Outage performance analysis of multiple users in cognitive networks

A cooperative model of multiple primary and secondary users coexisting cognitive network is presented. In this model, the control center is aware of all the users＇ locations in order to allocate the nearest secondary user to the primary user. The control center is aware of the information of the unused spectral resources in terms of the feedback of the sensing results from the secondary users. It allocates idle frequency bands among the secondary users. The primary user accesses the base station （BS） in orthogonal subchannels, and it cooperatively transmits packets with the secondary user and exploits the free band assigned by the control center to amplify-and-forward what it receives immediately. Under this scenario, the outage probability of the cooperative transmission pair of the primary and secondary transmitters is derived. The numerical simulation of the outage probabilities as a function of primary transmission probability ps, power allocation ratio ξ between the primary and secondary users, and the numbers of the primary and secondary users are given respectively. The results show that the optimal system performance is achieved under the conditions of ξ=0.5 and the numbers of the primary and the secondary users being equal.

Downlink Resource Allocation for NOMA-Based Hybrid Spectrum Access in Cognitive Network

To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources in a downlink multi-user cognitive radio(CR)network with slicing.Secondary users(SUs)are multiplexed using non-orthogonal multiple access(NOMA).The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE).Considering the demand for multiple services,the enhanced mobile broadband(eMBB)slice and ultrareliable low-latency communication(URLLC)slice were established.The proposed scheme can maximize the SE while ensuring Quality of Service(QoS)for the users.This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network.According to the signal-to-interference-plusnoise ratio(SINR)of the primary users(PUs),the proposed scheme can output the optimal channel selection and power allocation.The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme.Additionally,the proposed scheme has better SE than both the overlay and underlay only modes.

Cognitive Network Management in Internet of Things

The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the＂Internet of Things＂ （IoT）. Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.

Interference Alignment Based on Subspace Tracking in MIMO Cognitive Networks with Multiple Primary Users

The interference alignment （IA） algorithm based on FDPM subspace tracking （FDPM-ST IA） is proposed for MIMO cognitive network （CRN） with multiple primary users in this paper. The feasibility conditions of FDPM-ST IA is also got. Futherly, IA scheme of secondary network and IA scheme of primary network are given respectively without assuming a priori knowledge of interference covariance matrices. Moreover, the paper analyses the computational complexity of FDPM-ST IA. Simulation results and theoretical calculations show that the proposed algorithm can achieve higher sum rate with lower computational complexity.

Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network

The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.

Performance Analysis of Mixed Amplify-and-Forward and Decode-and-Forward Protocol in Underlay Cognitive Networks

In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocols, in the proposed protocol, a secondary source attempts to transmit its signal to a secondary destination with help of two secondary relays. One secondary relay always operates in AF mode, while the remaining one always operates in DF mode. Moreover, we also propose a relay selection method, which relies on the decoding status at the DF relay. For performance evaluation and comparison, we derive the exact and approximate closedform expressions of the outage probability for the proposed protocol over Rayleigh fading channel. Finally, we run Monte Carlo simulations to verify the derivations. Results presented that the proposed protocol obtains a diversity order of three and the outage performance of our scheme is between that of the conventional underlay DF protocol and that of the conventional underlay AF protocol.

Approach for Local Multi-Domain Cognition in Cognitive Network

During the evolution from cognitive radio to cognitive networks,the environment cognition extended from wireless environments to network and user environments.To understand the basic theory of Local Multi-Domain Cognition(LMDC),and to provide a theoretical basis for further study in cooperative multi-domain cognition and initiative multi-domain cognition,the LMDC is investigated in this paper.A Local Single-Domain Cognitive(LSDC)approach is first proposed based on multidimensional edge detection theory.This approach can divide the parameter space that describes the single-domain environment into different areas,and can represent each area with an identifier.Using this as a foundation,the single-domain environment is extended to multi-domain environments,and an LMDC approach is presented to describe the LMDC environment.The paper concludes by introducing two examples and the corresponding analysis to illustrate the feasibility of the proposed LMDC approach.

Resource management with borrowing from reservation for cognitive networks

Resource reservation is an effective measure to ensure end-to-end quality of service （QoS）, however, the burstyness of the traffic makes the reservation idle some time, and forms a waste of re- sources. Based on the analysis of active queue management （AQM） of DiffServ network, we propose a resource management scheme, which allows borrowing resources from unused reservation, accord- ing to the characteristics and advantages of cognitive networks. First, some nodes reserve certain proportion capacity for some special services （for instance, some services pay additional money） to guarantee the priority of these applications. Then resources are assigned according to the different parameters of services. If the available resource can not meet the requirements of new services, real- time ones are admitted with higher priority and allow borrowing the unused reservation from other nodes appropriately. Simulations show that, the proposed scheme has good performance at network resource utilization, the admission rate of new aDolications and OoS of users.

A CHANNEL ASSIGNMENT ALGORITHM VIA CONFLICT SHIFTING FOR DISTRIBUTED COGNITIVE NETWORKS

Channel assignment is a challenge for distributed cognitive networks due to spectrum mobility and lack of centralized entity.We present a dynamic and efficient algorithm via conflict shifting,referred as Shifting-based Channel Assignment(SCA).In this algorithm,the system was modeled with a conflict graph,and users cannot assign the channels that primary users(legacy users) and neighbors already occupied.In order to eliminate the conflicts between neighbors efficiently,secondary users(unlicensed users) try to transfer them through a straight path to the boundary,where conflicts are easier to solve as there are less neighbors for boundary users.Actions in one shift are executed in slots,and users act in a synchronous and separated manner.As a result,some of the conflicting channels are avoid from directly abandoned,and for this,utility of the entire network can be improved.Simulation results show that the proposed algorithm can provide similar utility performance while obviously reducing the communication cost than bargaining-base algorithms.In small scale networks with low user mobility(under 20%),it reduces 50% of the communication overhead than the later.

Self-Adaptive QoS Control in Cognitive Networks That Is Based on Service Awareness

This paper analyzes a self-adaptive Quality of Service （QoS） control architecture for cognitive networks （CNs） that is based on intelligent service awareness. In this architecture, packets can be identified and classified using an intelligent service-aware classification model. Drawing on Control Theory, network traffic can be controlled with a self-adaptive QoS control mechanism that has side-road collaboration. In this architecture, perception, analysis, correlation, feedback, decision making, allocation, and implementation QoS mechanisms are created automatically. These mechanisms can adjust resource allocation, adapt to a changeable network environment, optimize end-to-end performance of the network, and ensure QoS.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Wireless Networked Cognitive Radar System:Overview and Design Guidelines

Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Different from the adaptive signal processing at the receiver in adaptive radar,the cognitive radar realizes closedloop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment.As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart,the wireless networked cognitive radar(WNCR)attracts increasing research.This article firstly reviews the concept and development of cognitive radar,especially the related researches of networked cognitive radar.Then,the co-design of cognitive radar and communication is investigated.Although the communication quality between radar sensing nodes is the premise of detection,tracking,imaging and anti-jamming performance of the WNCR,the latest researches seldom consider the communication architecture design for WNCR.Therefore,this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application,and the corresponding guidelines are proposed to inspire future research.

Security-Reliability Analysis and Optimization for Cognitive Two-Way Relay Network with Energy Harvesting

This paper investigates the security and reliability of information transmission within an underlay wiretap energy harvesting cognitive two-way relay network.In the network,energy-constrained secondary network(SN)nodes harvest energy from radio frequency signals of a multi-antenna power beacon.Two SN sources exchange their messages via a SN decode-and-forward relay in the presence of a multiantenna eavesdropper by using a four-phase time division broadcast protocol,and the hardware impairments of SN nodes and eavesdropper are modeled.To alleviate eavesdropping attacks,the artificial noise is applied by SN nodes.The physical layer security performance of SN is analyzed and evaluated by the exact closed-form expressions of outage probability(OP),intercept probability(IP),and OP+IP over quasistatic Rayleigh fading channel.Additionally,due to the complexity of OP+IP expression,a self-adaptive chaotic quantum particle swarm optimization-based resource allocation algorithm is proposed to jointly optimize energy harvesting ratio and power allocation factor,which can achieve security-reliability tradeoff for SN.Extensive simulations demonstrate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.

Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process

Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.

Throughput scheduling in cognitive radio networks based on immune optimization

To study the throughput scheduling problem under interference temperature in cognitive radio networks, an immune algorithm-based suboptimal method was proposed based on its NP-hard feature. The problem is modeled as a constrained optimization problem to maximize the total throughput of the secondary users（ SUs）. The mapping between the throughput scheduling problems and the immune algorithm is given. Suitable immune operators are designed such as binary antibody encoding, antibody initialization based on pre-knowledge, a proportional clone to its affinity and an adaptive mutation operator associated with the evolutionary generation. The simulation results showthat the proposed algorithm can obtain about 95% of the optimal throughput and operate with much lower liner computational complexity.

Qo E-based resource allocation protocols in cognitive OFDMA network with hybrid model

A resource allocation protocol is presented in an orthogonal frequency division multiple access （OFDMA） cognitive radio （CR） network with a hybrid model which combines overlay and underlay models. Without disrupting the primary user （PU） transmissions, the overlay model allows the secondary user （SU） to utilize opportunistically the idle sub-channels; the underlay model allows the SU to occupy the same sub-channels with PU. The proposed protocols are designed for maximizing the quality of experience （QoE） of CR users and switching dynamically between the overlay and underlay models. QoE is measured by the mean opinion score （MOS） rather than simply fulfilling the physical and medium access control （MAC） layer requirements. The simulations considering the file transfer and video stream services show that the proposed resource allocation strategy is spectrum efficient.

Joint Beamforming and Time Switching Designs for Energy-Constrained Cognitive Two-Way Relay Networks

In this paper, we investigate a joint beamforming and time switching(TS) design for an energy-constrained cognitive two-way relay(TWR) network. In the network, the energy-constrained secondary user(SU) relay employs TS protocol to harvest energy from the signals sent by the circuit-powered primary user(PU) transmitter, and then exploits the harvested energy to perform information forwarding. Our aim is to maximize the sum rate of SUs under the constraints of the data rate of PU, the energy harvesting and the transmit power of the SU relay. To determine the beamforming matrix and TS ratio, we decouple the original non-convex problem into two subproblems which can be solved by semidefinite relaxation and successive convex optimization methods. Furthermore, we derive closed form expressions of the optimal solutions for each subproblem, which facilitates the design of a suboptimal iterative algorithm to handle the original sum rate maximization problem. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.

Optimization of Cognitive Femtocell Network via Oppositional Beetle Swarm Optimization Algorithm

In past decades,cellular networks have raised the usage of spectrum resources due to the victory of mobile broadband services.Mobile devices create massive data than ever before,facing the way cellular networks are installed pre-sently for satisfying the increased traffic requirements.The development of a new exclusive spectrum offered to meet up the traffic requirements is challenging as spectrum resources are limited,hence costly.Cognitive radio technology is pre-sented to increase the pool of existing spectrum resources for mobile users via Femtocells,placed on the top of the available macrocell network for sharing the same spectrum.Nevertheless,the concurrent reuse of spectrum resources from Femto networks poses destructive interference on macro networks.To resolve this issue,this paper introduces an optimal channel allocation model using the Oppo-sitional Beetle Swarm Optimization Algorithm(OBSOA)to allocate the channel with interference avoidance.A new OBSOA is derived in this paper by the inclu-sion of opposition-based learning(OBL)in BSOA.This algorithm allocates the channels used by PUs(PUs)to the secondary users(SUs)in such a way that inter-ference is minimized.This proposed approach is implemented in the MATrix LABoratory(MATLAB)platform.The performance of this proposed approach is evaluated in terms of several measures and the experimental outcome verified the superior nature of the OBSOA-based channel allocation model.OBSOA mod-el has resulted in a maximum signal-to-interference-plus-noise ratio value of 86.42 dB.

Outage performance of cognitive multisource multidestination relay networks with imperfect channel state information and interference from primary transmitter

Given imperfect channel state information(CSI)and considering the interference from the primary transmitter,an underlay cognitive multisource multidestination relay network is proposed.A closed-form exact outage probability and asymptotic outage probability are derived for the secondary system of the network.The results show that the outage probability is influenced by the source and destination number,the CSI imperfection as well as the interference from the primary transmitter,while the diversity order is independent of the CSI imperfection and the interference from the primary transmitter,yet it is equal to the minimum of the source and destination number.Moreover,extensive simulations are conducted with different system parameters to verify the theoretical analysis.

Energy-aware cooperative spectrum sensingfor underground cognitive sensor networks

With the development of wireless technologies,multifarious standards are currently used in the underground coal mine communication systems.In this paper,the coexistence of 802.15.4 based wireless senser networks （WSNs） with other wireless networks using cognitive radio technique are discussed.Multiple sensor nodes are involved in the spectrum sensing to avoid the interference from other wireless users.The more the sensor nodes cooperate in the sensing,the better the detection performance can be obtained; however,more energy is consumed.How to get the tradeoff between energy efficiency and detection performance is a key problem.According to the requirements for detection,we first give the least required detection time of a single sensor node.Then,the voting fusion rule is adopted for the final decision making.Finally,the relationship between final detection performance and energy consumption is analyzed.