Low Complexity Margin Adaptive Allocation for SC-FDMA

SC-FDMA

In Chapters 6 and 7 we presented a low complexity algorithm for margin adaptive (MA) resource allocation for localize SC-FDMA. The algorithm iteratively allocated frequency resource to maximize thepower level gain per iteration. While this method was shown to perform close to optimal power allocation for smaller transport block (TB) sizes, this gap deviates for very large blocks.

While efficient MA methods have been studied heavily for OFDM, SC-FDMA has received significantly less attention. This is particularly important, as this tech- nology has become popular in recent years due to its low peak-to-average power ratio (PAPR) as a solution for mobile devices. More importantly, it is these devices where MA is of greater importance due to their battery limited nature. It is therefore im- portant to design low-complexity, near optimal MA algorithms that work under a wide range of conditions (i.e.,channels, TB sizes, etc) in future work.

8.2.6

Frequency Correlated Performance of SC-FDMA

Allocation Methods

In Chapters 6 and 7, we assumed a block fading model where each channels were also independent as a function of user and RBs in frequency. In realistic conditions, there may be some correlation in frequency between the SNR of individual channels. Intuitively, such channels should improve the performance of our iterative allocation schemes as for a given initial allocation, there is a higher probability of finding a good quality adjacent channel. This however, may not be true as the same can be said for an initially poor selected channel. It is therefore important to study this impact on MA iterative allocation algorithms, particularly for localized SC-FDMA in future work.

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Appendix A

Probability Theory and Finite State

Markov Channels

Throughout this thesis, there are a number of mathematical tools employed. For clar- ity, this chapter overview various mathematical tools, fundamentals and definitions that are used throughout this thesis including probability theory, finite state Markov chains and optimization techniques/formulations.