Abstract: The monitoring of spectrum usage and signal identification, using cognitive radio, is done to identify frequencies that are vacant for reuse. It has been established that ‘internet of things’ device uses secondary frequency which is free, thereby facing the challenge of interference from other users, where some primary frequencies are not being utilised. The design was done by analysing a specific frequency spectrum, checking if all the frequency stations that range from 87.5-108 MHz are presently being used in Benin City, Edo State, Nigeria. From the results, it was noticed that by using Software Defined Radio/Simulink, we were able to identify vacant frequencies in the range of frequency under consideration. Also, we were able to use the significance of energy detection threshold to reuse this vacant frequency spectrum, when the cognitive radio displays a zero output (that is decision H0), meaning that the channel is unoccupied. Hence, the analysis was able to find the spectrum hole and identify how it can be reused.
Abstract: Spectrum underutilization has made cognitive
radio a promising technology both for current and future
telecommunications. This is due to the ability to exploit the unused
spectrum in the bands dedicated to other wireless communication
systems, and thus, increase their occupancy. The essential function,
which allows the cognitive radio device to perceive the occupancy
of the spectrum, is spectrum sensing. In this paper, the performance
of modern adaptations of the four most widely used spectrum
sensing techniques namely, energy detection (ED), cyclostationary
feature detection (CSFD), matched filter (MF) and eigenvalues-based
detection (EBD) is compared. The implementation has been
accomplished through the PlutoSDR hardware platform and the
GNU Radio software package in very low Signal-to-Noise Ratio
(SNR) conditions. The optimal detection performance of the
examined methods in a realistic implementation-oriented model is
found for the common relevant parameters (number of observed
samples, sensing time and required probability of false alarm).
Abstract: Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.
Abstract: As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.
Abstract: The IEEE 802.22 working group aims to drive the
Digital Video Broadcasting-Terrestrial (DVB-T) bands for data
communication to the rural area without interfering the TV broadcast.
In this paper, we arrive at a closed-form expression for average
detection probability of Fusion center (FC) with multiple antenna
over the κ − μ fading channel model. We consider a centralized
cooperative multiple antenna network for reporting. The DVB-T
samples forwarded by the secondary user (SU) were combined using
Maximum ratio combiner at FC, an energy detection is performed
to make the decision. The fading effects of the channel degrades
the detection probability of the FC, a generalized independent and
identically distributed (IID) κ − μ and an additive white Gaussian
noise (AWGN) channel is considered for reporting and sensing
respectively. The proposed system performance is verified through
simulation results.
Abstract: Spectrum sensing is the main feature of cognitive
radio technology. Spectrum sensing gives an idea of detecting the
presence of the primary users in a licensed spectrum. In this paper we
compare the theoretical results of detection probability of different
fading environments like Rayleigh, Rician, Nakagami-m fading
channels with the simulation results using energy detection based
spectrum sensing. The numerical results are plotted as Pf Vs Pd for
different SNR values, fading parameters. It is observed that
Nakagami fading channel performance is better than other fading
channels by using energy detection in spectrum sensing. A MATLAB
simulation test bench has been implemented to know the performance
of energy detection in different fading channel environment.
Abstract: Cognitive Radio is a turning out technology that
empowers viable usage of the spectrum. Energy Detector-based
Sensing is the most broadly utilized spectrum sensing strategy.
Besides, it's a lot of generic as receivers doesn't would like any
information on the primary user's signals, channel data, of even the
sort of modulation. This paper puts forth the execution of energy
detection sensing for AM (Amplitude Modulated) signal at 710 KHz,
FM (Frequency Modulated) signal at 103.45 MHz (local station
frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz.
The OFDM/OFDMA based WiMAX physical layer with
convolutional channel coding is actualized utilizing USRP N210
(Universal Software Radio Peripheral) and GNU Radio based
Software Defined Radio (SDR). Test outcomes demonstrated the
BER (Bit Error Rate) augmentation with channel noise and BER
execution is dissected for different Eb/N0 (the energy per bit to noise
power spectral density ratio) values.
Abstract: The need in cognitive radio system for a simple, fast, and independent technique to sense the spectrum occupancy has led to the energy detection approach. Energy detector is known by its dependency on noise variation in the system which is one of its major drawbacks. In this paper, we are aiming to improve its performance by utilizing a weighted collaborative spectrum sensing, it is similar to the collaborative spectrum sensing methods introduced previously in the literature. These weighting methods give more improvement for collaborative spectrum sensing as compared to no weighting case. There is two method proposed in this paper: the first one depends on the channel status between each sensor and the primary user while the second depends on the value of the energy measured in each sensor.
Abstract: In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.