This chapter summarizes the contributions of this thesis and the conclu- sions that can be drawn from them. In addition, it includes a discussion on research direction for possible future work.
7.1 Conclusions
The focus of this thesis has been on the development of machine learning al- gorithms for spectrum sensing within the context of cognitive radio wireless networks. In particular, supervised, semi-supervised and unsupervised learn- ing algorithms have been proposed and investigated for interweave spectrum sharing. Furthermore, novel eigenvalue based features have been proposed and shown to possess the capability to improve the performance of SVM classifiers for spectrum sensing under multi-antenna consideration. In addi- tion, novel beamformer based pre-processing technique has been developed for improving the quality of the features and enhancing the performance of the learning algorithms. For the investigation, probability of detection, probability of false alarm, receiver operating characteristics (ROC) curves and area under ROC curves have been used to evaluate the performance of the proposed schemes. Considering the chapters in detail:
In Chapter 1, the current command and control approach to frequency 166
Section 7.1. Conclusions 167
allocation was described. The spectrum scarcity and under-utilization prob- lems was also introduced. Furthermore, a general description of CR technol- ogy and various paradigms as viable solutions to spectrum scarcity problem were discussed. In addition, the role of spectrum sensing in the successful implementation of CR systems was highlighted. This is followed by rationale behind the choice of machine learning techniques for the schemes proposed in this thesis was provided. The chapter is concluded with an outline of the thesis structure and brief discussion of contributions made.
In Chapter 2, an overview of the various local spectrum sensing method- ologies in CR networks that are of interest to the thesis was presented. In particular, we reviewed blind and semi blind methods suitable for both sin- gle and multi-antenna conditions. These include methods such as matched filtering, cyclostationary detection, energy detection and hybrid schemes. Cooperative sensing methods which enables multiple SUs take advantage of spatial diversity for improving detection performance and containing the effects of channel imperfections is also briefly described.
In Chapter 3, various supervised classification algorithms were proposed and investigated for spectrum sensing application in CR networks. Multi- antenna CR networks was considered and a novel, eigenvalue based feature which has the capability to enhance the performance of SVM algorithms was proposed. Furthermore, spectrum sensing under multiple PU scenarios was given attention and a new re-formulation of the sensing task as a multiple hypothesis problem comprising multiple classes where each class embeds one or more states was presented. Generalized expressions for the various possi- ble states was also provided. In addition, the ECOC based multi-class SVM algorithms for solving the ensuing multiple class signal detection problem was investigated using two different coding strategies. Finally, simulation studies was included which lends credence to the robustness of the proposed sensing schemes.
Section 7.1. Conclusions 168
In Chapter 4, scenarios where the secondary network has only partial knowledge about the PU’s network was considered. Two semi-supervised parametric classifier that are based on the K-means and the EM algorithms were proposed for spectrum sensing purpose. Furthermore, it was recognized that the performance of the classifiers can degrade severely when they are deployed for sensing under slowly fading channel resulting when mobile SUs operate in the presence of scatterers. To address this problem, a Kalman filter based channel estimation strategy was proposed for tracking the fading channel and updating the decision boundary of the classifiers in real time. Simulation studies was presented which confirmed that the proposed scheme offers significant gain in performance.
In Chapter 5, the unsupervised classification algorithms based on the soft assignment, variational Bayesian learning framework was presented. Unlike the supervised and semi-supervised methods, the technique does not require any prior knowledge about the number of active PUs operating in the net- work and can successfully estimate this and other statistical parameters that are required for decision making. The proposed inference algorithm is thus blind in nature and lends itself readily for autonomous spectrum sensing ap- plication making it useful when an SU finds itself in alien RF environment. Simulation studies reveals that with few cooperating secondary devices, an overall correct detection rate of about 90% and above can be achieved, with the false alarm rate kept at 10% when the number of collected signal samples approaches 10000.
In Chapter 6, a novel beamforming based pre-processing technique for feature realization was presented for enhancing the performance of classi- fication algorithms under multi-antenna consideration. Furthermore, new algorithms were developed for multiple hypothesis testing facilitating joint spatio-temporal spectrum sensing. Using energy features and the error cor- recting output codes technique, the key performance metrics of the classifiers
Section 7.2. Future Work 169
were evaluated which demonstrate the superiority of the proposed methods in comparison with previously proposed alternatives.
In summary, in this thesis, firstly the practicality of adopting and apply- ing machine learning algorithms for spectrum sensing purpose in CR net- works was clearly demonstrated. Particularly, supervised, semi-supervised and unsupervised classification based sensing algorithms were developed. The proposed schemes are blind in the sense that the exact knowledge of the PU signal, noise or the channel gain is not required. Secondly, the problem of spectrum sensing under time varying channel condition occasioned by the mobility of SUs in the presence of scatterers was considered and a Kalman filter estimation based technique was proposed for channel tracking and for updating the decision boundary in real time towards enhancing the classi- fiers performance. Finally, a novel feature realization strategy was proposed for improving the performance of learning algorithms deployed for spectrum sensing application in CR networks.
7.2 Future Work
The research presented in this thesis could be extended in several directions. Firstly, the cooperating sensing problem in Chapter 3 can be extended by considering the application of game theoretic techniques such as the over- lapping coalitional game [90]. In this case, the SUs may first be clustered based on certain criteria so that instead of having all SUs send their sensing results to the SBS, only cluster heads do. Under this situation, it is possible to have one or more SUs located in overlapping regions of multiple clusters and SUs have to decide where to report. In addition, we have assumed that the reporting channel between the SUs and SBS is error free. In practical CR deployment, such channels may exhibit some imperfections. The impacts of this imperfection on the overall system performance should be analyzed and
Section 7.2. Future Work 170
ways of mitigating these effects be investigated.
Secondly, in Chapter 4, tracking PU-SU channel gain under Rayleigh distributed, flat fading environment was assumed and considered. A wider class of fading channel conditions could also be considered, which could be modeled by for example the Nakagami-m distribution [91]. It is of interest to know that this fading distribution has gained much attention lately owing to the fact that the Nakagami-m distribution gives a better model for land- mobile and indoor mobile multi-path propagation environments as well as scintillating ionospheric radio links [92]. Furthermore, the ideas presented in Chapter 4 and Chapter 5 could be combined by considering the use of multi- target tracking methods such as the probability hypothesis density (PHD) filter [93] to simultaneously track the activities of multiple PUs under SUs’ mobility scenarios.
Another possible research problem is how to ensure cooperation among SUs. In this work and almost all the related works on cooperative spectrum sensing, it is assumed that the SUs are trustworthy and well-behaved, which may not always be the case in reality. There may exist some dishonest users, even malicious ones in the system, corrupting or disrupting the normal operation of the CRN [94], [95]. Consequently, the system’s performance can be compromised. Thus, this security issue needs to be considered for emerging CRNs and a possible way of addressing this is to use mechanism design [96] which is an important concept in game theory.
Finally, the solutions presented in this thesis are for interweave approach to dynamic spectrum access, the other two methods, namely; underlay and overlay approaches briefly described at the outset could also be considered.