Noise Cancellation in Cognitive Radio Systems: A Performance Comparison of Evolutionary Algorithms

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms

Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel

In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods

Performance Analysis Of Machine Learning Algorithms In Detecting Cyber Attacks Targeting Automatic Dependent Surveillance-Broadcast (ADS-B) Systems

In order to support air traffic control services, the U.S. Federal Aviation Administration (FAA) has mandated the use of automatic dependent surveillance-broadcast (ADS-B) in aircraft in certain classes of airspace by January 2020. This system aims to replace the legacy approaches, such as primary and secondary radars, by employing global navigation satellite systems for its operation to generate a precise air picture for air traffic management. The major downside of this system is its security as it broadcasts information of an aircraft such as its position and velocity over an unencrypted datalink. This lack of security makes the ADS-B vulnerable to cybersecurity attacks which can compromise the safety and security of airspace systems. Therefore, it is important to detect these attacks. This dissertation aims at developing methods able to efficiently detect cyber attacks that target ADS-B systems. The proposed methods are based on supervised machine learning models. Therefore, these methods require to be trained using reliable training datasets. In this dissertation, real data as well as simulated one are used to build training datasets and validate the efficiency of the machine learning methods. From this data, several features are extracted depending on the attack type. Results confirm that these methods are reliable, accurate, and independent with high detection and low false alarm probabilities. In addition, unlike existing solutions, these techniques do not rely on information from other surveillance methods and are compatible with current ADS-B systems

Improved soft fusion-based cooperative spectrum sensing using particle swarm optimization

In soft-decision fusion- (SDF-) based cooperative spectrum sensing, weighting the coefficients vector is the main factor affecting the detection performance of cognitive radio networks. In this paper, the use of particle swarm optimization (PSO) algorithm as a prominent technique is proposed to optimize the weighting coefficients vector. The proposed PSO-based scheme opts for the best weighting coefficients vector, leading to improved detection performance of the system. The performance of the proposed method is analyzed and compared with genetic algorithm- (GA-) based technique as well as other conventional SDF schemes through computer simulations. Simulation results validate the robustness of the proposed method over all other SDF techniques

Efficient uplink time difference of arrival mobile device localization in cellular networks

In this work, mobile station (MS) localization in cellular systems is considered based on uplink time difference of arrival (UTDOA) of MS signal at spatially separated base stations with known locations is presented. The UTDOA estimation is conducted using normalized least mean square (NLMS) based adaptive line enhancer (ALE) followed by a cross correlation. More precisely, the use of ALE-pre-filtered cross correlation is proposed in hyperbolic localization to improve the accuracy of UTDOA estimation for reducing the uncertainty in localizing the mobile station. Computer simulation results indicate that proposed ALE-UTDOA technique can achieve superior positioning accuracy than conventional cross correlation (CC) based method with range of 67%-75%

Receiver diversity combining using evolutionary algorithms in rayleigh fading channel

In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods

Adaptive line enhancement for improved uplink time difference of arrival localization

The problem of locating mobile stations (MS) in cellular systems based on uplink time difference of arrival (UTDOA) of MS signal at spatially separated base stations with known locations is addressed in this paper. Multi path fading and channel noise are the main factors resulting in inaccurate mobile station position estimation. Therefore, use of the normalized least mean square (NLMS) algorithm based adaptive line enhancer (ALE) followed by a correlator is proposed to obtain more precise time difference of arrival (TDOA) estimation. The proposed technique applies the ALE as a pre-filter to signal cross correlation, leading to improved accuracy in TDOA estimation and consequently more precise positioning of MS. The robustness of the proposed technique is examined and analyzed through computer simulations. Simulation results indicate superior performance of the proposed ALE-UTDOA estimator over the conventional cross correlation method