Dual Connectivity in Heterogeneous Cellular Networks: Analysis of Optimal Splitting of Elastic File Transfers Using Flow-Level Performance Models

The dual connectivity feature in heterogeneous cellular networks can be used to improve the download performance for elastic applications by splitting a file transfer over two connections. We employ two parallel processor sharing queues along with a heavy-traffic approximation to develop an extended framework that allows for analysis of file download performance in dual connectivity enabled networks from a relatively less-investigated yet more tractable flow-level perspective rather than a packet-level perspective. Unlike existing models, the framework developed jointly accounts for different transmission capacities and utilizations of base stations, thus enabling a proper and comprehensive assessment of user-perceived file transfer delays. We analyze the optimum file splitting ratio for reducing download delays using convex optimization and validate our findings via both queueing network and flow-level wireless simulations. Our in-house flow-level wireless simulator takes into account user locations and macroscopic propagation characteristics of wireless channels in order to create a realistic evaluation environment; we observe that optimal splitting under heavy-traffic conditions can result in up to 60% reduction in download delays for commonly encountered wireless system specifications when the macrocell and small cell base stations operating with different transmission capacities both have high utilizations. We further illustrate that our flow-level model can successfully incorporate interfering sources and different transmit powers which can easily be subsumed into the transmission capacities used in the model. Overall, the results presented show that it is indeed crucial to consider different transmission capacities as well as utilizations of base stations when determining the optimum splitting ratio.