17:30 - 18:30 | Fri 17 Mar | Main Room | S8
Leveraging massive databases about mobile user activities, one way is to exploit information about user profiles (e.g. rate requirements, traffic type, etc) and allocate resources according to their needs. To study this, in this paper, users are assumed to belong to two communities which differ in their rate requirement in heterogeneous network setting. Both base-stations (BS) and users are modeled according to independent homogeneous Poisson Point Processes (PPP). To demonstrate the advantage of user classification, two sleeping strategies for small cells (SC) are investigated to improve energy efficiency. It is shown that using rate-based user classification in devising sleeping strategies provides better energy consumption and fair resource allocation compared to obliviously allocating maximum rate for all the users.
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