Optimization of N-out-of-K Rule

Banavathu, N R and Khan, Mohammed Zafar Ali (2018) Optimization of N-out-of-K Rule. PhD thesis, Indian institute of technology Hyderabad.

Full text not available from this repository. (Request a copy)


The explosive growth in wireless systems and services has led to spectrum scarcity in wireless communications due to ever-increasing demand for higher data rates. The shortage of useful radio spectrum is due to static allocation to specific services and rigid regulation of the spectrum usage rather than efficient utilization of the radio spectrum. Cognitive radio (CR) technology has been proposed to alleviate the spectrum shortage problem and the spectrum underutilization of current radio spectrum by allowing the cognitive users (CUs) to access spectrum of the licensed or primary user (PU) under sufficient protection to the PU. Therefore, spectrum sensing is a fundamental component for the CU to accurately detect the activities of the PU. However, spectrum sensing using single CU results in poor detection performance due to mulipath and hidden terminal problem. Therefore, cooperative spectrum sensing (CSS) is proposed to enhance the detection accuracy by taking the advantage of spatial diversity in the multiple CU observations. In this thesis, we consider decision based CSS, where in each CU makes a binary decision on the activity of the PU and the decisions are reported to the FC, where they are combined using N-out-of-K rule, i.e., at least N CUs must favour for the presence of the PU out of K CUs. Most existing works in the literature on N-out-of-K rule are for homogeneous CR networks, where CUs are assumed to have identical local false alarm and identical detection probability. However, in practice, the sensing accuracy will vary since the CUs are at different distances from the PU. In this thesis, we consider heterogeneous CSS using the N-out-ofK rule, wherein spatially dispersed CUs operate at different local false alarm, detection probabilities and detect the activities of the PU using the N-out-of-K rule. We obtain the generalized expressions for the global false alarm and global detection probabilities for the N-out-of-K rule for the heterogeneous CR network in the presence of control channel errors and the proposed solution specializes to various existing results. We then propose Neyman-Pearson (N-P) test which maximizes the global detection probability of the N-out-of-K rule for a given target global false alarm probability at the FC. We show limitations on the target global false alarm probability due to control channel errors. We then consider the optimization of N-out-of-K rule for the heterogeneous CR network under Bayesian test and show that several existing works are special cases of the proposed solution. We also obtain a most generalized expression for optimal K for the homogeneous CR network and show that various results are special cases of the proposed solution. A joint optimization problem is formulated to optimize both N and K under Bayesian test. The performance of the CSS obtained using joint optimized values of N and K results in significant improvement. Finally, we propose, optimization of N-out-of-K rule for maximizing the average channel throughput (ACT) of the heterogeneous CR network. We present asymptotic expressions for the optimal N and the optimal K by maximizing the ACT under adequate protection to the PU. Note that the mathematical results derived in this thesis are general and are applicable to any detector used in CSS. However, we choose the energy detector (ED) as an example to verify the theoretical findings

[error in script]
IITH Creators:
IITH CreatorsORCiD
Khan, Mohammed Zafar AliUNSPECIFIED
Item Type: Thesis (PhD)
Uncontrolled Keywords: Discrete Optimazation, Neyman-Pearson Test, Baysein Test, heterogeneous Network
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 05 Dec 2018 06:19
Last Modified: 21 Sep 2019 10:14
URI: http://raiith.iith.ac.in/id/eprint/4614
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4614 Statistics for this ePrint Item