Mohsen Riahi Manesh

A Bayesian approach to estimate and model SINR in wireless networks

Summary In wireless communications, the signal-to-interference-plus-noise ratio (SINR) is important in spectrum management and link scheduling. In cognitive radio and ad hoc networks, where the spectrum is shared between nodes, the SINR is required to measure the outage probability and the level of accumulated interference on a specific node from other nodes sharing the same band. Several techniques have been proposed to estimate and statistically model the SINR. However, most of these techniques do not account for uncertainty in factors such as the number of nodes and their locations, the distance between nodes, the transmission powers, and the frequencies. In addition, these methods are not able to learn from and adapt to the changes of the network. Therefore, there is a need for models able to dynamically deal with the uncertainty affecting the SINR and provide a modular framework for its estimation. In this article, a Bayesian model is proposed to probabilistically model the SINR and describe how variables affect its probability distribution. Simulation results confirm the validity and robustness of the proposed method.

Performance evaluation of spectrum sensing techniques for cognitive radio systems

With the increase in utilization of portable devices and ever-growing demand for greater data rates in wireless transmission, an increasing demand for spectrum channels was observed since last decade. Currently, radio spectrum channels are assigned for quite long time periods to licensed subscribers who may not constantly use these bands, which leads to an inefficient use of the radio spectrum. The concept of cognitive radio technology was suggested to deal with the problem of spectrum scarcity caused by the underutilization of the radio spectrum. A cognitive radio system is able to sense its functional and geographical surrounding and adjust its operation. Therefore, cognitive radio operation has to be considered with intelligent sensing and smart decision-making methods. The aim of the paper is to examine and evaluate the three most fundamental spectrum sensing techniques i.e. energy detection-based, autocorrelation-based, and matched filter-based sensing. Simulation platforms were developed for each of the approaches using GNU radio and Python language.

An optimized SNR estimation technique using particle swarm optimization algorithm

Estimation of the signal-to-noise ratio (SNR) has become an integral part of wireless communication systems, particularly in cognitive radio systems. The knowledge of the SNR at any time is essential because it has a significant influence on the performance of the system. Approximating this parameter can help better calculate the occupancy level of different channels of the radio spectrum which is an essential part in decision making process of cognitive radio systems. Recently, a novel SNR estimation approach based on the eigenvalues of the covariance matrix of the received samples was proposed in the literature. This method is highly dependent on a number of parameters including number of input samples, number of eigenvalues, and Marchenko-Pastur distribution size. In the process of SNR estimation, these parameters are chosen based on some factors such as available hardware, channel condition, and the application for which SNR is estimated. In this paper, we analyze the effect of each of the mentioned parameters on the SNR estimation method and show that they need to be optimized. We propose the use of particle swarm optimization (PSO) algorithm in the eigenvalue-based SNR estimation technique to optimize these parameters. The results of the proposed method are compared with those of the original SNR estimation method. The results validate the improvement achieved by our technique compared to the original technique.

Security Threats and Countermeasures of MAC Layer in Cognitive Radio Networks

Abstract Cognitive radio is a promising technology proposed to solve the scarcity of the radio spectrum by opportunistically allocating the idle channels of the licensed users to unlicensed ones. The effectiveness of the cognitive radio is highly dependent on fair and efficient management of the access to the unused portion of the frequency channels, which is performed by the media access control layer. Therefore, any malicious activities disrupting operation of this layer result in significant performance degradation of the cognitive radio networks. As a result, it is necessary to understand functions of the media access control layer of the cognitive radio and analyze the impacts of possible attacks these functions might encounter. This paper provides an overview of different attacks that target the media access control layer of cognitive radio and analyzes them based on this layers functions. In addition, the paper describes and compares recent defense strategies related to each attack.

Real-time spectrum occupancy monitoring using a probabilistic model

Abstract The scarcity of the radio spectrum has motivated a search for more optimal and efficient spectrum management methods. One of these methods is spectrum sharing, which multiplies the number of devices that can use this resource without causing harmful interference to licensees. Spectrum sharing requires spectrum scanning to gain awareness of the spectrum occupancy patterns and decide how to allocate access to this resource. This process has been traditionally done by sensing the channel to determine its state, occupied or empty, and then using frequentist inference to estimate the channel occupancy. However, frequentist inference does not handle uncertainty and does not take into account the probabilities of false alarm and detection when estimating the channel occupancy rate. On the other hand, Bayesian inference can handle uncertainty by considering the impact of these parameters on spectrum sensing results. Additionally, it is possible to include previous knowledge into the construction of Bayesian models to learn and make decision under uncertainty. In this paper, we propose a spectrum scanning method, Bayesian inference, to estimate the channel occupancy rate. One advantage of this method is that it takes into consideration the probabilities of false alarm and detection of the spectrum sensor. This feature makes the estimation of the channel occupancy rate more accurate.

Denoising signals in cognitive radio systems using an evolutionary algorithm based adaptive filter

Noise originating from several sources in a RF environment degrades the performance of communication systems. In wideband systems, such as cognitive radios, noise at the receiver can also originate from non-linearity present in the RF front end, time-varying thermal noise within the receiver radio system, and noise from adjacent network nodes. Several denoising techniques have been proposed for cognitive radios, some of which are applied during spectrum sensing and others to received noisy signals. Examples of these techniques include least mean square (LMS) and its variants. However, these algorithms have low performance with non-linear signals and cannot locate the global optimum solution for noise cancellation. Therefore, application of global search optimization techniques, such as evolutionary algorithms, is considered for noise cancellation. In this paper, particle swarm optimization (PSO) and LMS algorithms are implemented and their performances compared. Extensive simulations were performed where Gaussian and nonlinear random noise were added to the transmitted signals. The performance comparison was done using two metrics: bit error rate and mean square error. The results show that PSO outperforms LMS under both Gaussian and non-linear random noise.

Analysis of vulnerabilities, attacks, countermeasures and overall risk of the Automatic Dependent Surveillance-Broadcast (ADS-B) system

Abstract The U.S. Federal Aviation Administration has mandated the use of the Automatic Dependent Surveillance-Broadcast (ADS-B) system by January 2020 as a key component of the NextGen Project, which is intended to upgrade the air traffic control infrastructure and operations. The ADS-B system seeks to replace legacy approaches such as primary and secondary radars by employing global satellite navigation systems to generate precise air pictures for air traffic management. The security of ADS-B is a major concern because the system broadcasts detailed information about aircraft, their positions, velocities and other data over unencrypted data links, making it easy to launch eavesdropping, jamming and message modification attacks on aircraft in flight. This paper discusses ADS-B vulnerabilities and attacks that leverage the ADS-B protocol stack. The paper also presents the security requirements, state-of-the-art attack detection techniques and countermeasures, along with an overall risk analysis of the ADS-B system.

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.

A Bayesian model of the aggregate interference power in cognitive radio networks

Interference is one of the critical factors that affects the performance of cognitive radio networks. In these networks, secondary users are allowed to use the primary user channel with the condition that they cause no interference to it. Interference power received at the primary user is impacted by a number of parameters, including nodes transmit powers, distance between the primary user and the other nodes, path loss, and shadowing. A number of techniques have been proposed to model the interference power. However, these techniques do not consider the uncertainty associated with these parameters. Therefore, a model that deals with the uncertainty affecting the aggregate interference power is needed. In this paper, a Bayesian model is proposed to probabilistically describe how a number of factors affect the aggregate power of interference.