Energy efficient cooperative spectrum sensing techniques in cognitive radio networks.

The practical cognitive radio network

The structure of proposed cooperative spectrum scheme

Energy detection test in AWGN and Rayleigh channels

The performance of hard fusion rules in AWGN channel

The performance of hard fusion schemes in AWGN channel

The performance of optimal hard fusion techniques in Rayleigh channel . 57

Comparison on energy efficiency in hard fusion schemes

A practical cognitive radio network

Proposed cooperative spectrum sensing model

Spectrum Sensing

Furthermore, spectrum sensing must be able to quickly track the temporal variations of the radio environment. Such spectrum sensing requirements place stringent demands on the hardware implementation of cognitive radios in terms of the sensing bandwidth, processing power, and radio frequency (RF) circuitry.

Spectrum Analysis

Spectrum sensing refers to the ability of a cognitive radio to sense a spectrum band to capture parameters related to the cumulative power levels and user activities of licensed primary users. Information about the detected spectrum must be adequate enough for CR to reach accurate conclusions about the radio environment [21].

Spectrum Access Decisions

Adaptability (Reconfiguration) of Cognitive Radio

Channel Uncertainty

Such uncertainties usually have implications in terms of the required channel uncertainty, noise uncertainty and detection sensitivity. Here, the CR unit causes unwanted interference to the primary user (receiver) as the primary transmitter's signal cannot be detected due to the location of the SU's units.

Noise Uncertainty

In this network, only one secondary user (SU) detects a number of primary users (PUs) as a receiver of transmitted information data from the primary user transmitter (PUTX). Under channel fading or shadowing, a low received SNR of the PU signal does not necessarily mean that the PU is located outside the secondary user's interference range, as the PU may undergo a deep fading due to shadowing obstacles [2].

Detection Sensitivity

However, it should come as no surprise that this threshold may generally depend on the characteristics of the interfering signal (eg signal waveform and intermittent interference). In this work, a group of SUs collaborate on the final decision on the presence or absence of the PU.

Non-Cooperative Detection

This makes it an unreliable technique to be used to detect the presence of the primary user, especially at low SNR conditions. The remediation decision is based on knowledge of the statistical distribution of the autocorrelation function.

Fig. 4: Classification of Spectrum Sensing Techniques.

Interference Based Detection

Rαyy(τ)e−j2πf (4) where Rαyy(τ) is the cyclic autocorrelation function obtained from the conjugate time-varying autocorrelation function of PU signals (t) periodic at time (t). When the parameter α is the cyclic frequency and equal to zero, the SCF becomes the power spectral density.

Cooperative Spectrum Sensing

This collaborative spectrum sensing scheme is not the focus of this work due to its complexity in implementation. This has indicated a paradigm shift towards achieving robust spectrum sensing based on adaptability.

Fig. 8: Decentralized Spectrum Sensing Network

Kurtosis and Skewness

The main challenge in cognitive radio networks is noise uncertainty, however HOS performs extremely well under these conditions and is the focus of this work. The noise is assumed to be stationary and follows the additive property of Gaussian white noise (AWGN) distribution.

Jarque Bera Test

The AWGN is a channel model where the only impediment to communication is noise; with a constant spectral density. Under normal distribution, the statistical values are known, so the SUs can use this statistic to determine the presence or absence of the primary user on the channel.

Authors in [8] have shown that JB test as applied in spectrum registration algorithms can achieve better detection performance than existing higher order statistics (HOS) methods as it is more robust to noise uncertainty even when examined on a small sample size. χ2) distribution test can be specified to determine the information derived from two moments. Furthermore, in a composite fading/shadowing environment, apart from multipath fading, wireless signals can undergo a shadowing process, typically modeled as a log-normal distribution, and multipath fading, which can be modeled as a Rayleigh, Rice, or Nakagami distribution. tions [23]. Therefore, some practical communication channels can be modeled as a multipath fading superimposed on log-normal shading.

Rayleigh Fading Channels

Many wireless communications networks are subject to fading caused by multipath propagation due to reflections, refractions, and scattering from buildings and other large structures. In this environment, the receiver does not average the envelope fading due to multipath, but rather responds to the current composite multipath/shadowed signal.

Nakagami-M Fading

Lognormal Fading

Soft Fusion Decision Schemes

In [41], authors studied collaborative sensing in wireless transmissions using soft decision making and the likelihood ratio test. It was shown that combinations of soft decisions in spectrum sensing provide more accurate and reliable PU detection than combinations of hard decisions.

Hard Fusion Decisions Schemes

Energy efficiency is therefore an important component for cognitive radio operations and communication over the wireless channels. An energy efficiency metric can be defined as the effective throughput per a unit of transmitted power. The term green is synonymous with energy efficiency for Wireless Sensing Network (WSN) design, since maximizing energy efficiency reduces power consumption in a WSN lifecycle and subsequently reduces air pollution.

This means that a scheme can be called energy efficient or green if it can reduce the total power of the network without significantly affecting the network throughput. The number of SUs determines the total energy consumed in the CRN, an efficient CSS network is one where the least number of SUs participate in making the final decision with high reliability and detection probability. In cooperative spectrum sensing, they are determined by the following general metrics; local probability of detection (Pd), local probability of false alarm (Pf a), local probability of false detection (Pm), global probability of detection (Qd), global probability of false alarm (Qf a) and global probability of false detection (Qm ).

The Pf a should be kept as small as possible to prevent underutilization of transport options. The performance of the spectrum sensing technique is generally affected by the Pf a as it is the most essential metric [9, 24].

The Global Probabilities in CCSS Networks

This work focuses on centralized cooperative spectrum sensing based on energy sensing and higher-order sensing statistics (HOS). To investigate the effectiveness of energy-efficient higher-order statistics (HOS) techniques over wireless cooperative spectrum sensing schemes in cognitive radio networks. Paper B entitled "Energy Efficient Higher Order Statistical Tests (HOS) in a Centralized Cooperative Spectrum Sensing Network".

Paper A: Optimal Energy Based Cooperative Spectrum Sensing

Cooperative spectrum sensing (CSS) alleviates the problem of imperfect primary user (PU) detection in cognitive radio (CR) networks by exploiting the spatial diversity of the different secondary users (SUs). Jiang, “Energy sensing-based cooperative spectrum sensing in cognitive radio networks,” IEEE Transactions on wireless communications, vol. The non-cooperative spectrum sensing (NCSS) includes energy sensing, matched filter and cyclostationary feature detection techniques [1].

Practical Cooperative Sensing Scheme

Proposed Cooperative Spectrum Scheme

Energy Detection Hypothesis Test

Additive White Gaussian Noise Statistics (AWGN)

Based on the test statistic Y(yi), the false alarm probability (Pf a,i) for the ith SU can be formulated as [18]. A.9) where Mis is the number of data samples, λd,i is the decision threshold for the i-thSU and Q(·) is the Gaussian function of Q. Similarly, the false detection probability (Pmd,i = 1−Pd,i) for thei- thSU expressed as [16]. A.10), where Pf a,i and Pmd,i represent individual SU probabilities of false alarm and false detection in local decisions (u1,u2, ..,un) as shown in the figure.

Rayleigh Fading Channel Statistics

Under the Rayleigh fading channel, the false alarm probability fori-thSUPf a,i is given by [18]. Probabilities given in Equations (A.12) and (A.13) are local probability error detection and false alarm for (u1,u2, ..,un)decisions made by SUs shown in Fig.

First Stage Optimization on SU’s Selection Criteria

Q(dr−1,n−1)−Q(dr,n−1) is the decremental detection factor, γi is the SNR of thei-thSU, and U=2TW is the time-bandwidth product. The value of Q(gdr,n) in equation (A.20) can be iteratively derived as follows. A.22) where Pf a,r is the local false alarm probability for ther-thSU. Pd,i n∈ {i=1, 2, .,N} (A.25) where Q(gdn) is iteratively derived as follows: Qgd(n) =Q(dpn−1)Pd,n, but Pd , the detection probability for the nth SU and N is the total number of SUs in the CSS network.

Second Stage Optimal Strategy

The goal is to find the optimal k out of ndefined by differentiating Qd with respect to Qf, formulated as. A.36). Compare the listed values of global Qd(k,Pf a,i) for all numbers of kSUs and select the highest one among the list, this gives the optimal number of ks for the optimal out of nrule. To achieve a good trade-off between these opposing objectives of throughput and energy consumption, it is more convenient to optimize the parameters of k out of n for the maximum energy efficiency (η).

Energy Optimization Setup

Cooperative spectrum sensing (CSS) alleviates the problem of imperfect detection of primary users (PUs) in cognitive radio (CR) networks by exploiting spatial diversity of the different secondary users (SUs). Cooperative spectrum sensing (CSS) uses multiple secondary users (SUs) to sense the vacant spectrum and send their decision to the fusion center (FC) for a final global decision to be made about the presence of the primary user (PU) on the channel. The cost function is to maximize the probability of detection subject to minimizing the probability of false alarm.

Practical Cooperative Sensing Model

Proposed Cooperative Spectrum Model

Spectrum Sensing Hypothesis

Spectrum Sensing HOS Techniques

The hypothetical omnibus test is derived by comparison with the defined threshold value (K2λ), formulated as. B.15) For a predetermined Pf, the omnibus test threshold is a fixed value determined by. 1 is a small deviation from the critical value for the skewness of the estimated distributed random data, W2 = (√ .4B2−4−1) is a normalization constant for the skewness, δ= √1. The skewness as a function of the variance µ2(skewness) is formulated as follows. B.20) whereDis is a constant indicating the degrees of freedom for the chi-square distribution.

Fusion Strategy Hypothesis Tests

The conditional (per channel) false alarm probability is given as . B.27) where θ is the phase angle, γ is the SNR of the signal, σ0 is the modulation constant and µ0 is the mean of the data distribution as given in Table B.1.

First Stage Optimization on SU Selection Criteria

By Lagrange's theorem, the maximum threshold is obtained by differentiating by parts as follows. where i = 1, ..,n is the number of SUs selected to participate in fusion and λi∗ is the initial optimal threshold derived as . B.34) where σs2 is the noise variance, γi is the SNR of the ith SU, and M is the number of signal data samples. The global probability of detection inr out of nrule is derived as. B.25), r is the actual number of SUs that constitute r out of n counting rules and is the total number of SUs selected to participate in the decision making. Q(fr−1,n−1)−Q(fr,n−1) (B.41) where P(H0)andP(H1) are the weights for the probability of false(Pf,n|γ,θ) and the probability for detection (Pd,n|γ,θ) is the number of SUs participating in detecting the presence or absence of the PU on the channel, γ is the SNR, and θ is the uniformly distributed phase angle, respectively.

Second Stage Optimal Strategy

Next, the global probability of detecting ink from a case is given as B.46), we distinguish the function according to parts as follows. B.3, the ROC curves show the probability of detection (Pd) against SNR as formulated in algorithm 1 for omnibus (omnb), Jarque Bera (JB), kurtosis & skewness (kurt&skew) and kurtosis (kurt) test statistics. B.5, the shows the global probability of detection (Qd) against false alarm (Qf) as described in the second phase optimization, for optimal timeout rule based on HOS tests.