Energy-saving resource allocation by exploiting the context information

Abstract

Improving energy efficiency of wireless systems by exploiting the context information has received attention recently as the smart phone market keeps expanding. In this paper, we devise energy-saving resource allocation policy for multiple base stations serving non-real-time traffic by exploiting three levels of context information, where the background traffic is assumed to occupy partial resources. Based on the solution from a total energy minimization problem with perfect future information, a context-aware BS sleeping, scheduling and power allocation policy is proposed by estimating the required future information with three levels of context information. Simulation results show that our policy provides significant gains over those without exploiting any context information. Moreover, it is seen that different levels of context information play different roles in saving energy and reducing outage in transmission.

Energy-Saving Pushing Based on Personal Interest and Context Information

“…(4b) is the transmission rate constraint of pre-downloading B bits within T time slots, (4c) is the power constraint of the SBSs. The optimal solution of problem (4) satisfies the following multi-level water-filling structure [10]…”

“…, L are known at the SBS, which is reported to the MBS as the network level context information. Note that only P j L (i.e., the probability that a SBS is not occupied by RT traffic) is assumed known in [10]. • User level context information:…”

Role of large scale channel information on predictive resource allocation

“…As the smart phone popularizes, improving the performance of mobile networks such as energy efficiency (EE) by exploiting context information has drawn attentions [1][2][3][4][5].…”

“…By contrast, the large-scale channel gains can be estimated from user locations and the radio map [2], which however has estimation errors due to the prediction error of locations and the measurement error of the signal strength as reported by [10]. Recently, the simulation results provided in [5] demonstrate that the power-saving gain obtained from knowing the future large-scale channel gains is almost the same as that provided by perfectly knowing all the future instantaneous channel gains. This observation motivates a natural question to ask: under what condition can we only use large-scale channel information to achieve the potential of predictive resource allocation?…”

“…In this paper, we strive to answer the question by examining the solution of an energy-saving predictive resource allocation. To reveal the essential role of the large-scale channel information, we consider a traffic with long deadline, which is modeled as transmitting a given amount of data in a long duration, and simplify the system model in [5] to a single user scenario without background traffic. From the optimal solution of an energy minimization problem with perfect future channel information, we show that only two key parameters in the solution, threshold and water-filling level, depend on the future channels.…”