LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework

Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithm, and examines the sensing throughput tradeoff for cognitive radio (CR) networks under noise variance uncertainty. It is assumed that there are one white sub-band, and one target sub-band which is either white or non-white. Under this assumption, first we propose a novel generalized energy detector (GED) for examining the target sub-band by exploiting the noise information of the white sub-band, then, we study the tradeoff between the sensing time and achievable throughput of the CR network. To study this tradeoff, we consider the sensing time optimization for maximizing the throughput of the CR network while appropriately protecting the primary network. The sensing time is optimized by utilizing the derived detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall (i.e., robust against noise variance uncertainty) and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal sensing times with perfect and imperfect noise variances are not the same. In particular, when the frame duration is 2s, and SNR is -20dB, and each of the bandwidths of the white and target sub-bands is 6MHz, the optimal sensing times are 28.5ms and 50.6ms with perfect and imperfect noise variances, respectively.Comment: Accepted in CROWNCOM, June 2014, Oulu, Finlan

Review on Synchronization for OFDM Systems

Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation scheme. It is widely used modulation technique because it has high data rate, high spectral efficiency and robustness to multipath fading channel. One of the major drawbacks of OFDM system is synchronization. It is very sensitive to frequency synchronization errors in the form of Carrier Frequency Offset (CFO). The Carrier Frequency Offset can causes Inter Carrier Interference (ICI) and destroy the orthogonality of the OFDM system. Therefore it is necessary to perform frequency synchronization. In this paper various Carrier Frequency Offset Estimation methods are presented

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