Hector Reyes

A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks

Display Omitted Gaussian noise samples are delta correlated; ACF(t)=ź(t).We can do spectrum sensing with the Euclidean distance between ACF(t) and a reference line.The Euclidean distance method performs better than the ED and ACF(1) methods. Spectrum sensing is an important aspect of cognitive radios. This paper describes a method for spectrum sensing based on the autocorrelation of the received samples. The proposed method was evaluated by means of experiments wherein the probabilities of detection and false alarm at different signal-to-noise ratios (SNRs) were observed. The platform used for the experiments was a set of Universal Software Radio Peripheralź (USRPź) devices acting as radio frequency front ends in combination with GNU Radio software. Since the signal processing was performed in the software domain, Gaussian noise of different levels was emulated by changing the standard deviation of a Python random number generator. In addition, the output power of a signal generator was varied to obtain different levels of SNR. A metric called the Euclidean distance was derived to analyze the autocorrelation of the samples received by the USRPź device in order to decide between two possible situations: only noise present or signal plus noise present. The proposed method was compared with two methods: one based on the value of the autocorrelation at the first lag and another one based on the power of the signal, known as energy detection spectrum sensing technique.

Spectrum occupancy measurement: An autocorrelation based scanning technique using USRP

This paper presents a technique for scanning and evaluating the radio spectrum use. This technique determines the average occupancy of a channel over a specific duration. The technique was implemented using Software Defined Radio units and GNU Radio software. The survey was conducted in Grand Forks, North Dakota, over a frequency range of 824 MHz to 5.8 GHz. The results of this technique were compared to those of two existing techniques, energy detection and autocorrelation, that were also implemented. The results show that the proposed technique is more efficient at scanning the radio spectrum than the other two techniques.

A Bayesian network model of the bit error rate for cognitive radio networks

In addition to serve as platforms for dynamic spectrum access, cognitive radios can also serve as a method for improving the performance of wireless communication systems by smartly adjusting their operating parameters according to the environment and requirements. The uncertainty always present in the environment makes the practical implementation of the latter application difficult. In this paper, we propose a probabilistic graphical model, Bayesian network that captures the causal relationships among the variables bit energy to noise spectral density ratio (EbN0), carrier to interference ratio (C/I), modulation scheme (MOD), Doppler phase shift (Dop_Phi), and bit error rate (BER). BER indicates how the communication link is performing. The goal of our proposed Bayesian network is to use the BER as evidence in order to infer the behavior of the other variables, so the cognitive radio can learn how the conditions of the environment are, and based on that knowledge make better informed decisions. This model along with the method used to build it are described in this paper.

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.

Channel quality estimation metrics in cognitive radio networks: a survey

The rapid growth of wireless communication systems around the world has led to a significant demand for more radio spectrum resources. Radio spectrum regulators are facing several challenges due to spectrum scarcity caused not only by increased demand but also by inefficient management of this resource. Currently, the spectrum is statically allocated to licensed users even though some of them use this resource intermittently. This has resulted in underutilisation of the spectrum and therefore, spectrum scarcity for new users. Cognitive radio aims at solving this problem through dynamic spectrum access, which uses spectrum holes opportunistically. A cognitive radio can select the best channel available at a particular location and time. To accomplish that goal, the system requires some criteria, or channel quality estimation metrics, to rank the channels that it potentially could use. In this study, the authors present a survey on several channel quality estimation metrics such as bit error rate, signal-to-interference-plus-noise ratio, outage probability, channel quality estimation index, spectrum sensing accuracy of secondary user, and the idle state duration expectation.

Outage probability estimation technique based on a Bayesian model for cognitive radio networks

Cognitive radio is a new technology that aims to solve the scarcity and underutilization of the radio spectrum. It also aims to achieve its goals with a high quality of service. Hence, channel quality estimation metrics are used to help the cognitive radio to readjust its parameters and enhance its quality of service. One of these metrics is the probability of outage. This metric depends on either the level of signal to interference plus noise ratio (SINR), or the channel capacity and data rate. However, uncertainty affects these two variables, which in turns affects the probability of outage. Therefore, a method that deals with uncertainty is necessary. In this paper, we propose a model based on a Bayesian network. This method qualitatively and quantitatively relates the variables affecting SINR and outage probability by a conditional probabilistic model. The results of the proposed Bayesian model show the effectiveness in handling uncertainty.

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.

A cognitive radio system for improving the reliability and security of UAS/UAV networks

This paper describes a system based on cognitive radio technology to improve the reliability and security of wireless communications of unmanned aerial systems and vehicles (UAS/UAV) networks. UAS/UAV networks can experience problems with connectivity and thus with data reception and delivery. Since UAS/UAV are mobile, their connectivity is dynamic; thus, link status changes are more frequent than for traditional networks. Specifically, link losses due to jamming, interference, fading, and multipath are common problems. Another factor is the way the radio spectrum is used at each specific location. The availability of specific spectrum frequency bands can vary from one location to another, thus making it crucial for aircraft to be frequency agile to maintain connectivity.

Spectrum Channel Characterization Using Delay and Doppler Spread Parameters

Abstract—This paper describes a non-blind technique, channel sounder, to characterize a wireless channel. This technique is based on the transmission of a pseudo random sequence through the channel, the calculation of its autocorrelation to estimate the channel impulse response, and from it the calculation of the Delay and Doppler spread parameters. This channel sounder was implemented using GNU radio software and software defined radio units (USRP N200). Experiments were performed at different scenarios: an anechoic chamber, a parking lot, and a street. The results show that in absence of interference or multipath the Delay and Doppler Spread parameters were zero; however they differed from zero with interference, attenuation, and multipath. These results show that the technique could be used to characterize and qualify available spectrum channels, since the measurements can reflect not only multipath but also other factors such as interference and attenuation.

A Bayesian inference method for estimating the channel occupancy

The proliferation of mobile devices has led to an increasing demand for radio spectrum resources. Currently, the spectrum allocation is static, which has resulted in underutilization of this resource. This situation has motivated the search for solutions to address the spectrum scarcity problem. The channel occupancy rate is a piece of information that can assist the decision making process regarding reallocation of spectral resources. This information can be useful for DSA (Dynamic Spectrum Access) systems, spectrum decision, and spectrum regulators. In most of the spectrum usage surveys, frequentist inference techniques are used to scan the spectrum and estimate the occupancy rate of particular frequency bands. This approach has several limitations that are addressed in this work through Bayesian inference in order to build a spectrum decision framework able to deal with uncertainty. This paper presents a work in progress, whose goal is to build a probabilistic model to assist the spectrum decision process in cognitive radios.