This thesis proposes four novel techniques to solve the problem of growing energy consumption requirements in cellular communication networks. The first and second part of this work propose a novel energy efficient scheduling mechanism and two new bandwidth management techniques, while the third part provides an algorithm to actively manage the power state of base stations (BSs) so that energy consumption is minimized throughout the day while users suffer a minimal loss in achieved data rate performance within the system. The proposed energy efficient score based scheduler (EESBS) is based on the already existing principle of score based resource allocation. Resource blocks (RBs) are given scores based on their energy efficiency for every user and then their allocation is decided based on a comparison between the scores of the different users on each RB. Two additional techniques are introduced that allow the scheduler to manage the user’s bandwidth footprint or in other words the number of RBs allocated. The first one, bandwidth expansion mode (BEM), allows users to expand their bandwidth footprint while retaining their overall transmission data rate. This allows the system to save energy due to the fact that data rate scales linearly with bandwidth and only logarithmically with transmission power. The second technique, time compression mode (TCoM), is targeted at users whose energy consumption is dominated by signalling overhead transmissions. If the assumption is made that the overhead is proportional to the number of RBs allocated, then users who find themselves having low data rate demands can release some of their allocated RBs by using a higher order modulation on the remaining ones and thus reduce their overall energy expenditure. Moreover, a system that combines all of the aforementioned scheduling techniques is also discussed. Both theoretical and simulation results on the performance of the described systems are provided. The energy efficient hardware state control (EESC) algorithm works by first collecting statistical information about the loading of each BS during the day that is due to the particular mobility patterns of users. It then uses that information to allow the BSs to turn off for parts of the day when the expected load is low and they can offload their current users to nearby cell sites. Simplified theoretical, along with complete system computer simulation, results are included. All the algorithms presented are very straightforward to implement and are not computationally intensive. They provide significant energy consumption reductions at none to minimal cost in terms of experienced user data rate.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:586352 |
| Date | January 2013 |
| Creators | Videv, Stefan |
| Contributors | Haas, Harald; Thompson, John |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/7988 |
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