Chuting Yao

Energy-Saving Pushing Based on Personal Interest and Context Information

Pushing files to users based on predicting the personal interest of each user may provide higher throughput gain than broadcasting popular files to users based on their common interests. However, the energy consumed at base station for pushing files individually to each user is also higher than broadcast. In this paper, we propose an energy-saving transmission strategy for pre- downloading the files to each user by exploiting the excess resources in the network during off-peak time. Specifically, a power allocation and scheduling algorithm is designed aimed to minimize the extra energy consumed for pushing, where network and user level context information are exploited. Simulation results show that when the energy of both content placement and content delivery is taken into account, the proposed unicast strategy consumes less energy and achieves higher throughput than broadcasting when the files popularity is not uniform and the personal interest prediction is with less uncertainty.

Energy-Saving Predictive Resource Planning and Allocation

Predictive resource allocation is an emerging approach to improve the performance of mobile systems as human behavior is reported predictable by leveraging big data analytics. Yet what information can be predicted by big data, what information need to be predicted for wireless access optimization, how to translate the information, and how to exploit the synthetic knowledge for allocating radio resources are not well understood and largely explored. In this paper, we are concerned with the latter two issues. In particular, we devise an energy-saving resource planning and allocation policy for multiple base stations (BSs) to serve mobile users with non-real-time (NRT) traffic by exploiting the user, network, and application levels of context information, where RT traffic may occupy partial resources of each BS. Inspired by the solution from an energy minimization problem with future instantaneous information, a low complexity multi-timescale predictive policy is proposed. Upon the arrival of each NRT user request, the resource planning is made with the user and network level context information, defined as the average channel gains of the NRT users and the statistics of residual bandwidth after serving RT traffic, with which the scheduling, power allocation, and BS sleeping can be accomplished after instantaneous channel information and residual network resource are available at each BS in each time slot. Simulation results show that the proposed policy can dramatically reduce the energy consumed by the BSs for serving the NRT traffic.

Role of large scale channel information on predictive resource allocation

When the future achievable rate is perfectly known, predictive resource allocation can provide high performance gain over traditional resource allocation for the traffic without stringent delay requirement. However, future channel information is hard to obtain in wireless channels, especially the small-scale fading gains. In this paper, we analytically demonstrate that the future large-scale channel information can capture almost all the performance gain from knowing the future channel by taking an energy-saving resource allocation as an example. This result is important for practical systems, since large-scale channel gains can be easily estimated from the predicted trajectory of mobile users and radio map. Simulation results validate our analysis and illustrate the impact of the estimation errors of large-scale channel gains on energy saving.

Achieving high throughput with predictive resource allocation

Big data analytics makes predicting human behavior possible, but it is unclear how to exploit the predictable information for improving performance of wireless networks. In this paper, we investigate the potential of predictive resource allocation in supporting high throughput by exploiting excess resources. To this end, we assume that the requests and trajectories of mobile users and the average resource usage status of base stations can be predicted within a window. To fully use resources within the prediction window and reserve resources for the unpredictable traffic arrived after the window, we optimize a resource allocation plan to minimize the maximal transmission completion time. To assist the base stations for user scheduling, we introduce a method to make a transmission plan. These two plans determine where, when and what to transmit to the users with how much resources. Simulation results show that the predictive resource allocation can provide substantial gain over non-predictive strategy in terms of both network throughput and user experience.

Data-driven resource allocation with traffic load prediction

Wireless big data is attracting extensive attention from operators, vendors and academia, which provides new freedoms in improving the performance from various levels of wireless networks. One possible way to leverage big data analysis is predictive resource allocation, which has been reported to increase spectrum and energy resource utilization efficiency with the predicted user behavior including user mobility. However, few works address how the traffic load prediction can be exploited to optimize the data-driven radio access. We show how to translate the predicted traffic load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example. By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information, we not only provide a performance upper bound, but also reveal that only two key parameters are related to the future information. By exploiting the residual bandwidth probability derived from the traffic volume prediction, the two parameters can be estimated accurately when the transmission delay allowed by the user is large, and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches infinity. We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large. Simulation results validate our analysis, show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies, and illustrate that the time granularity in predicting traffic load should be identical to the delay allowed by the user.

Impact of uncertainty in predicting the user's request on pushing

Pushing contents to users based on the predicted user preference cannot only improve user experience, but also boost network throughput by exploiting the excess resources. However, prediction is never perfect. Due to the uncertainties of predicting the users request, the base station (BS) may waste resources on pushing unnecessary files before the arrival of users request. In this paper, we investigate the impact of the uncertainty in predicting the content to be requested and the request arrival time on the average energy consumption of pushing. To this end, we first introduce a pushing policy with a priori known prediction uncertainty. Then, we derive the average energy consumption of pushing, and analyze the energy saving gain over traditional transmission method, where the BS serves a user right after the request arrives. Analytical and numerical results show that pushing with prediction uncertainty can save energy by optimizing the number of the pushing files, and the gain is remarkable for a user who has stronger preference among the predicted file list.

Energy-saving resource allocation by exploiting the context information

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.

Is Accumulative Information Useful for Designing Energy Efficient Transmission

Energy efficiency (EE) has become an important design goal for mobile communication systems. Since the EE of a system is evaluated during a period of time, a transmission strategy designed from maximizing instantaneous EE (INEE) may not achieve the maximal EE of the system. To exploit the accumulative nature of the energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated amount of data transmitted to the accumulated energy consumed until the time for optimization. In this paper, we study when ACEE is beneficial to EE-oriented optimization. By taking a single user multi-antenna multi-subcarrier system serving two classes of traffic as an example, we formulate three problems to optimize rate allocation among subcarriers and time slots respectively maximizing the INEE, ACEE and EE upper-bound, which can be easily extended to multi-user systems. We proceed to analyze the behavior and performance of the corresponding solutions. Analytical and simulation results show that using ACEE yields a more energy efficient design for the systems serving best effort traffic with less transmit antennas, serving less users simultaneously or at low signal to noise ratio under time-varying channels. However, the conclusions for real-time traffic are different.

When accumulative information is beneficial for maximizing energy efficiency

Energy efficiency (EE) has become an important design goal for wireless systems. Since the EE of a system is evaluated in a duration where the channel may vary, designing a transmission strategy to maximize instantaneous EE may lead to a loss in achievable EE of the system. To exploit the accumulative information of throughput and energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated throughput divided by the overall energy consumed over the past time slots. By analyzing the solutions of three EE maximal problems to optimize rate allocation among multiple subcarriers in multiple time slots, we show when and why the ACEE can achieve high system EE. Simulation results verify our theoretical analysis. Our analysis show that the ACEE is beneficial for the systems with low circuit power consumption under time-varying channels when either the data rate requirement or signal to noise ratio is low, and either the number of antennas or subcarriers is small.