<p>One goal in the development of future cellular systems is to increase performance. Channel dependent scheduling can possibly contribute to a performance enhancement. It requires channel qualityinformation and uplink channel knowledge is often incomplete. This master thesis work compares channel dependent scheduling and channel independent scheduling for a Single Carrier Frequency Division Multiple Access-based uplink in time domain and time and frequencydomain assuming continuous channel quality information updates. It also evaluates different methods for providing channel quality information by investigating how the limited channel knowledge they supply affects the performance of channel dependent scheduling.</p><p>Single-cell simulations with perfect channel knowledge indicate small gains for channel dependent scheduling. Large gains are seen when performing frequency and time domain scheduling instead of only time domain scheduling. Limited channel knowledge causes performance loss for channel dependent scheduling. The performance is only slightly decreased if a method with sufficiently frequent providing of channel quality information updates is applied.</p><p>More realistic multi-cell simulations show large gains for channel dependent scheduling. It is possible that these results are influenced by link adaptation and scheduling problems due to non predictable interference when performing dynamic scheduling. In the comparison between channel dependent and channel independent scheduling the channel dependent scheduling can benefit from the fact that the selected channel dependent scheduling algorithms result in a more static scheduling than the selected channel independent scheduling algorithms do.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-8213 |
| Date | January 2007 |
| Creators | Jersenius, Kristina |
| Publisher | Linköping University, Department of Electrical Engineering, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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