Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The ever increasing data demand has led to the significant increase of energy consumption in cellular networks. ,  study the problem of energy cost saving in heterogenous cellular networks with hybrid energy supplies. Owing to the diversities of mobile traffic and renewable energy, the energy cost saving problem involves both temporal and spatial dimensional optimization.  decompose the whole problem into three sub-problems and correspondingly our proposed solution is divided into three parts: First,  obtain average estimated energy consumption profiles for all base stations; Second,  allocate the green energy for each base station to minimize its energy cost based on its estimated energy consumption profile; Third, given the allocated green energy in the current slot,  perform the user association to minimize the total energy cost of the network. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.
['Qiang Yang', 'Bang Wang']
I am writing a research paper on a idea Energy cost minimization via intelligent temporal and spatial resource allocation in green heterogeneous cellular networks
[{"from": "human", "value": "I am writing a research paper on a idea Energy cost minimization via intelligent temporal and spatial resource allocation in green heterogeneous cellular networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The ever increasing data demand has led to the significant increase of energy consumption in cellular networks. ,  study the problem of energy cost saving in heterogenous cellular networks with hybrid energy supplies. Owing to the diversities of mobile traffic and renewable energy, the energy cost saving problem involves both temporal and spatial dimensional optimization.  decompose the whole problem into three sub-problems and correspondingly our proposed solution is divided into three parts: First,  obtain average estimated energy consumption profiles for all base stations; Second,  allocate the green energy for each base station to minimize its energy cost based on its estimated energy consumption profile; Third, given the allocated green energy in the current slot,  perform the user association to minimize the total energy cost of the network. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost."}]