks, machine-type communication (MTC) devices require the access of wireless cellular networks. However, the Long Term Evolution (LTE) networks, which are designed for human users, may not be able to handle a large number of bursty random access requests from MTC devices. We propose a scheme that uses both access class barring (ACB) and timing advance information to reduce random access overload in MTC systems. Given the number of backlogged MTC devices, we formulate an optimization problem to determine the optimal ACB parameter, which maximizes the expected number of MTC devices successfully served in each random access slot. We present a closed-form approximate solution and propose an algorithm to estimate the number of backlogged MTC devices to improve the practicability of the proposed scheme. Besides, the data traffic demand of mobile users is significant in future communication networks. In heterogeneous wireless networks, mobile devices close to each other can also communicate in a device-to-device (D2D) manner to transfer digital objects (e.g., videos). However, the opportunity that mobile users download their interested objects from neighbors is transient. We propose an expected available duration (EAD) metric to evaluate the opportunity that an object can be downloaded from neighbors. The EAD metric takes into account the pairwise connectivity of users, social influence between users, diffusion of digital objects, and the time that users would like to wait for D2D data offloading. To download more data from neighbors, a mobile user can first download the available object that has the smallest EAD. Moreover, for resource allocation in future wireless cellular networks with the cloud radio access network (C-RAN) architecture, we model user’s utility by a sigmoidal function of signal-to-interference-plus-noise ratio (SINR) to capture the diminishing utility returns for very small or very large SINRs in real-time applications (e.g. video streaming). Our objective is maximizing the aggregate utility of users while taking into account the imperfectness of channel state information, limited backhaul capacity of C-RAN, and minimum quality of service requirements. We propose an efficient resource allocation algorithm which outperforms a baseline scheme for weighted system sum rate maximization. === Applied Science, Faculty of === Graduate