In this work, we present a terrain-aware 3D multi-unmanned aerial vehicles (UAV) cooperative spectrum sensing (CSS) framework that jointly optimizes UAV placement and antenna orientation to maximize detection probability while minimizing hover energy. A bounding volume hierarchy (BVH)-based adaptive scheme is proposed for efficient line-of-sight (LoS) evaluation, and a hierarchical genetic algorithm (GA)–particle swarm optimization (PSO) approch is adopted to address the inherently non-convex bi-objective problem, achieving a balanced exploration of the search space while ensuring LoS connectivity and energy efficiency. Monte Carlo simulations using real terrain data demonstrate up to 37.02% improvement in detection probability and 48.90% reduction in hover energy compared to PSO-only baselines, confirming the practicality and effectiveness of the proposed framework for uneven terrain environments.

Spectrum sensing plays a major role for dynamic spectrum access in Cognitive Radio Networks as we move towards 6G networks characterized by high-frequency operation, ultralow latency requirements, and dense spectrum usage. Most state-of-the-art spectrum sensing techniques suffer from high computational complexity and degraded performance in low Signal-to-Noise Ratio (SNR) environments. To overcome these shortcomings, we propose a Fourier Analysis Network (FAN)-based spectrum sensing approach that specifically addresses the challenge of low detection probability in low-SNR conditions while significantly reducing computational cost. The proposed FAN-based framework integrates Fourier transform properties within the network layers, allowing it to better model periodic and frequency-localized characteristics of wireless signals, such as those found in 5G and 6G-based waveforms. We have evaluated the proposed approach on three datasets that include 5G-NR, DeepSig, and Radar. The experimental results show that the proposed method achieves an improvement in the detection probability of 32.88% and 91.43% compared to the baseline models KAN-SS and PU-DetNet, respectively, at SNR = -10dB. The proposed scheme also outperforms the baseline models in terms of F1-score, with a reduction of 50.60% in FLOPs.

The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach.

Band assignment is an important function in multi-band heterogeneous networks. Most of the existing works have considered the rate of user equipment (UE) as a key criterion for the band assignment. However, the power consumption at the mmWave band is significantly higher than the Sub-6 GHz band, which is particularly significant because the UE battery power is limited. In this context, we design a novel long short term memory (LSTM) aided energy efficient band assignment system for a moving UE. Corresponding to the realistic scenario, we apply a timeseries split based approach to train and test the model. The proposed policy is validated on the publicly available ‘DeepMIMO’ dataset. Research findings shows that the RMSE of predicted rate using timeseries split approach is much less than the conventional approach. Moreover, with proposed scheme, the median energy efficiency is 56.30% higher as compared to the case when energy usage is not considered in the bandswitching decision, while the median rate with our proposed scheme reduces only by 13.81%.

The high frequency communication bands (mm Wave and sub- THz) promise tremendous data rates, however, they also have very high power consumption which is particularly significant for battery-power-limited user-equipment (UE). In this context, we design an energy aware band assignment system which reduces the power consumption while also achieving a target sum rate of$M$in$T$time-slots. We do this by using 1) Rate forecaster(s); 2) Channel forecaster(s) which forecasts$T$direct multistep ahead using a stacked long-short term memory (LSTM) architecture. We propose an iterative rate updating algorithm which updates the target rate based on current rate and future predicted rates in a frame. The proposed approach is validated on the publicly available ‘DeepMIMO’ dataset. We find that the rate forecaster based approach performs better than the channel forecaster. Furthermore, LSTM based predictions outperforms well celebrated Transformer predictions in terms of normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE). Research findings reveals that the power consumption with this approach is ~300 mW lower compared to a greedy band assignment at a 1.5Gb/s target rate.

The task of classifying human emotions based on facial expressions has been a challenging area of research for the past two decades. Early approaches relied on hand-crafted features such as SIFT, HOG, and LBP and classifiers trained on facial expression datasets. However, these methods failed to provide accurate results on newer, more complex datasets. Recently, the trend has shifted towards using deep learning models for facial expression classification, providing improved results. Nevertheless, there is still room for improvement in the accuracy of these models. In this research, we propose a novel model to improve facial expression classification accuracy on several benchmark datasets. Our model is based on generating nine different patches for different relevant facial regions and uses nine different streams, each for one of those micro-facial regions. We evaluated our model on the CK+, Oulu-Casia, and RAF-DB datasets and achieved state-of-the-art results, including 10-fold cross-validation accuracy of 99.8% and 91.25% for CK+ and Oulu-Casia datasets, respectively. Our proposed model demonstrates a significant improvement in facial expression classification accuracy and provides a promising approach to future research in this field.

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami-$m$fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its$idle$and$busy$periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) has emerged as an effective paradigm to solve problem of inefficient spectrum utilization. Spectrum sensing is the key in DSA/CR. Spectrum sensing decisions can be utilized to accurately estimate the primary channel activity statistics (PAS) like mean of idle/busy period, Duty cycle etc. Such estimated statistics can be used by CR to improve performance. However, when the secondary user (SU) is mobile, estimating these statistics becomes challenging. Taking this into account, this work provides a thorough review on the estimation of PAS for mobile SUs, considering vehicular scenario. The random way-point based mobility model is adopted for modelling SU mobility. Specifically, this work provides a set of closed form expressions for the estimated statistics under SU mobility as a function of the true PAS, SU velocity, initial distance between PU and SU, PU's protection range (R), SU's sensing range (S).

With the rapid development of intelligent transportation systems, vehicular devices are getting connected with each other. However, this leads to the problem of spectrum scarcity. Dynamic spectrum access (DSA)/cognitive radio (CR) has emerged as an effective solution to solve the problem of inefficient spectrum utilization. Spectrum sensing is the key in DSA/CR system. In cognitive vehicular networks (CVNs), spectrum sensing becomes more complex and challenging and that often leads to a loss in performance detection. Due to the effect of channel fading/shadowing and due to secondary user (SU) mobility, individual SUs may not be able to detect the existence of primary user (PU). In this paper, we propose a weighted cooperative spectrum sensing (weighted-CSS) framework for accurate detection of PU in CVNs. The weights are calculated from the probability of PU being inside the SU's sensing range and SU being outside the PU's protection range (inside probability). The calculated weight for SU indicates the reliability in the signal received by SU. The framework contains two stages. In the first stage, inside probability is calculated at each SU and the inside probability and the energy signal received from PU are sent to a base station (BS). In the second stage, BS assigns a weight to each SU based on the inside probability and makes a decision by combining the information received from SUs. Numerical results indicate that, on an average, the proposed framework performs ≈15% better than the conventional local spectrum sensing.

In contrast with the traditional cryptography, physical layer security has attracted the attention of many researchers having aim at reinforcing the security of communication systems. As far as vehicular communication is concerned, it is challenging to maintain secure and reliable communication between the connected vehicles due to the density, mobility, and dynamic network topology. This paper considers a vehicle to infrastructure communication in which a legitimately fixed transmitter equipped with a single antenna transmits a confidential message to a legitimate mobile receiver equipped with multiple antennas in the presence of a passive mobile eavesdropper. In such a single input multiple output wireless system, the receiver performs the maximal ratio combining technique assuming constant vehicle speed. We assume that the antennas are closely spaced and depending upon the imperfect channel state information (CSI), we derive the closed-form expressions for the average outage probability, secrecy outage probability, and average secrecy outage rate over the uniform, exponential, and arbitrary correlated Nakagami- m channels for dual antenna branches. In order to gain insight we also perform the high SNR asymptotic analysis of the outage probability and secrecy outage probability. Simulations are conducted to validate the accuracy of our derived analytic expressions. The computation error analysis is carried out to provide the suitability of the correlation type at the legitimate receiver side. Our findings suggest that the performance of the case with exponential channel correlation is better than those for the uniform and arbitrary. Numerical results show the joint effect of vehicle mobility and the antenna correlation on secrecy performance. Moreover, we also observed that the imperfect knowledge of the CSI degrades the security of the confidential messages severely under the effect of mobility.

This paper investigates the performance of non-orthogonal multiple access (NOMA) in vehicular networks where a base station (BS) communicates with the vehicles moving away from the BS with single-input multiple-output. To combine the signals received at the antennas, diversity combining techniques such as maximal ratio combining (MRC) and selection combining (SC) are performed at the receiver of each vehicle. However, in practice, the expected performance from the diversity techniques may not be achieved due to the fact that all the diversity branches are not independent and identically distributed (i.i.d) all the time. In this context, analytical expressions of the outage probability and ergodic sum rate are derived for the considered vehicular networks with the assumption of independent but not necessarily identically distributed (i.n.i.d) Nakagami-${m}$fading channels. The performance analysis of NOMA vehicular networks is also extended for multiple-input multiple-output antenna configurations and evaluated in the presence of successive interference cancellation (SIC) error propagation. The obtained analytical results are validated by Monte Carlo simulations. Furthermore, the performance of NOMA is verified with conventional orthogonal multiple access (OMA) for fading parameter$m=1$and$m=2$with perfect channel knowledge and channel estimation. Numerical results show that NOMA outperforms the conventional OMA by approximately 20% and has high sum rate with i.n.i.d as well as i.i.d channel consideration. However, i.n.i.d consideration degrades the performance of NOMA and OMA as the diversity gain achieved with i.n.i.d consideration is less as compared to i.i.d consideration. The performance is further deteriorated with SIC error and channel estimation.

In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.

Primary channel activity statistics play an important role in improving the performance of Dynamic Spectrum Access (DSA) / Cognitive Radio (CR) systems. The statistical information of the idle/busy periods of a primary channel can be estimated based on the outcomes of spectrum sensing. Recent studies have shown that these statistics can be estimated accurately even under Imperfect Spectrum Sensing (ISS) scenarios. Those studies, however, have assumed no constraints on the required sample size of observations of the idle/busy periods in order to provide accurate estimation (i.e., large sample size was assumed to test the accuracy of these statistics estimation methods). In real-world scenario, DSA/CR systems are limited to the hardware design capabilities, which include limited memory capacity, energy consumption and computational capability. As a result, it is very important to find how many samples of the idle/busy periods are required to provide an acceptable level of accuracy for the estimated statistics. Therefore, this work analyses the impact of the sample size on the estimation of the primary channel statistics under ISS and it finds closed-form expressions for the required sample size of the idle/busy periods to achieve a targeted accuracy. In addition, the analytical results achieved in this work are validated by means of simulations and hardware experiments.

Cognitive Radio (CR) is aimed at increasing the efficiency of spectrum utilisation by allowing unlicensed users to opportunistically access licensed spectrum bands during the inactivity periods of the licensed users. CR systems can benefit from an accurate knowledge of the spectrum occupancy patterns and their statistical properties. This statistical information can be obtained by periodically monitoring (sensing) the idle/busy state of the licensed channels. However, a reliable estimation of the primary activity statistics may require long observations times. This work proposes efficient methods to reduce the observation time required to produce a reliable estimation of the primary activity statistics. Furthermore, a method enabling CR users to quantify the accuracy of the estimated statistics is also proposed. Compared to other existing approaches, the proposed methods can provide accurate estimations of the primary activity statistics in significantly shorter observation times, thus allowing CR users to quickly adapt to new unknown operating channels.

Statistical information of primary channels has received considerable research interest in the recent years. This is due to the important role that these statistics play in improving the performance of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. Although a DSA/CR system has no initial knowledge about the statistical information of the primary channels, these statistics can be estimated from the observations of spectrum sensing. However, spectrum sensing is not perfect in the real world and sensing errors are likely to occur during DSA/CR operation, which in turn leads to incorrect estimation of primary channel statistics as well. As a result, several attempts have arisen to reconstruct the estimated periods of the primary channel occupancy patterns which are affected by the sensing errors, in order to provide more accurate estimation for the statistical information. However, all the reconstruction methods available in the literature assume the perfect knowledge of the primary users' minimum occupancy time. In this context, this work proposes the first reconstruction method that does not require any prior knowledge about the primary channel activity and inactivity patterns while achieving almost the same performance achieved by the latest reconstruction methods available in the literature, making it significantly attractive and feasible in practical implementation scenarios.

In this work, energy detection‐based spectrum sensing for multiple antenna receiver under the effect of mobility is investigated by considering L number of correlated antenna branches. The authors consider the uniform, exponential and arbitrarily correlation among the antenna branches based on the spacing between them. The moment generating function (MGF) approach is applied to obtain the statistical knowledge of the received signal to noise ratio because the Laplace domain behaviour will help to derive the closed‐form expressions using simple algebraic operations. They derived the closed‐form expressions for the detection probability over Nakagami‐ m fading, in terms of Lauricella and Confluent Hypergeometric function for maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques under the effect of vehicle mobility. Monte‐Carlo simulation is carried out to validate the derived analytical expressions. The results show that the degradation in detection performance due to fading correlation can be reduced by choosing the appropriate diversity scheme and by increasing the number of antennas. Furthermore, they also found that at high fading parameter ( ) value, the low value of the probability of false alarm and highly correlated fading, MRC works better than EGC for high relative velocity.

Cooperative spectrum sensing (CSS) is a technique where multiple cognitive radio users cooperate among themselves to make binary decisions about the presence of a primary user. The single cognitive user often faces the hidden terminal problem. However, CSS tackles this problem by sending local sensing‐based decisions to the fusion centre. A major drawback of conventional energy detection is the poor performance at low SNR regime. In this work, likelihood ratio statistics is considered as a test‐statistic due to its highest statistical power. An improved likelihood ratio statistic‐based CSS scheme is proposed by considering several past sensing events. The proposed scheme mitigates the poor detection at low SNR regime and misdetections arising due to sudden drops in signal energy. Furthermore, the generalised Byzantine attack is taken into account considering a security aspect. The proposed scheme is also shown to outperform Anderson Darling‐based malicious user detection in CSS at a low SNR regime. The proposed scheme is verified and validated over empirical spectrum data. The performance improvement is at the cost of computational time, which in practice is very low and is justified by the significant performance improvements of the proposed scheme at low SNR regime.

Primary channel statistics have recently gained increasing attention due to its remarkable role in the performance improvement of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. These statistics can be calculated from the outcomes of spectrum sensing, which is the well-known method used to identify the available instantaneous opportunities in the spectrum. Computing statistical information from spectrum sensing, however, may sometimes be unreliable due to the fact that spectrum sensing is imperfect in the real world and errors are likely to occur in the sensing decisions. In this context, this work provides a detailed analysis of a broad range of primary channel statistics under Imperfect Spectrum Sensing (ISS) and finds a set of closed-form expressions for the calculated statistics under ISS as a function of the original primary channel statistics, probability of error, and the employed sensing period. In addition, the obtained mathematical expressions are employed to find and propose novel estimators for the primary channel statistics, which outperform the existing estimators in the literature and can provide accurate estimations of the original statistics even under high probability of error of spectrum sensing. The correctness of the obtained analytical expressions and the accuracy of the proposed estimators are corroborated with both simulation and experimental results.

The emerged Dynamic Spectrum Access (DSA) concept based on Cognitive Radio (CR) is a promising solution to overcome the problems related to frequency spectrum scarcity. In DSA/CR systems, the inactivity patterns of the licensed frequency channels are exploited in an opportunistic and non-interfering manner by unlicensed users. Therefore, the knowledge of the occupancy rate (i.e., duty cycle) of these licensed channels is crucial for boosting the performance of the DSA/CR system. For example, it can help to select the lowest occupied channel which can offer higher opportunistic spectrum to the unlicensed users. Channel Duty Cycle (DC) is a statistical parameter about the activity of the licensed channel in time- domain, which is initially unknown to the DSA/CR system but can be estimated from the outcomes of spectrum sensing. However, spectrum sensing is imperfect in practice due to sensing errors, which in turn will provide incorrect estimation of the channel DC. In this context, this work successfully finds a novel method to accurately estimate the channel DC even under Imperfect Spectrum Sensing (ISS) without requiring any prior knowledge about the licensed channel activity. This is achieved after accurately analysing the impact of ISS on the estimation of the statistical moment (mean) of the channel activity periods, for which a closed form expression is obtained as a function of the true mean, probability of errors and sensing period. The achieved mathematical expression helps to find a novel method to accurately estimate the true mean of the channel activity periods and subsequently the channel DC based on the outcomes of the ISS.

The application of machine learning models to spectrum sensing in cognitive radio is not uncommon in literature, but most of these models fail to consider temporal dependencies in the signal. In this paper, the temporal correlation among the spectrum data is exploited using a Long Short-Term Memory (LSTM) network. More specifically, the previous sensing event is fed along with the present sensing event to the LSTM model. The proposed sensing scheme is validated based on empirical data of various radio technologies. The proposed LSTM model is compared with other machine learning algorithms in terms of classification accuracy. Furthermore, the proposed scheme is also compared with other spectrum sensing techniques. Results indicate that the proposed scheme improves the detection performance and classification accuracy at low signal-to-noise ratio regimes. Moreover, it is observed that the achieved improvement is obtained at the expense of longer training time and nominal increase in execution time.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network, thus being a promising approach to solve the problem of spectrum scarcity. Energy Detection, a spectrum sensing technique for DSA/CR systems, is widely used for blind sensing of unused frequency bands due to its non-parametric sensing ability and computationally low complexity. However, spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary User's (SU's) mobility and often yields a detection performance loss as compared to static scenarios. In order to mitigate the impact of reduced detection performance due to mobility, the usage of an improved version of energy detection technique is proposed in this paper. Usage of Improved Energy Detection (IED) technique in CVNs results more than 10% increment in DSA/CR system performance. In this paper, we study the joint impact of SU's sensing range, PU's protection range and SU's mobility model on the PU Activity using IED technique in CVNs, with detection probability and probability of false alarm as the performance metrics. Also, we derive a closed form expression for the probability of PU being inside SU's sensing range. Based on the proposed framework, numerical results show great agreement with analysis, yielding a superior performance.

An accurate estimation of the noise floor is of paramount importance for an optimum performance of spectrum sensing in Cognitive Radio (CR). The most common approach followed by existing noise floor estimation methods is to attempt to isolate a set of noise-only samples based on a given energy/power threshold. However, this approach is unreliable and in general unable to provide accurate estimations of the noise floor, in particular under low SNR conditions where the power of the Primary User (PU) signal is comparable to the noise floor of the CR device. In this context, this work considers a different approach where the power observed by the CR device is modelled as a Gaussian mixture. Based on a mathematical analysis of the relation among the parameters of the obtained Gaussian mixture, a modified version of the well-known Expectation Maximisation (EM) algorithm is proposed to fit the Gaussian mixture to the observed power values and provide an estimation of the noise floor, something that the general EM algorithm fails to achieve in this scenario. The obtained results demonstrate that the proposed method provides a highly accurate estimation of the noise floor in the presence of PU signals over the whole range of SNR values.

Most of the existing approximations for the Gaussian Q-function have been developed bearing in mind applications that require high estimation accuracy for large argument values (e.g., derivation of the bit/symbol error rates of digital communication systems, which are typically in the order of 10-6to 10-12). Such values correspond to positive arguments of the function and consequently most of the existing approximations are valid for positive arguments only. However, other relevant problems where the Gaussian Q-function can appear do not require such a level of accuracy (e.g., derivation of the detection probability of a signal detector, where accuracies of two or three decimal figures are sufficient) and, more importantly, require the evaluation of the Q-function over the whole range of values (i.e., both positive and negative arguments). In this context, this paper analyses a sigmoid approximation to the Q-function that provides adequate levels of accuracy for any real argument. As an illustrative example, this approximation is employed to obtain new closed-form expressions for the probability of detection of an energy detector under Rayleigh and Nakagami- m fading channels.

Primary channel activity statistics (such as the minimum idle/busy periods of the channel, the moments or the underlying distributions) can be exploited by cognitive radio (CR) systems to adapt their operation and improve their performance. Such statistics can be directly estimated from periodic observations of the instantaneous idle/busy state of the primary channel (i.e., periodic spectrum sensing). However, the periodicity of such observations (i.e., the sensing period) imposes a fundamental limit on the time resolution in which idle/busy periods can be observed, and consequently on the accuracy of any subsequent estimated statistics. In this context, this paper provides a comprehensive analysis on the estimation of the primary activity statistics based on periodic channel state observations performed with a finite sensing period. In particular, this paper provides a comprehensive set of closed-form expressions for the estimated statistics as a function of the true primary activity statistics and the employed sensing period. These expressions can find a wide range of applications in the analysis, design, and simulation of CR systems. Moreover, several methods to minimize the estimation errors and improve the accuracy are proposed and validated with both simulations and hardware experiments.

Recent advancements in Full Duplex (FD) radio have built a promising idea for faster channel sensing in cognitive radio. Full duplex cognitive radio (FDCR) provides an efficient way to utilize the idle channel without interrupting the ongoing transmission. Currently, non-parametric sensing techniques like energy detection and its modified techniques are implemented for FDCR. In this paper, we introduce a Goodness of Fit based distribution-free sensing in FDCR. With Monte Carlo simulations and analytical approximation, we show that the proposed technique outperforms energy detection and other goodness-of-fit based sensing algorithms for FDCR.

Spectrum sensing is a key aspect of Cognitive Radio (CR). The main requirement in CR systems is the ability to sense the primary signal accurately and rapidly. In this paper, a novel hybrid spectrum sensing scheme in CR is proposed which considers the hypothesis problem as a binary classification problem. The proposed scheme is a combination of classical energy detection, Likelihood Ratio Test statistic (LRS-G2) and Artificial Neural Network (ANN). The scheme utilises energy from energy detection and Zhang test statistic from LRS-G2as features to train the ANN while ANN provides the adaptive learning and stable performance to the scheme. The performance of proposed sensing scheme is evaluated on several real-world primary signals of various radio technologies and it has been found out that for all those radio technologies the proposed scheme outperforms the classical energy detection and the improved energy detection.