Multi-antenna energy detector under unknown primary user traffic

In cognitive radio (CR) networks, the knowledge of primary user (PU) traffic plays a crucial role in designing the sensing slot duration and synchronization with PU traffic. However, the secondary user (SU) sensing unit usually does not have the knowledge of the exact time slot structure in the primary network. Moreover, it is also possible that the communication among PUs are not based on synchronous schemes at all. In this paper, the effect of unknown primary user (PU) traffic on the performance of multi-antenna spectrum sensing is evaluated under a flat fading channel. In contrast to the commonly used continuous time Markov model of the existing literature, a realistic and simple PU traffic model is proposed which is based only on the discrete time distribution of PU free and busy periods. Furthermore, in order to assess the effect of PU traffic on the detection performance, analytical expressions for the probability density functions of the decision statistic are derived considering Energy Detection (ED) test as spectrum sensing method. It is shown that the time varying PU traffic severely affects the spectrum sensing performance. Most importantly, our results show that the performance gain due to multiple antennas in the sensing unit is significantly reduced by the effect of PU traffic when the mean lengths of free and busy periods are of the same order of magnitude of the sensing slot

Data Driven Quickest Detection in Networks and Its Applications in Spectrum Sensing

Cognitive radio is one of the enabling technologies considered for the next generation communication systems for many mission-critical applications. In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. In this dissertation, the study is focused on data driven quickest detection applied to energy detection based spectrum sensing. Firstly, a framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing where the primary user (PU) activities are heterogeneous in the space and dynamic in the time. The performance of the proposed scheme is analyzed mathematically. Using numerical simulations, detection performance measured by false alarm rate and average detection delay is obtained for different setups. Numerical simulations have demonstrated the validity of the proposed technique.Secondly, we propose a universal quickest change detection scheme based on density ratio estimation for spectrum sensing by detecting the sudden change of spectrum (e.g., the emergence of PU), where neither the pre-change nor post-change distribution (even the distribution forms) is known to secondary users (SUs), thus achieving robustness to complex spectrum environment, where SUs have no prior information about the measurement distributions. The validity of the proposed schemes has been shown by numerical simulations.Finally, we extend the detection of change in spectrum to millimeter-wave environment. As millimeter-wave is becoming part of the physical layer standard in the next-generation cellular network, it also brings about many questions and challenges. Not all the existing theories and methods for traditional wireless communication can apply directly to millimeter-wave communication because of the adoption of directional antenna and the high frequency band used. We propose a data-driven spectrum change sensing technique based on mean recurrence time to efficiently detect the PU activities which is tolerant of small fluctuations. The proposed spectrum sensing works well without a priori knowledge of the sensed signal, and doesn\u27t take assumption of independent and identically distributed random variables. It can also serve as a general framework for detection in other areas. The experimental results validate the proposed detection framework

Cognitive Radio Networks: Realistic or Not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important interactions of the research with commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. This is necessary to detect the existence of spectrum holes without which no other procedure can be fulfilled. However, simply sensing (cooperatively or not) the energy received from a primary transmitter cannot enable correct dynamic spectrum access. For example, the low strength of a primary transmitter's signal does not assure that there will be no interference to a nearby primary receiver. In addition, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing elegant and clever solutions to the DSA problem no robust, implementable scheme has emerged. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments.Comment: Work in progres