Resource Allocation and Energy Efficiency in Wireless Networks

Resource Allocation and Energy Efficiency

The demand for wireless data traffic increases exponentially. To deliver much higher data rates in future networks, the transmission resources must be utilized more efficiently than today.

To keep up with the rapid growth in wireless data traffic, an ambitious but necessary goal for the next generation wireless networks is to deliver 1000x higher data rates per km² than today. The key to handling such an orders-of-magnitude increase is efficient radio resource allocation; that is, to utilize the available time and frequency resources for wireless data transmission as efficiently as possible. Although it is possible to acquire new frequency bands for wireless networks, it is physically impossible to find more than twice the spectrum in the bands suitable for wide-area coverage. To reach up to 1000x, we investigate how to reuse a given amount of spectrum in the spatial domain; that is, to improve the data throughput per km² by having as many concurrent transmissions per km² as possible.

There are two dominant approaches to increase the spatial reuse of spectrum. The first one is to deploy many WiFi-like access points with small coverage areas, while the other one is to evolve the current outdoor access points by using multiple-antenna technology. These approaches are both being used in practice. The main impairment that needs to be tackled is the interference created by the simultaneous use of the spectral resources in adjacent areas.

Our research deals with different aspects of optimized usage of transmission resources in wireless networks. One aspect is interference mitigation by optimizing the transmit power control, user-cell association, and beamforming. Another aspect is network topology design, where the density of access points and the hardware configuration at each access point are optimized to achieve a resource-efficient network. A variety of different performance metrics can be used when evaluating and optimizing the resource allocation; for example, the area data rates (bit/s/km²), the data rate per user (bit/s/user), the energy efficiency (bit/Joule), or the number of simultaneously connected users. The picture above illustrates the tradeoff that exists between these different metrics in a practical communication systems. In our research, we are either dealing with one metric at a time or consider a collection of metrics using multiple-objective optimization.

This research is supported by the following projects:

Holistic energy efficiency optimization in cellular networks (2015-2018)
Funded by the Swedish Foundation for Strategic Research (SSF).
Project leader: Emil Björnson

Optimized Design of Wireless Networks with Multiple Performance Metrics (2016-2019)
Funded by the Swedish Research Council.
Project leader: Emil Björnson


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