Implementation of sensing technique for cognitive radio

In this paper, we analysis wideband spectrum sensing with distance based weight combining for Cognitive Radio (CR) systems. CR systems is implemented the spectrum of the Primary User(PU) by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of PU is BPSK signal and the wireless channel between a PU and CR systems is modeled as Gaussian channel. From the simulation results, the wideband sensing with distance based and Distance based weight Combing (DWC) methods shows higher spectrum sensing performance than single CR user spectrum sensing.

In this paper, a new spectrum sensing method based on energy detection is proposed and analyzed in a cognitive radio(CR) system. We employ a delay diversity receiver for sensing the primary user's spectrum with reasonable cost and complexity. Conventional CR with the receiver equipping multiple antennas requires additional hardware and space for installing multiple antennas in accordance with increase in the number of antennas. If the number of antennas increases, detection probability as well as hardware complexity and cost rise. Then, it is difficult to make a primary user detector practically. Therefore, we adopt a delay diversity receiver for solving problems of the conventional spectrum detector utilizing multiple antennas. We derive analytical expressions for the spectrum sensing performance of the proposed system. From the simulation results, it is demonstrated that the primary user detector with the delay diversity receiver has almost half the complexity and shows similar or improved performance as compared with that employing multiple antennas. Therefore, the proposed spectrum sensing structure can be a practical solution for enhancing the detection capacity in CR system operations. The results of this paper can be applied to legacy CR systems with simple modifications.

To improve the efficiency of bandwidth usage, the cognitive radios concept has emerged. Spectrum sensing is a pivotal function in cognitive radio systems so that the secondary users can be able to access the free spectrum holes without interfering with primary users. Energy detection is the most common and easiest spectrum sensing technique for cognitive radios. In this paper, the conventional energy detection receiver operating characteristic curve and performance metrics are simulated under additive white Gaussian noise (AWGN) environment by using Monte Carlo simulation. Also, the performance of energy detection is analyzed under different types of M-ary modulation. The analysis of performance will carry the assumption of noise uncertainty. This performance gain is compared with conventional energy detection without any noise effect. In addition, the relation between a number of samples and signal to noise ratio wall (SNR wall) is provided and simulated. The simulation tool used in the analysis is MATLAB software. Finally, the implementation of energy detection technique in the time domain and frequency domain is designed on Xilinx Spartan-3E (XC3S500E-FG320) Field Programmable Gate Array (FPGA) kit by using Verilog language and Xilinx ISE Simulator version 14.1.

Spectrum sensing is important for a cognitive radio (CR) system to protect primary users (PUs) from harmful interference. At present, most of the existing sensing schemes are proposed in Gaussian noise environments. Nevertheless, a CR system actually also suffers from non-Gaussian noise, such as man-made impulsive noise, ultrawideband interference and co-channel interference. In this article, to handle the degradation of detection performance in non-Gaussian impulsive noise environments, a robust spectrum sensing scheme named hyperbolic tangent-based energy detector (HT-ED) is proposed. The hyperbolic tangent function is applied to suppress the impulsive noise by the nonlinear behavior, but without restraining the normal noise by the linear behavior. This property enables the new method to achieve the good detection performance in both Gaussian and non-Gaussian noise environments. In addition, it is similar to an energy detector, the new detector does not need to know the characteristics of the PU's signal. The performance analysis of the proposed HT-ED scheme is also presented. The simulation results show the HT-ED is robust against impulsive noise, which is modeled as Laplace or α-stable noise. Moreover, the HT-ED provides a superior detection performance compared with the other existing relative methods in a wide range of non-Gaussian noise.

Spectrum sensing is a fundamental problem in cognitive radio communication system. In this paper, the implementations of spectrum sensing based on the Covariance Absolute Value (CAV) on the test bed of cognitive radio system are presented. This demo cognitive radio system uses the spectrum holes which are determined by sensing for communication with the spectrum from 694MHz to 806MHz and can avoid impact immediately when detecting the primary users (no mater digital television or analog television) at working spectrum. The implementation further reduces the computational complexity and makes a great significance in practical applications. A series of tests show that the detection performance of Covariance Absolute Value is not liable to be affected by the noise uncertainty in practical application and meets the need of the system primly. Furthermore, the performances of detection are also verified with different kinds of source signals.

In multi-hop relaying systems that have many relay stations (RSs), a cognitive radio (CR) technique can improve system performance greatly, due to an efficient resource sharing between the RSs. In this letter, the performance of an ARQ mechanism for CR-based multi-hop relaying systems is studied, using Markov chain and matrix signal flow graph (MSFG) techniques. Also, a priority-based resource allocation scheme is proposed to prevent performance degradation caused by the usage of unstable resources acquired by the CR technique.

Energy detector is the simplest spectrum sensing technique in cognitive radio in terms of computation complexity as it is developed assuming only channel noises for the detection of primary user signal. However in realistic sense the typical communication system comprises of various other sources of noise (like thermal noise) that significantly affect the sensing performance of the detector. The conventional energy detector is also not a coherent detector as the attributes of the primary user signal is unknown. Considering these limitations a system is proposed that can have the knowledge of the primary user signal attributes under realistic situation at the secondary user (SU) receiver thereby enhancing the accuracy of the sensing significantly. In this work a new coherent energy detector system architecture is proposed that first separates the signal components from a noisy signals (non Gaussian noise) at SU receiver, then from the separated signals it estimates the signal attributes (power of the signal, noise variance, signal-to-noise ratio) required for conventional energy detector. The work is developed as a hybrid system model utilizing blind source separation algorithm and neural network (NN) known hereafter as ‘integrated blind signal separation (BSS)-NN based energy detector’.

Cognitive radio has attracted an increasing amount of interest over the past few years as an effective method of alleviating the spectrum scarcity problem in wireless communications. One of the most promising approaches in cognitive radio is the opportunistic spectrum access, which enables unlicensed users to access licensed frequency bands that are detected to be idle. In this paper, we propose a novel cognitive radio system that exhibits improved throughput and spectrum sensing capabilities compared to the conventional opportunistic spectrum access cognitive radio systems studied so far. More specifically, we study the average achievable throughput of the proposed cognitive radio system under a single high target detection probability constraint, as well as its ergodic throughput under average transmit and interference power constraints, and propose an algorithm that acquires the optimal power allocation strategy and target detection probability, which under the imposed average interference power constraint becomes an additional optimization variable in the ergodic throughput maximization problem. Finally, we provide simulation results, in order to compare the achievable throughput of the proposed cognitive radio system with the respective throughput of the conventional cognitive radio systems and discuss the effects of the optimal power allocation and target detection probability on the ergodic throughput of the proposed cognitive radio system.

In this paper, we consider the performance of received signal strength (RSS) and time of arrival (TOA) under different areas of primary user (PU) position estimation. The areas of PU position can be separated into two cases: inner area and outer area of boundary of secondary user (SU) nodes. This boundary is created by convex hull algorithm. In simulation result, we found that RSS is appropriated to use in inner area and TOA is suitable to use in outer area. Therefore, in this paper, we propose two hybrid location estimation techniques that combine RSS and TOA in cognitive radio (CR) system. The proposed techniques include the hybrid TOA/RSS in serial and the hybrid TOA/RSS in parallel technique. Simulation results show that the proposed techniques have better performance than the methods that use only RSS or TOA. Moreover, the proposed techniques are faster than TOA about 60%.

Two points need to be considered on power loading policy of cognitive radio (CR) systems. The first is to maximize the channel capacity over the given frequency band, which is detected usable for CR systems. The second is the out-of-band interference suppression of the CR systems. In the paper based on the mathematical expression of the interference power caused by MB-OFDM based CR systems, the relationship of the symbols on the sub-carriers can be obtained. To realize the relationship the sub-carrier weighting method can be adopted. With the effect of the weighting factors, the symbols relationship can be satisfied, and simultaneously the maximum channel capacity can be achieved on certain degree. The simulation results of the relationship among the three parameters, channel capacity, interference power and total transmission power, also shows that a better tradeoff can be achieved between the channel capacity and the interference power.

A cooperative spectrum sensing technique in cognitive radio (CR) networks is proposed in this paper, which is based on the data fusion of different stochastic resonance (SR) energy detectors. Due to the noisy uncertainty in the unpredictable wireless communication channel, the detection performance of the traditional energy detector cannot be guaranteed. By introducing the SR system with different SR noise types, and with the fusion on these SR-based energy detectors, the detection probability can be improved even under low signal-to-noise ratio (SNR) circumstances. Computer simulation results validate the efficiency of the proposed novel cooperative spectrum sensing approach.

This research paper is intended to derive, simulate and analyze the capacity of cognitive radio (CR) system in fading channels. Capacity being an information theoretic perspective for a wireless system plays a role in the amount of information bits which can be transmitted. As wireless channel is subjected to multipath effects in a CR system caused by fading, capacity analysis needs to be done. Simulation carried out in terms of capacity, mean square error for channel estimation and bit error rate (BER) for the proposed system model of cognitive radio can give valuable information about performance of CR system in terms of data bits handling capacity. The amount of capacity in flat fading channels and frequency selective fading channels are simulated. The obtained results in terms of capacity can be used as reference for further analysis to be explored relating to design of cognitive radio systems for developing applications for 5G systems.

The cognitive radio technology aims to increase the spectrum utilization by accessing opportunistically the unused spectrum bands. Multicarrier communications are considered to be used in cognitive radio systems. In cognitive radio systems, the power allocation should be carried out within the cognitive radio network so that no excessive interference is caused to the primary user. In this paper, an iterative power allocation algorithm is proposed to maximize the downlink capacity of the cognitive radio system under both the total power budget and interference introduced to the primary user constraints. Moreover, two complexity reduced algorithms are presented. The problem of channel allocation in multiuser cognitive radio networks is discussed. Finally, the performance of the proposed algorithms is investigated using computer simulations.KeywordsCognitive RadioPower AllocationOrthogonal Frequency Division Multiplex SystemCognitive Radio SystemSubcarrier AllocationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter explores the novel machine learning (ML) based approaches to cognitive radio (CR) systems developed here will lead to innovative human bond communication (HBC) applications that will serve the needs of a community. An example of a community could range from spectators in a sports arena to ad hoc vehicular networks to a community in a neighbourhood sharing common interests. This chapter explores the synergy between ML and cognitive radio systems. Based on this exploration, we will formulate novel algorithms to share spectrum through dynamic spectrum leasing methodologies and adaptive policy decision making processes that seek to maximize the utilization of available scarce spectrum. This exploration will also lead to a better understanding of the traditional CR techniques and to an integrated approach that will formulate cognitive cycles at multiple levels of a system and to cross-cognitive cycle approaches that will lead to further efficiencies in spectrum utilization. The research will also facilitate a tighter coupling between CR theories and ML theories and lead to new theoretical analyses and novel algorithms that could bring about greater synergy between wireless networks and computer science.

Cognitive radio (CR) systems make efficient use of the frequency spectrum by opportunistically exploiting unoccupied or under-utilized frequency bands. However, the frequency bands used by CR systems are expected to suffer from various forms of noise and interference with non-Gaussian distributions, such as narrowband and co-channel interference caused by the primary user and other CRs, respectively, and man-made impulsive noise. To mitigate the harmful effects of non-Gaussian noise and interference, we propose a robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm metric for CR systems that employ the popular combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). For the considered CR system we provide an approximate upper bound and an asymptotic closed-form approximation for the bit error rate (BER). Our results show that the proposed L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm metric can achieve large performance gains compared to the conventional L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm metric in non-Gaussian noise and interference.