University of Oulu

Implementation consideration of M2M4 SINR estimation algorithm

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Author: Bashir, Nouman1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Communications Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.6 MB)
Pages: 50
Persistent link:
Language: English
Published: Oulu : N. Bashir, 2016
Publish Date: 2016-12-21
Thesis type: Master's thesis (tech)
Tutor: Latva-aho, Matti
Reviewer: Latva-aho, Matti
Saarnisaari, Harri
Efficient use of wireless spectrum is needed, due to enormous increase in wireless devices during last few years. In this context lot of effort is being done to make an intelligent and cognitive radio system, which can use the spectrum opportunistically. The ratio of the signal average power to the interference plus noise average power is called signal to interference plus noise ratio (SINR). SINR is one of the important parameters that can help in developing cognitive radio systems, because on the basis of its calculation the spectrum can be utilized efficiently. The principle goal of this thesis is to implement a SINR estimation algorithm for a cognitive radio network (CRN) test-bed. The proposed SINR estimation algorithm is second order moment and fourth order moment (M2M4) SINR estimation algorithm, where M2 and M4 are the second order moment and fourth order moments respectively. The M2M4 estimation algorithm is one of the non-data-aided (NDA) estimation algorithms. Hence, the algorithm takes the received signal as input and calculates the second and fourth moments blindly. The average signal power and average interference plus noise power can be calculated from these second and fourth order moments, their ratio yields the SINR. The M2M4 estimation algorithm is first simulated in MATLAB, and then it is designed for system generator model to draw fair comparison between simulations and system generator model. The experimental evaluation revealed that despite of the word length constraint in the system generator model, it performs reasonably well when compared to the ideal (MATLAB) solution. The M2M4 estimation algorithm is tested and verified by different test cases, to ensure its validity. The algorithm is tested for different signal strengths. The result shows M2M4 is an efficient algorithm for the SINR estimation. However, the proposed architecture could not fit into the aimed hardware because of heavy design since it consume more resources than available.
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