Optimization of Random Phase Updating Technique for Effective Reduction in PAPR, Using Discrete Cosine Transform
Journal Title: Journal of Information Systems and Telecommunication - Year 2016, Vol 4, Issue 2
Abstract
One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random phase updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the complexity of this method would be less than other phase injection methods which could be an important factor. Another interesting capability of random phase updating technique is the possibility of applying the variance of threshold power. The operation of phase injection is repeated till the power variance reaches threshold power variance. However, this may be a considered as a disadvantage for random phase updating technique. The reason is that reaching the mentioned threshold may lead to possible system delay. In this paper, in order to solve the mentioned problem, DCT transform is applied on subcarrier outputs before phase injection. This leads to reduce the number of required carriers for reaching the threshold value which results in reducing system delay accordingly.
Authors and Affiliations
Babak Haji Bagher Naeeni
Keywords
Related Articles
- EP ID EP184440
- DOI 10.7508/jist.2016.02.004
- Views 129
- Downloads 0
Coreference Resolution Using Verbs Knowledge
Coreference resolution is the problem of determining which mention in a text refer to the same entities, and is a crucial and difficult step in every natural language processing task. Despite the efforts that have been m...
A Global-Local Noise Removal Approach to Remove High Density Impulse Noise
Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filt...
A new Sparse Coding Approach for Human Face and Action Recognition
Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the spars...
Improved Generic Object Retrieval In Large Scale Databases By SURF Descriptor
Normally, the-state-of-the-art methods in field of object retrieval for large databases are achieved by training process. We propose a novel large-scale generic object retrieval which only uses a single query image and t...
Fusion of Learning Automata to Optimize Multi-constraint Problem
This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. S...