On detection of OFDM signals for cognitive radio applications

As the requirement for wireless telecommunications services continues to grow, it has become increasingly important to ensure that the Radio Frequency (RF) spectrum is managed efficiently. As a result of the current spectrum allocation policy, it has been found that portions of RF spectrum belonging to licensed users are often severely underutilised, at particular times and geographical locations. Awareness of this problem has led to the development of Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) as possible solutions. In one variation of the shared-use model for DSA, it is proposed that the inefficient use of licensed spectrum could be overcome by enabling unlicensed users to opportunistically access the spectrum when the licensed user is not transmitting. In order for an unlicensed device to make decisions, it must be aware of its own RF environment and, therefore, it has been proposed that DSA could been abled using CR. One approach that has be identified to allow the CR to gain information about its operating environment is spectrum sensing. An interesting solution that has been identified for spectrum sensing is cyclostationary detection. This property refers to the inherent periodic nature of the second order statistics of many communications signals. One of the most common modulation formats in use today is Orthogonal Frequency Division Multiplexing (OFDM), which exhibits cyclostationarity due to the addition of a Cyclic Prefix (CP). This thesis examines several statistical tests for cyclostationarity in OFDM signals that may be used for spectrum sensing in DSA and CR. In particular, focus is placed on statistical tests that rely on estimation of the Cyclic Autocorrelation Function (CAF). Based on splitting the CAF into two complex component functions, several new statistical tests are introduced and are shown to lead to an improvement in detection performance when compared to the existing algorithms. The performance of each new algorithm is assessed in Additive White Gaussian Noise (AWGN), impulsive noise and when subjected to impairments such as multipath fading and Carrier Frequency Offset (CFO). Finally, each algorithm is targeted for Field Programmable Gate Array (FPGA) implementation using a Xilinx 7 series device. In order to keep resource costs to a minimum, it is suggested that the new algorithms are implemented on the FPGA using hardware sharing, and a simple mathematical re-arrangement of certain tests statistics is proposed to circumvent a costly division operation.As the requirement for wireless telecommunications services continues to grow, it has become increasingly important to ensure that the Radio Frequency (RF) spectrum is managed efficiently. As a result of the current spectrum allocation policy, it has been found that portions of RF spectrum belonging to licensed users are often severely underutilised, at particular times and geographical locations. Awareness of this problem has led to the development of Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) as possible solutions. In one variation of the shared-use model for DSA, it is proposed that the inefficient use of licensed spectrum could be overcome by enabling unlicensed users to opportunistically access the spectrum when the licensed user is not transmitting. In order for an unlicensed device to make decisions, it must be aware of its own RF environment and, therefore, it has been proposed that DSA could been abled using CR. One approach that has be identified to allow the CR to gain information about its operating environment is spectrum sensing. An interesting solution that has been identified for spectrum sensing is cyclostationary detection. This property refers to the inherent periodic nature of the second order statistics of many communications signals. One of the most common modulation formats in use today is Orthogonal Frequency Division Multiplexing (OFDM), which exhibits cyclostationarity due to the addition of a Cyclic Prefix (CP). This thesis examines several statistical tests for cyclostationarity in OFDM signals that may be used for spectrum sensing in DSA and CR. In particular, focus is placed on statistical tests that rely on estimation of the Cyclic Autocorrelation Function (CAF). Based on splitting the CAF into two complex component functions, several new statistical tests are introduced and are shown to lead to an improvement in detection performance when compared to the existing algorithms. The performance of each new algorithm is assessed in Additive White Gaussian Noise (AWGN), impulsive noise and when subjected to impairments such as multipath fading and Carrier Frequency Offset (CFO). Finally, each algorithm is targeted for Field Programmable Gate Array (FPGA) implementation using a Xilinx 7 series device. In order to keep resource costs to a minimum, it is suggested that the new algorithms are implemented on the FPGA using hardware sharing, and a simple mathematical re-arrangement of certain tests statistics is proposed to circumvent a costly division operation

Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

Multi-stage Wireless Signal Identification for Blind Interception Receiver Design

Protection of critical wireless infrastructure from malicious attacks has become increasingly important in recent years, with the widespread deployment of various wireless technologies and dramatic growth in user populations. This brings substantial technical challenges to the interception receiver design to sense and identify various wireless signals using different transmission technologies. The key requirements for the receiver design include estimation of the signal parameters/features and classification of the modulation scheme. With the proper identification results, corresponding signal interception techniques can be developed, which can be further employed to enhance the network behaviour analysis and intrusion detection. In detail, the initial stage of the blind interception receiver design is to identify the signal parameters. In the thesis, two low-complexity approaches are provided to realize the parameter estimation, which are based on iterative cyclostationary analysis and envelope spectrum estimation, respectively. With the estimated signal parameters, automatic modulation classification (AMC) is performed to automatically identify the modulation schemes of the transmitted signals. A novel approach is presented based on Gaussian Mixture Models (GMM) in Chapter 4. The approach is capable of mitigating the negative effect from multipath fading channel. To validate the proposed design, the performance is evaluated under an experimental propagation environment. The results show that the proposed design is capable of adapting blind parameter estimation, realize timing and frequency synchronization and classifying the modulation schemes with improved performances

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

New challenges in wireless and free space optical communications

AbstractThis manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced