A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems

Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelâs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels. An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm

Frequency shift filtering for cyclostationary signals.

The frequency-shift (FRESH) filter is a structure which exploits the spectral correlation of cyclostationary signals for removing interference and noise from a wanted signal. As most digital communication signals are cyclostationary, FRESH filtering offers certain advantages for interference rejection in a communications receiver. This thesis explores the operation and application of FRESH filters in practical interference scenarios. The theoretical background to cyclostationarity is clarified with graphical interpretations of what cyclostationarity is, and how a FRESH filter exploits it to remove interference. The effects of implementation in a sampled system are investigated, in filters which use baud rate related cyclostationarity, leading to efficiency improvements. The effects of varying the wanted signal pulse shape to enhance the cyclostationarity available to the FRESH filter are also investigated. A consistent approach to the interpretation of the FRESH filter's operation is used throughout, while evaluating the performance in a wide range of realistic channel conditions. VLF radio communication is proposed as one area where interference conditions are particularly suitable for the use of FRESH filtering. In cases of severe adjacent channel interference it is found that a FRESH filter can almost completely remove the interferer. The effects of its use with an impulse rejection technique are also investigated. Finally, blind adaptation of FRESH filters through exploitation of carrier related cyclostationarity is investigated. It is found that one existing method loses the advantage of FRESH filtering over time invariant linear filtering. An improvement is proposed to the latter which restores its performance to that of a trained FRESH filter, and also reveals that carrier related cyclostationarity can be exploited, in some cases, by a simpler method. J

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown

Low Probability of Intercept Waveforms via Intersymbol Dither Performance under Multipath Conditions

This thesis examines the effects of multipath interference on Low Probability of Intercept (LPI) waveforms generated using intersymbol dither. LPI waveforms are designed to be difficult for non-cooperative receivers to detect and manipulate, and have many uses in secure communications applications. In prior research, such a waveform was designed using a dither algorithm to vary the time between the transmission of data symbols in a communication system. This work showed that such a method can be used to frustrate attempts to use non-cooperative receiver algorithms to recover the data. This thesis expands on prior work by examining the effects of multipath interference on cooperative and non-cooperative receiver performance to assess the above method’s effectiveness using a more realistic model of the physical transmission channel. Both two and four ray multipath interference channel models were randomly generated using typical multipath power profiles found in existing literature. Different combinations of maximum allowable symbol delay, pulse shapes and multipath channels were used to examine the bit error rate performance of 1) a Minimum Mean Squared Error (MMSE) cooperative equalizer structure with prior knowledge of the dither pattern and 2) a Constant Modulus Algorithm (CMA) non-cooperative equalizer. Cooperative MMSE equalization resulted in approximately 6-8 dB BER performance improvement in Eb/No over non-cooperative equalization, and for a full range symbol timing dither non-cooperative equalization yields a theoretical BER limit of Pb=10−1. For 50 randomly generated multipath channels, six of the four ray channels and 15 of the two ray channels exhibited extremely poor equalization results, indicating a level of algorithm sensitivity to multipath conditions

Oversampling PCM techniques and optimum noise shapers for quantizing a class of nonbandlimited signals

We consider the efficient quantization of a class of nonbandlimited signals, namely, the class of discrete-time signals that can be recovered from their decimated version. The signals are modeled as the output of a single FIR interpolation filter (single band model) or, more generally, as the sum of the outputs of L FIR interpolation filters (multiband model). These nonbandlimited signals are oversampled, and it is therefore reasonable to expect that we can reap the same benefits of well-known efficient A/D techniques that apply only to bandlimited signals. We first show that we can obtain a great reduction in the quantization noise variance due to the oversampled nature of the signals. We can achieve a substantial decrease in bit rate by appropriately decimating the signals and then quantizing them. To further increase the effective quantizer resolution, noise shaping is introduced by optimizing prefilters and postfilters around the quantizer. We start with a scalar time-invariant quantizer and study two important cases of linear time invariant (LTI) filters, namely, the case where the postfilter is the inverse of the prefilter and the more general case where the postfilter is independent from the prefilter. Closed form expressions for the optimum filters and average minimum mean square error are derived in each case for both the single band and multiband models. The class of noise shaping filters and quantizers is then enlarged to include linear periodically time varying (LPTV)M filters and periodically time-varying quantizers of period M. We study two special cases in great detail