Demand estimation in dense and efficient small cell architectures

Abstract

We consider a heterogeneous network scenario where macro cells provide continuous coverage and many small cells with short transmission ranges within the coverage area of each macro cell provide high capacity access. We focus on scenarios where most small cells can be put on the sleep mode and macro cell capacity is sufficient for serving a large fraction of user population. Few users generate high demand and thus a small fraction of small cell base stations (SC-BS) that can serve these users should be ideally powered on to offload the macro cell base station (MC-BS). As different sets of users create demand surge over time, a network controller should keep track of peak demand locations and power on a minimal number of SC-BSs. We propose a new solution to this end. Our solution iteratively determines which SC-BSs should be turned on and performs power control to dictate the coverage area of each SC-BS. We use the flow byte counters on the MC-BS and active SC-BSs to form a system of linear equations and use linear optimization using L 1 norm of the demand vector as the objective function to localize the demand. Our evaluations indicate that the solution is quite effective in tracking the peak demand locations. For instance in one particularly challenging scenario with heavy-tailed i.i.d. spatial distribution for peak demand, 80% of the peak demand locations were identified correctly more than 90% of the time using less than 7% of the SC-BSs.