User and Application Aware Dynamic Cell-Mobile Device Reconfiguration for Green Cellular Network
Evolving 4G networks offering high data rates and enhanced coverage, and mobile devices capable of high quality video and web browsing, are the key drivers leading to explosive growth in mobile multimedia traffic. Since multimedia data is both data intensive, and compute intensive, this trend is leading to steep increases in the power consumption of mobile networks, particularly of base stations and mobile devices albeit on different scales. While factors such as cell sizes, spatio-temporal fluctuations in number and physical location of network users, and the requirements of these usersâ€™ applications affect power consumption of base stations, multimedia applications place significantly more demand on the amount of data that needs to be wirelessly downloaded and processed, and hence on the power and battery consumption of the mobile devices.
In order to address the challenges of increasing cellular network power consumption due to the explosive growth in multimedia traffic, in this project we explore the design of "green" cellular network architectures and algorithms to reduce energy consumption. We propose solutions to reduce power consumption of both base stations and mobile devices: dynamic reconfiguration of base stations to configure cell sizes and capacity in response to changing cellular traffic to minimize power consumption of base stations, and base station and mobile device reconfiguration techniques that lower mobile device battery consumption due to multimedia applications.The proposed solutions will significantly reduce the power demands of base stations and mobile devices realizing end to end green cellular network which not only provides ubiquitous connectivity, but also caters to ever increasing popular applications such as mobile video and gaming.
- Rate Adaptation and Base Station Reconfiguration for Battery Efficient Video Download
- Lowering Power Consumption of Base Station by Dynamic Cell Reconfiguration
Rate Adaptation and Base Station Reconfiguration for Battery Efficient Video Download
In this research, we aim to model the power and battery consumption of mobile devices due to multimedia applications, and develop techniques which simultaneously adapt the data download rate and base station and mobile baseband modes, to lower the battery consumption. We choose mobile video as the representative multimedia application because mobile data trends indicate that by 2015, mobile video will contribute to about two thirds of the total data traffic, making it the leading multimedia application on mobile devices. While the screen resolutions and processing capabilities of mobile devices continue to increase significantly enabling mobile video applications, the accompanying steep increase in power consumption and lack of comparable improvements in current battery technologies makes it critical to develop techniques that can lower mobile video battery consumption.
Battery consumption due to mobile video download and playback is predominately due to (1) RF and base band components used for downloading video data, and (2) video decoder and display used for video playback. With the adoption of Multi Input-Multi Output (MIMO technologies that use multiple antennas and more power consuming baseband processing, the power due to RF and baseband components will likely increase significantly, and dominate the power consumption for high bit-rate mobile video applications. Hence, our research focuses on reducing the battery demand imposed by the MIMO RF and baseband components while downloading video. Our approach utilizes the elasticity of buffer to vary video download rate and reconfigure the base station in a way that reduces the battery load imposed by the RF and baseband processing on the mobile device. Also, if buffer level permits, video download and hence the related processing on the mobile device is stopped, enabling battery idling and charge recovery thereby increasing battery lifetime.Specifically,our techniques aim to reduce the battery consumption during mobile video download which is poised to become an important driver of mobile device usage. Below is the summary of outcome of this research:
- Power model that estimates the joint power consumption of base station and mobile device due to mobile video
- Rate adaptation and base station reconfiguration techniques that minimizes the mobile device battery consumption during mobile video download while ensuring user experience and video quality
Lowering Power Consumption of Base Station by Dynamic Cell Reconfiguration
In this research, we aim to develop Dynamic Cell Reconfiguration (DCR) techniques which dynamically reconfigures cell sizes and capacity in response to changing cellular traffic and user requirements to minimize power consumption of base stations while satisfying the Quality of Service (QoS) requirements of users. Our approach consists of the following techniques: (a) active base station selection; (b) user assignment and (c) transmit power adaptation. Active base station selection involves deciding which base stations remain on (or need to be switched on) in order to satify the QoS requirements of all users in the network and which base stations need to be switched off in order to minimize base station power consumption. User association focuses on load balancing for base stations and decides which base station the user should be assigned to in order to optimize the utilization of base stations. Adapting transmit power aims at determining the optimal transmit power of base stations required to satisfy the QoS requirements of users and minimize the power consumption of base stations. Further, we will invesitgate the dynamic cell reconfiguration techniques coupled with battery aware techniques that minimize mobile device battery consumption due to multimedia applications enabling deployment of end to end "green cellular networks."
Renewable Energy-Aware Video Download in Cellular Networks
The use of renewable energy (RE) sources is a promising solution to reduce grid power consumption and carbon dioxide emissions (CO2e) of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature leading to mismatch between generation and base station (BS) needs, resulting in low savings in grid power. To address the above challenges, we propose a novel base station time resource allocation technique using data storage at user equipments (UEs) to optimally utilize renewable energy to reduce grid power consumption. The proposed approach transforms the surplus RE (in excess of the BS power requirements) to excess data delivered to the users and stored in UE data storages, to draw from in deficit periods (when RE generation is lesser than BS requirements) to reduce grid power. Though the proposed approach is applicable to any application that utilizes UE data buffer, we formulate the problem for mobile video and propose a algorithm for RE aware BS resource allocation during mobile video download, as the latter will dominate wireless traffic and hence BS power consumption. Our experimental results using sample solar and BS utilization traces demonstrate the ability of the proposed approach to reduce grid power consumption by increasing solar power utilization while satisfying user QoS requirements.