CYCLOSTATIONARY FEATURES OF PAL TV AND WIRELESS MICROPHONE FOR COGNITIVE RADIO APPLICATIONS

Frequency spectrum being a scarce resource in communication system design, spectrum sharing seems to be the solution to an optimal utilization of frequency spectrum. The traditional fixed frequency allocation is not suitable for futuristic networks that demand more and more spectrum for new wireless services. Cognitive radio is a new emerging technology based on spectrum sharing concept. Spectrum sensing is a vital task in this emerging technology by which it is able to scan the frequency spectrum to identify the unused spectrum bands and utilize them. In this thesis, we discuss spectrum sensing in the context of IEEE 802.22 Wireless Regional Area Network (WRAN). In order to do so, we develop the co-existence scenario with three cases according to geographical positions of primary services and secondary service. In WRAN application, the SUs utilize the unused channel in TV spectrum, which means that the primary users are TV service and other FCC part 74 low power licensed devices. We focus on special case of Analog TV-PAL service and wireless microphone service as part 74 devices. Before discussing the spectrum sensing technique, we propose architecture for sensing receiver. The concept of noise uncertainty is also introduced in this context. The cyclostationarity theory is introduced and we explain the motivation behind using the theory for spectrum sensing and the reason that makes the cyclostationary features detector a powerful detection technique in cognitive radio. We obtain the cyclostationary features of these primary signals using spectral correlation function. Based on these features, we develop two algorithms for spectrum sensing and their performances are evaluated in comparison with energy detector which is considered as the standard simple detector. Given that the cyclostationary features are unique for a particular signal; these features can be used for signals classification. In our case, we use those features to decide if the licensed channel is used by TV service or wireless microphone service. This provides additional information for spectrum management and power control. Implementation issue is very important in cognitive radio generally and spectrum sensing specially, hence we discuss the implementation of cyclostationary features detector and compare its complexity with that of energy detector

CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS

The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing. The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive. In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable

Efficient Hardware Architecture for Cyclostationary Detector

Cognitive radio is one of the modern techniques which is evolved for utilising the unused spread spectrum effectively in wireless communication. In cognitive radio system the foremost concept is sensing the holes (spaces) in the frequency spectrum allotted and it facilitates a way that how effectively and efficiently the bandwidth is used by finding the spectrum holes in a designated spectrum. There are various methods available for sensing the spectrum and one such a sensing method is cyclostationary detection.  The method of cyclostationary feature mainly focuses on detecting whether the primary user is present or absent. The threshold of a signal is calculated by cyclic cross-periodogram matrix of the corresponding signal to determine the presence of signal or noise. The difficulty in evaluating the targeted threshold is evaded by training an artificial neural network by extracted cyclostationary feature vectors which are obtained by FFT accumulation method. This paper proposes a hardware architecture for cyclostationary detection

Spectrum Sensing Techniqes in Cognitive Radio: Cyclostationary Method

Cognitive Radios promise to be a major shift in wireless communications based on developing a novel approach which attempt to reduce spectrum scarcity that growing up in the past and waited to increase in the future. Since formulating stages for increasing interest in wireless application proves to be extremely challenging, it is growing rapidly. Initially this growth leads to huge demand for the radio spectrum. The novelty of this approach needs to optimize the spectrum utilization and find the efficient way for sharing the radio frequencies through spectrum sensing process. Spectrum sensing is one of the most significant tasks that allow cognitive radio functionality to implement and one of the most challenging tasks. A main challenge in sensing process arises from the fact that, detecting signals with a very low SNR in back ground of noise or severely masked by interference in specific time based on high reliability. This thesis describes the fundamental cognitive radio system aspect based on design and implementation by connecting between the theoretical and practical issue. Efficient method for sensing and detecting are studied and discussed through two fast methods of computing the spectral correlation density function, the FFT Accumulation Method and the Strip Spectral Correlation Algorithm. Several simulations have been performed to show the ability and performance of studied algorithms.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Efficient Hardware Architecture for Cyclostationary Detector

Cognitive radio is one of the modern techniques which is evolved for utilising the unused spread spectrum effectively in wireless communication. In cognitive radio system the foremost concept is sensing the holes (spaces) in the frequency spectrum allotted and it facilitates a way that how effectively and efficiently the bandwidth is used by finding the spectrum holes in a designated spectrum. There are various methods available for sensing the spectrum and one such a sensing method is cyclostationary detection. The method of cyclostationary feature mainly focuses on detecting whether the primary user is present or absent. The threshold of a signal is calculated by cyclic cross-periodogram matrix of the corresponding signal to determine the presence of signal or noise. The difficulty in evaluating the targeted threshold is evaded by training an artificial neural network by extracted cyclostationary feature vectors which are obtained by FFT accumulation method. This paper proposes a hardware architecture for cyclostationary detection

On the feasibility of the communications in the TVWS spectrum analysis and coexistence issue

In the last decade, the enormous growth in the wireless industry has come from using only a small part of the wireless spectrum, nominally less than 10% under 3 GHz. Nowadays, the vast majority of the available spectral resources have already been licensed. Measurements made by the Federal Communication Commission (FCC) have shown that a great part of the spectrum, although allocated, is virtually unused. For all this reasons, in the last years, several countries have already (USA) or are in the process (EU, China, Japan, South Korea) of switching off analog TV broadcasting in favor of Digital Terrestrial Television (DTT) broadcasting systems and digital switchover plans have driven a thorough review of TV spectrum exploitation. The resulting unused channels within this band are called “TV white spaces” (TVWS). Even after the redistribution of the digital TV channels, the problem of an efficient utilization of the allocated frequencies is still far from being solved. For example, there are still large territorial areas on which, although allocated, the TV channels result unused, due to coverage problems. New spectrum allocation approaches such as the dynamic spectrum access method have been studied. This new concept implies that the radio terminals have the capacity to monitor their own radio environment and consequently adapt to the transmission conditions on whatever frequency band are available (adaptive radio). If this concept is supplemented with the capacity of analyzing the surrounding radio environment in search of white spaces, the term adaptive radio is extended to Cognitive Radio (CR). The spectrum management rule of CR is that all new users for the spectrum are secondary (cognitive) users (SU) and requires that they must detect and avoid the primary (licensed) users (PU) in terms of used frequencies, transmission power and modulation scheme. In the TV bands specifically, the presence of PUs (e.g. TV broadcasters) can be revealed both performing a spectrum sensing operation and considering the information provided by the external databases called “geo-location databases” (GL-DB). The database provides, for a certain location, the list of the free TV channels and the allowable maximum effective isotropic radiated power (EIRP) for transmitting without harmful interference to incumbent users. Decision thresholds are still a critical parameter for protecting services in a scenario where cognitive devices would be operating. There are cases where the approach based on GL Spectrum Occupancy DB might not be available, either because the database does not exist for that area (for example in non densely populated areas) or in the case that access to the database is not possible (deep indoor operation, low populated areas etc.). Several studies have suggested that radio noise has increased significantly over the last decades and consequently the assumptions about decision thresholds and interference protection ratios might be outdated. The Hidden Node Margin (HNM) is a parameter that quantifies the difference between the potential interfered signal values at the location where it is measured or estimated by the cognitive device, and the actual value at the location where the receiving antenna for this signal is located. HNM is a key parameter to define the protection requirements that cognitive devices must comply in order not to create any harmful interference to broadcast receiving systems. In this context, this thesis goes in a precise direction, with four main topics related to the feasibility of communication cognitive systems operating in the TVWS, considering coexistence as the main operational issue. The first topic studies new spectrum sensing approaches in order to improve the more critical functionality of CRs. In the second topic an unlicensed indoor short-range distribution system for the wireless retransmission in the DTT band of High definition TV (HDTV) contents with immediate implementations as home entertainment systems has been carried out. The third topic of this thesis is about a particular database developed in order to provide information to easily calculate HNM values and associated statistics, TV Channel Occupancy and Man Made Noise Upper Limits. The empirical data for this work has been recorded in different locations of Spain and Italy during 2011 and 2012 thanks to the partnership between the Department of Electrical and Electronic Engineering (D.I.E.E.) of the University of Cagliari and the Department of Electronics and Telecommunications of the University of Bilbao (UPV/EHU). Finally in the last topic we focus on the IEEE 802.22 WRAN standard evaluating, thanks to extended measurements, the performance of an 802.22 system operating into the same coverage range of a DTT receiver

CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS

The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing. The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive. In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable