PROJECT TITLE :
Two-Dimensional Optimization on User Association and Green Energy Allocation for HetNets With Hybrid Energy Sources
In inexperienced communications, it's imperative to reduce the total on-grid energy consumption as well as minimize the peak on-grid energy consumption, since the big peak on-grid energy consumption will translate into the high operational expenditure (OPEX) for mobile network operators. During this paper, we consider the 2-dimensional optimization to lexicographically minimize the on-grid energy consumption in heterogeneous networks (HetNets). All the bottom stations (BSs) therein are envisioned to be powered by each power grid and renewable energy sources, and also the harvested energy will be stored in rechargeable batteries. The lexicographic minimization of on-grid energy consumption involves the optimization in both the space and time dimensions, because of the temporal and spatial dynamics of mobile traffic and green energy generation. The reasonable assumption of your time scale separation allows us to decompose the problem into 2 sub-optimization problems while not loss of optimality of the first optimization downside. We tend to initial formulate the user association optimization in area dimension via convex optimization to reduce total energy consumption through distributing the traffic across different BSs appropriately in an exceedingly certain time slot. We then optimize the inexperienced energy allocation across completely different time slots for a personal BS to lexicographically minimize the on-grid energy consumption. To resolve the optimization drawback, we tend to propose a coffee complexity optimal offline algorithm with infinite battery capacity by assuming non-causal inexperienced energy and traffic information. The proposed optimal offline algorithm serves as performance higher bound for evaluating sensible on-line algorithms. We have a tendency to more develop some heuristic online algorithms with finite battery capability which need only causal green energy and traffic data. The performance of the proposed optimal offline and online algorithms is evaluated by simulations.
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