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The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. [ABSTRACT FROM AUTHOR]

A novel multistatic integrated sensing and communication (ISAC) system based on macro–micro cooperation for the sixth-generation (6G) mobile network is proposed. Instead of using macrosites at both the transmitter and receiver sides, microsites are considered as receivers in cooperative sensing. This system is important since microsites can be deployed more flexibly to reduce their distances to the sensing objects, providing better coverage for sensing service. In this work, we first analyze the deployment problem of microsites, which can be deployed along the radius and azimuth angle to cover macrosite cells. The coverage area of each microsite is derived in terms of its position in the cell. Then, we describe an efficient estimating approach for obtaining the position and velocity of sensing objects in the macrosite cell. By choosing multiple microsites around the targeted sensing area, joint data processing with an efficient optimization method is also provided. Simulation results show that the multistatic ISAC system employing macro–micro cooperation can improve the position and velocity estimation accuracy of objects compared to systems employing macrosite cooperation alone, demonstrating the effectiveness and potential for implementing the proposed system in the 6G mobile network. [ABSTRACT FROM AUTHOR]

Achieving higher accuracy of spectrum sensing (SS) becomes a challenge in cognitive radio adhoc network (CRAN) due to presence of varied power levels, interference, multipath fading and hidden node problems. Cooperative sensing based SS address these challenges by making spectrum decision based on distributed sensing measurements from multiple nodes. A major problem in cooperative SS is that, false sensing measurements sent by compromised node can impact the spectrum access probability and network services. These false measurements also affect the channel utilization calculation and disrupt the QoS based routing protocols for cognitive radio (CR) networks. The compromised node can also compromise the privacy of SU by leaking communicated messages. In this work, we provide a cross layer QoS assured routing system for CRAN that is secure as well as protects privacy. The proposed solution solves the problems of false sensing measurements using Kolmogorov Smirnov test on spectrum measurements in both spatially and temporal contexts. Attacks on spectrum measurements are detected and filtered using sequential probability test. Cross layer interactions are used in decision on source rate control, multipath selection and adaptive queing to provide guaranteed QoS. The proposed solution is able to increase the spectrum sensing accuracy by at least 7.6% in presence of sensing attacks and increase packet delivery ratio by at least 4% compared to existing works. [ABSTRACT FROM AUTHOR]

A novel multistatic integrated sensing and communication (ISAC) system based on macro–micro cooperation for the sixth-generation (6G) mobile network is proposed. Instead of using macrosites at both the transmitter and receiver sides, microsites are considered as receivers in cooperative sensing. This system is important since microsites can be deployed more flexibly to reduce their distances to the sensing objects, providing better coverage for sensing service. In this work, we first analyze the deployment problem of microsites, which can be deployed along the radius and azimuth angle to cover macrosite cells. The coverage area of each microsite is derived in terms of its position in the cell. Then, we describe an efficient estimating approach for obtaining the position and velocity of sensing objects in the macrosite cell. By choosing multiple microsites around the targeted sensing area, joint data processing with an efficient optimization method is also provided. Simulation results show that the multistatic ISAC system employing macro–micro cooperation can improve the position and velocity estimation accuracy of objects compared to systems employing macrosite cooperation alone, demonstrating the effectiveness and potential for implementing the proposed system in the 6G mobile network.

In the vehicular visual sensor networks, multiple vehicles with on-board cameras capture visual observations of the environment while moving along their trajectories and the 3-D map is jointly estimated to provide guidance for real-time navigation. Due to high mobility of vehicles and heavy computation burden of 3-D map estimation, the cooperative vehicular visual sensing is latency-critical and computation-intensive, and thus suitable to be solved by edge computing. This article designs an air-ground integrated mobile edge computing (MEC) framework for the vehicular visual sensor networks, in which parallel computation is implemented by the vehicles locally and also partially offloaded to the edge server on the unmanned aerial vehicle (UAV), and our aim is to minimize the total latency of the network through the optimization of the UAV trajectory and the offloading ratios of vehicles. To characterize the motion, sensing, computation, and communication processes in the network, the timeline includes two periods, with the motion period for motion and sensing and the service period for computation and communication, where the service period is divided into several stages with the latency models for computation or communication in each stage. Then we propose a dynamic alternate trajectory design and partial offloading scheduling algorithm for the air-ground integrated MEC in the vehicular visual sensor networks. Numerical results verify the effectiveness of our scheme in terms of latency and also show the impacts of computation burden, computation capability, and communication condition on performance. [ABSTRACT FROM AUTHOR]

The detection of 3D spectrum opportunities in the downlink sharing scenario in satellite-terrestrial integrated networks was investigated. First, defined the 3D space-time spectrum opportunities in the satellite-terrestrial integrated networks, and further divided the opportunities into three areas in the spatial domain: the communication area of primary user, the communication protection belt, and the free access area. Then, based on the proposed 3D opportunity model, derived the closed expressions of the detection and false alarm probability at the user-level and the network-level, respectively. In order to further utilized the spatial spectrum opportunity, a power control scheme of the cognitive user was determined through 3D spectrum sensing, i.e., interference constraint (IC). The simulation results demonstrated that the proposed power control scheme could realized the detection of 3D spectrum opportunity. [ABSTRACT FROM AUTHOR]

The tremendous growth of the wireless communications and their applications stimulate the urgent need to keep on the available radio spectrum. As a result, cognitive radio (CR) technologies are proposed and developed to manage the limitation of the available spectrum by methods of sensing and sharing the free channels. Wideband spectrum sensing algorithms have a great impact of detecting the vacant channels of the whole spectrum simultaneously. Cooperative sensing techniques are introduced based on sharing users' sensing outcomes among other users. Therefore, it represents an efficient method to overcome signal shadowing and fading problems. Recently, artificial intelligence (AI) techniques are considered to improve the quality of service (QoS) parameters in cognitive radio networks. In this paper, an adaptive Neuro-Fuzzy interference system (ANFIS) algorithm is proposed in the process of decision-making to detect the optimal and accurate free channels. ANFIS model is trained with some pertinent features over a Music-like channel power level (PMU(k)), channel identity number (k), and channel repetition number. Consequently, the second stage is introduced by applying ANFIS technique on the adaptive blind cooperative wideband spectrum sensing basis to select the optimum required number of cooperative users with increasing performance based on the detected signal to noise ratio (SNR) level per secondary user. Simulation is based on Simulink of five users with different SNR due to fading and shadowing problems. Simulation results proved that, the proposed technique based on cooperative spectrum sensing algorithm with ANFIS model for detection outperformed other traditional detection techniques. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) and spectrum harvesting (SH) are two promising and useful green communication and networking mechanisms for the next-generation wireless networks. While the former techniques exploit ambient energy sources to scavenge energy, the latter exploit the unused or moderately used electromagnetic spectrum. With the advent of cyber-physical systems and the Internet-of-Things (IoT), the presence of tens of billions of low power sensor devices would soon be a reality. These small sensing devices would be present in many systems around us, such as home appliances, telecommunication devices, medical electronics, transport systems, etc. These miniaturized, low-power consuming devices may exploit EH and SH techniques for energy storage and communication. These EH-SH-enabled sensors or low-power nodes need to consume very little energy for sensing and communicating opportunistically. However, several theoretical problems and practical challenges exist in EH-SH communications. In this comprehensive survey paper, we first present the historical background of EH, and SH techniques, and their development over several decades. Specifically, we focus on EH-SH communication technologies and protocols for a wide range of systems and networks. We present a detailed survey of the various harvesting techniques and protocols from recent literature. Finally, we describe exciting open, intra-disciplinary, and inter-disciplinary challenges for further research on EH-SH communication technologies.

The sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we propose to develop a Vehicular Fog Computing (VFC) architecture to implement cooperative sensing among multiple adjacent vehicles driving in the form of a platoon. Based on our VFC architecture greedy and Support Vector Machine (SVM) algorithms are adopted respectively to enhance the sensing coverage and accuracy in the platoon. Furthermore, the distributed deep learning is processed for trajectory prediction by applying the Light Gated Recurrent Unit (Li-GRU) neural network algorithm. Simulation results based on real-world traffic datasets indicate the sensing coverage and accuracy by the proposed algorithms can be significantly improved with low computational complexity.

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users' presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network's performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

Automated vehicles make use of multiple sensors to detect their surroundings. Sensors have significantly improved over the years but still face challenges due to the presence of obstacles or adverse weather conditions, among others. Cooperative or collective perception has been proposed to help mitigate these challenges through the exchange of sensor data among vehicles using V2X (Vehicle-to-Everything) communications. Recent studies have shown that cooperative perception can complement on-board sensors and increase the vehicle's awareness beyond its sensors field of view. However, cooperative perception significantly increases the amount of information exchanged by vehicles which can degrade the V2X communication performance and ultimately the effectiveness of cooperative perception. In this context, this study conducts first a dimensioning analysis to evaluate the impact of the sensors' characteristics and the market penetration rate on the operation and performance of cooperative perception. The study then investigates the impact of congestion control on cooperative perception using the Decentralized Congestion Control (DCC) framework defined by ETSI. The study demonstrates that congestion control can negatively impact the perception and latency of cooperative perception if not adequately configured. In this context, this study demonstrates for the first time that the combination of congestion control functions at the Access and Facilities layers can improve the perception achieved with cooperative perception and ensure a timely transmission of the information. The results obtained demonstrate the importance of an adequate configuration of DCC for the development of connected and automated vehicles.

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

We study cooperative spectrum sensing in cognitive radio (CR) networks using the hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel operates in a time division multiple address (TDMA) manner. Thus, spectrum sensing is operating in a slot-by-slot basis. In contrast to the conventional Bayesian updating using only one observation, in this work, we propose to perform the update in a concatenated fashion with all the observations available from the secondary users (SUs). In the proposed scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed concatenated update scheme with that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of the further increase in the number of SUs are limited.

Green energy powered cognitive radio is proposed as a promising method to improve the energy efficiency (EE) and spectrum efficiency (SE). We consider the green power farms that harvest energy from solar and wind, which is used for the primary transmitter and the cognitive sensor network (CSN). The primary transmitter has priority to utilize the spectrum and the energy, and then the energy powered for the CSN may not be sufficient. Both the energy management and energy harvesting process will affect the throughput of the cognitive sensors (CSs). In this paper, we aim to design the system parameters in the CSN (including the sensing threshold, the sensing time, the final decision threshold in the fusion center, and the number of cooperating CSs) that can improve the utilization efficiency of the harvested energy and maximize the CSs' throughput. Since the parameters are intertwined with the energy causality constraint and the average throughput, we decouple the influence of final decision threshold on the throughput from the influence of the sensing time and sensing threshold. The optimization problem is divided into two sub-problems. Algorithm 1 and Algorithm 2 are proposed to solve sub-problem 1 and sub-problem 2, respectively. The simulation results show that the proposed scheme can improve the average throughput significantly, and is able to achieve the best tradeoff between SE and EE.

To exploit the spectrum reuse potential and enhance the cooperative sensing ability of the cooperative sensing unmanned aerial vehicle network (CSUN), we propose a novel joint sensing-communication (JSC) CSUN that can simultaneously conduct downward-looking radar sensing and sensing data fusion communication with the unified spectrum and transceiver by adopting the beam sharing scheme. To achieve the beam sharing scheme, we design a novel antenna array equipped on each UAV, which can generate a sensing beam and a communication beam orthogonally for downward detection and data-fusion communication, respectively. To quantify the cooperative sensing performance of CSUN, we propose and formulate a novel upper-bound average cooperative sensing area (UB-ACSA) as the cooperative sensing performance metric, based on deriving the closed-form expressions for the average mutual sensing interference of CSUN and the communication capacity constraints. Finally, extensive simulations are conducted to validate the theoretical results. We also demonstrate that the cooperative sensing performance of the JSC CSUN can improve by 66.3 percent compared with the conventional CSUN in terms of UB-ACSA. [ABSTRACT FROM AUTHOR]

Vehicle localization and mobility tracking are important tasks in autonomous driving. Traditional methods either have insufficient accuracy or rely on additional facilities to reach the desired accuracy for autonomous driving. In this paper, a multi-sensor multi-vehicle localization and mobility tracking framework is developed for autonomous vehicles equipped with GPS, inertial measurement unit (IMU), and an integrated sensing system. Our algorithm fuse the information from local onboard sensors as well as the observations of other vehicles or existing intelligent transportation system infrastructure such as road side units (RSU) to improve the precision and stability of localization and mobility tracking. Specifically, this framework incorporates the dynamic model of vehicles to achieve better localization and tracking performance. The communication delays during the information sharing process are explicitly taken into account in our algorithm development. Simulations manifest that not only the accuracy of localization and mobility tracking could be greatly enhanced in general, but also the robustness can be guaranteed under circumstances where traditional localization and tracking devices fail. [ABSTRACT FROM AUTHOR]

In this article, an optimal threshold level value to improve spectrum sensing performance that satisfies minimum error probability is derived. Determining the threshold level to minimize sensing error with the minimum number of samples required can improve the detection performance, thus resulting in enhanced spectrum usage. A closed‐form expression for the optimal threshold level is derived, and further simplified by using the sensing error function along with the optimal weights and transmit powers to the decision‐making fusion center. Numerical results are provided and show improved sensing performance compared to conventional schemes. [ABSTRACT FROM AUTHOR]

Summary: With the advent of various emerging wireless products, the usage of limited spectrum has grown exponentially in the recent years. In the next few years, that mobile data traffic globally is expected to grow up to 50 EB/month, which is nearly a five times increase over year 2018. Therefore, it will become extremely difficult to satisfy the ever increasing demand through the current fixed spectrum assignment policy in which spectrum band is exclusively used for the particular applications, and it has also led to underutilization of a significant portion of the spectrum (like TV bands). Cognitive radio networks has emerged as a possible solution for the problem which makes dynamic spectrum access possible for unlicensed user when licensed user is not active. Among various operations of cognitive radio, channel assignment to the unlicensed user is very important. Further, wireless regional area network is one of the most important application of cognitive radio, which provides wireless broadband to the rural area using vacant TV channels. This paper discusses channel assignment techniques considering various functionalities for cognitive radio networks in respect of wireless regional area network in the existing literature. Initially, a comprehensive introduction to both cognitive radio networks and wireless regional area networks is provided, and in the end, the paper summarizes the various issues and research challenges in the channel assignment for wireless regional area networks. [ABSTRACT FROM AUTHOR]

In the process of spectrum perception, in order to realize accurate perception of the channel state, the method of multi-node cooperative perception can usually be used. However, the first problem to be considered is how to complete information fusion and obtain more accurate and reliable judgment results based on multi-node perception results. The ideas put forward in this paper are as follows: firstly, the perceived results of each node are obtained on the premise of limiting detection probability and false alarm probability. Then, on the one hand, the weighted fusion criterion of decision-making weight optimization of each node is realized based on a genetic algorithm, and the useless nodes also can be screened out to reduce energy loss; on the other hand, through the linear fitting ability of RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the normal operation of the perceptive work of each node. What's more, the real-time training data can be obtained by spectral segmentation technology to ensure the real-time accuracy of the optimization results. Finally, the simulation results show that this method can effectively improve the accuracy and stability of channel perception results, optimize the structure of the cooperative network and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Efficient, quick and reliable spectrum sensing in consonance with maximum throughput is required at the cognitive radios to achieve maximum utilization of the network. In cognitive radio environment, the intelligent secondary users shall effectively sense the presence of primary user signal to avoid interference, in order to achieve maximum operational feasibility. A cooperative sensing scheme using Kullback Leibler divergence with higher throughput and lower sensing time to avail and identify the underutilized frequency spectrum is presented in this paper. The optimized sensing scheme utilizes the log-likelihood ratio estimated at the secondary user during each sensing instance, with fusion centre acquiring and assimilating this information to estimate the likelihood of spectrum availability. The optimized scheme is implemented in a robust manner assuming the signal and noise are gaussian distributed. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput over various false alarm probability and signal to noise ratio are presented. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a wireless technology developed to improve the usage in the spectrum frequency. Energy consumption is considered as a big problem in this technology, especially during a spectrum sensing. In this paper, we propose an algorithm to improve the energy consumption during the spectrum sensing. The theoretical analysis to calculate the amount of energy consumption, using the proposed method during sensing stage as well as the transmission stage during transmitting a local decision to the fusion center FC, are derived. The proposed algorithm is using energy detection technique to detect the presence or absence of the primary user (PU). The proposed algorithm consists of two stages: the coarse sensing stage and fine sensing stage. In the coarse sensing stage, all the channels in the band are sensed shortly and the channel that have maximum (or minimum) energy is identified to make a dense fine sensing for confirming the presence of the PU signal (or hole). The performance of the proposed algorithm is evaluated in two scenarios: non-cooperative, and cooperative in both the AWGN and Rayleigh fading channels. The simulation results show that the proposed method improves the energy consumption by about 40% at a low SNR values, when compared with the traditional methods based on a single sensing stage and more advanced method based on censoring and sequential censoring algorithms.

An innovative EE-oriented cooperative sensing and transmission scheme in relay-assisted cognitive radio networks,called energy-efficient best-relay cooperative transmission (BCT) was proposed.Based on the BCT scheme,mean energy efficiency (MEE) maximization problem with sensing duration and transmitting power as optimization variables was modeled for fading channels under constraint of minimal secondary outage probability.By virtue of Jensen’s inequality,the original optimization problem was decomposed into two relatively independent subproblems which solved sensing duration and power allocation respectively.And for the two subproblems,an efficient cross iteration based algorithm was proposed to obtain the suboptimal solutions.Both analytical and simulation results demonstrate that the proposals can achieve significantly higher EE while enhancing reliability of secondary transmission remarkably compared to non-cooperation single cognitive transmission schemesin high QoS requirement.

Recently the subject of energy-efficient is very important in cognitive radio (CR), especially during spectrum sensing, since the large energy consumption (EC) cost, produces a restriction in their implementation especially in devices with limited power, i.e battery. In these design, energy detector consumes a significant part of energy during spectrum sensing to detect the activity of the primary user (PU). In this paper, we investigated a method for improving EC in two scenarios: non-cooperative and cooperative. The idea behind the improvement is based on sensing the spectrum with low-density samples. The optimization concept for reducing EC through controlling the number of frequency samples to be sensed is illustrated as well as the probability of detection in both scenarios. To evaluate the proposed method a comparison is made between the proposed method and censoring method. The performance of energy detection system is evaluated in AWGN and Rayleigh fading channels. The simulation results show that in non-cooperative scenario at Eb/No equal 10 dB, and for sensing ratio equal 50%, EC decreases by 50% and 46% with sma loss in of detection probability of 5% in AWGN channel and 12% in Rayleigh channel. In cooperative scenario, the results show that as the number of cognitive users (CU) increased the average EC per user decreased with an improvement in probability of detection. In case of sensing ratio 50%, the EC is decreased by 43.6% as compared with censoring method.

In this paper, we propose a cooperative cognitive radio network (CCRN) based on energy detection and hard-decision fusion. The performance characteristics are investigated analytically in the presence of noise plus generalized fading channels. In particular, scenarios with κ – μ and η – μ fading channels affecting the sensing channels are considered. More precisely, Each cognitive radio user (CRU) relies on an energy detector (ED). The signal from the primary user (PU), received by a CRU, is fed to the ED, and the energy of signal is used to make a local decision. At the fusion center (FC), the decisions received at the FC are fused, using hard-decision fusion, to obtain a final decision on the status of the PU. The performance of CCRN, through total error rate and throughput is evaluated considering the impact of relevant network parameters. Towards that, first we derive the novel mathematical expressions for probability of detection, subject to generalized fading. Also, Monte Carlo simulation is performed to validate the derived expressions. Next, the analytical frame works based on derived expression for evaluating total error rate and throughput performances for any network and channel conditions are developed. Further, the impact of an erroneous sensing (S) and reporting (R) channels on overall performance of CCRN is also investigated. Finally, the impact of the generalized fading parameters, the signal-to-noise ratio, the time-bandwidth product, the number of CRUs, the detection threshold, and the channel error probability on the total error rate and throughput performances is investigated. [ABSTRACT FROM AUTHOR]

In this paper, the analytical performance evaluation of the cooperative cognitive radio network (CCRN) with hard-decision and soft-data fusions is investigated in the presence of noisy and generalized $\kappa -\mu$ fading channel. The novel expressions to calculate detection probability of CCRN subject to $\kappa -\mu$ fading and fusion are derived. The analysis of various fusions is evaluated and compared. The performance of CCRN, considering an erroneous sensing and reporting channels, is studied. Also, an optimal threshold subject to fusion scheme is computed. The CCRN performance is evaluated through miss detection probability and total error rate, considering the significant impact of channel and network parameters. Finally, Monte Carlo simulation is performed to validate the derived expressions. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) technology is regarded as a key network technology used to manage the limitation of the available spectrum in wireless communication networks. Spectrum Sensing (SS) is the core process in CR engine based on detecting the free channels and sharing it among other users. In wideband spectrum, many algorithms are proposed to sense the available free channels. Cooperative sensing is mainly considered as an effective solution of signal fading and shadowing problems in CR networks. From another side, CR networks can utilize Artificial Intelligence (AI) techniques for dynamically sensing and decision making processes. In this paper, a blind adaptive spectrum sensing algorithm based over centralized cooperative sensing platform is proposed. Then, an Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is applied in decision-making process to achieve the optimum and accurate decisions. The simulation process and the output results showed that, the proposed technique outperforms both a standalone sensing techniques and other cooperative sensing with conventional decision making algorithms regarding to the probability of false alarms, probability of detection and probability of missed detection especially in low Signal to Noise Ratio (SNR). The results obtained by the proposed algorithm based on ANFIS decision making technique outperformed the other conventional decision making techniques. [ABSTRACT FROM AUTHOR]

Intending to enhance the utilization of the radio spectrum in cognitive radio without inflecting the primary user is a primary issue. Cooperative sensing techniques have been proposed to improve the radio access decision compensating for the inherent sensing errors present in the devices. Meanwhile collaboration among secondary users poses a potential security threat. A malicious node might alter the resulted sensing information to attain a personal benefit. This text quantifies the number of nodes getting affected by the malicious nodes' false reports by deriving formulas validated by simulated scenarios. The analysis of this paper is based on the assumption that nodes are deployed according to a poisson point process. First, we estimate the scope of influence of the malicious node, it is shown in a one dimensional network that a malicious node can take advantage of its relative position with respect to other neighboring nodes in order to leverage its influence. Then, the influence of this malicious node in a two dimensional network is investigated varying its relative position. The derivations are validated by simulations of the network carried out via R programming language describing the relevant deployment scenarios. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the unified performance analysis of an energy detector (ED) over α - η - μ /IG and α - κ - μ /IG generalized fading channels. The probability density functions (PDF) of the received signal-to-noise ratio (SNR) for the composite fading channels are derived using Gauss-Laguerre method. Novel analytical expressions for the average probability of detection (PD) for a single user and cooperative spectrum sensing (SS) are derived. The effects of diversity and noise uncertainty on the average PD are also presented. Rapidly convergent infinite series representations for the average PD are derived in terms of the upper bound of the truncation error. These expressions are used to analyze the behavior of ED-based SS over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. The accuracy of the derived expressions is validated through comparison of results obtained by Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper we present a Cooperative Spectrum Sensing (CSS) algorithm for Cognitive Radios (CR) based on IEEE 802.22Wireless Regional Area Network (WRAN) standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration) to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22) within a time constraint (channel sensing time). To meet this objective, we have developed CSS algorithm using unsupervised K-means clustering classification approach. The received energy level of each Secondary User (SU) is considered as the parameter for determining channel availability. The performance of proposed algorithm is quantified in terms of detection accuracy, training and classification delay time. Further, the detection accuracy of our proposed scheme meets the requirement of IEEE 802.22 WRAN with the target probability of falsealrm as 0.1. All the simulations are carried out using Matlab tool.

Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms.

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.