Research on Network Cognition Model and Mechanism of Intelligent Information Network

Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.

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.

Secrecy Rate Maximization for 6G Cognitive Satellite-UAV Networks

To cover remote areas where terrestrial cellular networks may not be available,non-terrestrial infrastructures such as satellites and unmanned aerial vehicles(UAVs)can be utilized in the upcoming sixth-generation(6G)era.Considering the spectrum scarcity problem,satellites and UAVs need to share the spectrum to save costs,leading to a cognitive satellite-UAV network.Due to the openness of both satellite links and UAV links,communication security has become a major concern in cognitive satelliteUAV networks.In this paper,we safeguard a cognitive satellite-UAV network from a physical layer security(PLS)perspective.Using only the slowlyvarying large-scale channel state information(CSI),we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users.The optimization problem is a mixed integer nonlinear programming(MINLP)problem with coupling constraints.We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory,the max-min optimization tool,and the bipartite graph matching algorithm.The simulation results corroborate the superiority of our proposed scheme.

Cognition Based Reconfigurable Wireless Network Technology

The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technology and cognitive theory, the evolution from existing radio networks to future reconfigurable radio networks with the cognitive ability becomes possible. Nowadays the research aspects of E2R include the system architecture of reconfigurable radio networks and some key technologies for their evolution.

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.

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.

Intelligent Covert Communication Design for Cooperative Cognitive Radio Network

Extensive research attentions have been devoted to studying cooperative cognitive radio networks(CCRNs),where secondary users(SU)providing cooperative transmissions can be permitted by primary users(PU)to use spectrum.In order to maximize SU’s utility,SU may transmit its own information during the period of cooperative transmission,which stimulates the use of covert transmission against PU’s monitoring.For this sake,this article reviews the motivations of studying covert communications in CCRN.In particular,three intelligent covert transmission approaches are developed for maximizing SU’s utility in CCRNs,namely,intelligent parasitic covert transmission(IPCT),intelligent jammer aided covert transmission(IJCT)and intelligent reflecting surface assisted covert transmission(IRSC).Further,some raw performance evaluations are discussed,and a range of potential research directions are also provided.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.

Secure Transmission in Cognitive Radio Networks Using Full-Duplex Technique with Outdated CSI

This paper investigates the effects of the outdated channel state information(CSI)on the secrecy performance of an underlay spectrum sharing cognitive radio networks(CRNs),where the secondary user(SU)source node(Alice)aims to transmit the trusted messages to the full-duplex(FD)aided SU receiver(Bob)with the assistance of cooperative relay(Relay).Considering the impact of feedback delay,outdated CSI will aggravate the system performance.To tackle such challenge,the collaborative zero-forcing beamforming(ZFB)scheme of FD technique is further introduced to implement jamming so as to confuse the eavesdropping and improve the security performance of the system.Under such setup,the exact and asymptotic expressions of the secrecy outage probability(SOP)under the outdated CSI case are derived,respectively.The results reveal that i)the outdated CSI of the SU transmission channel will decrease the diversity gain from min(NANR,NRNB)to NRwith NA,NRand NBbeing the number of antennas of Alice,Relay and Bob,respectively,ii)the introduction of FD technique can improve coding gain and enhance system performance.

Robust beamforming for IRS-aided SWIPT in cognitive radio networks

Intelligent reflecting surface(IRS)is widely recognized as a promising technique to enhance the system perfor-mance,and thus is a hot research topic in future wireless communications.In this context,this paper proposes a robust BF scheme to improve the spectrum and energy harvesting efficiencies for the IRS-aided simultaneous wireless information and power transfer(SWIPT)in a cognitive radio network(CRN).Here,the base station(BS)utilizes spectrum assigned to the primary users(PUs)to simultaneously serve multiple energy receivers(ERs)and information receivers(IRs)through IRS-aided multicast technology.In particular,by assuming that only the imperfect channel state information(CSI)is available,we first formulate a constrained problem to maximize the minimal achievable rate of IRs,while satisfying the harvesting energy threshold of ERs,the quality-of-service requirement of IRs,the interference threshold of PUs and transmit power budget of BS.To address the non-convex problem,we then adopt triangle inequality to deal with the channel uncertainty,and propose a low-complexity algorithm combining alternating direction method of multipliers(ADMM)with alternating optimi-zation(AO)to jointly optimize the active and passive beamformers for the BS and IRS,respectively.Finally,our simulation results confirm the effectiveness of the proposed BF scheme and also provide useful insights into the importance of introducing IRS into the CRN with SWIPT.

AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy

With the advent of the Industry 5.0 era,the Internet of Things(IoT)devices face unprecedented proliferation,requiring higher communications rates and lower transmission delays.Considering its high spectrum efficiency,the promising filter bank multicarrier(FBMC)technique using offset quadrature amplitude modulation(OQAM)has been applied to Beyond 5G(B5G)industry IoT networks.However,due to the broadcasting nature of wireless channels,the FBMC-OQAMindustry IoT network is inevitably vulnerable to adversary attacks frommalicious IoT nodes.The FBMC-OQAMindustry cognitive radio network(ICRNet)is proposed to ensure security at the physical layer to tackle the above challenge.As a pivotal step of ICRNet,blind modulation recognition(BMR)can detect and recognize the modulation type of malicious signals.The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes.A novel FBMC BMR algorithm is proposed with the transform channel convolution network(TCCNet)rather than a complicated two-dimensional convolution.Firstly,this is achieved by designing a low-complexity binary constellation diagram(BCD)gridding matrix as the input of TCCNet.Then,a transform channel convolution strategy is developed to convert the image-like BCD matrix into a serieslike data format,accelerating the BMR process while keeping discriminative features.Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8%and 40%over the traditional inphase/quadrature(I/Q)-based and constellation diagram(CD)-based methods at a signal noise ratio(SNR)of 12 dB,respectively.Moreover,the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network(CD-AlexNet)and I/Q-Convolutional Long Deep Neural Network(I/Q-CLDNN)algorithms,respectively.

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

Research on Physical Layer Security in Cognitive Wireless Networks with Multiple Eavesdroppers Based on Resource Allocation Algorithm

With the rapid development of the Internet of Things (IoT), non-Orthogonal Multiple Access (NOMA) technology and cognitive wireless network are two promising technologies to improve the spectral efficiency of the system, which have been widely concerned in the field of wireless communication. However, due to the importance of ownership and privacy protection, the IoT system must provide corresponding security mechanisms. From the perspective of improving the transmission security of CR-NOMA system based on cognitive wireless network, and considering the shortcomings of traditional relay cooperative NOMA system, this paper mainly analyzes the eavesdropping channel model of multi-user CR-NOMA system and derives the expressions of system security and rate to improve the security performance of CR-NOMA system. The basic idea of DC planning algorithm and the scheme of sub-carrier power allocation to improve the transmission security of the system were introduced. An algorithm for DC-CR-NOMA was proposed to maximize the SSR of the system and minimize the energy loss. The simulation results show that under the same complexity, the security and speed of the system can be greatly improved compared with the traditional scheme.

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Cooperative Privacy Provisioning for Energy Harvesting Based Cognitive Multi-Relay Networks

In order to provide privacy provisioning for the secondary information,we propose an energy harvesting based secure transmission scheme for the cognitive multi-relay networks.In the proposed scheme,two secondary relays harvest energy to power the secondary transmitter and assist the secondary secure transmission without interfere the secondary transmission.Specifically,the proposed secure transmission policy is implemented into two phases.In the first phase,the secondary transmitter transmits the secrecy information and jamming signal through the power split method.After harvesting energy from a fraction of received radio-frequency signals,one secondary relay adopts the amplify-and-forward relay protocol to assist the secondary secure transmission and the other secondary relay just forwards the new designed jamming signal to protect the secondary privacy information and degrade the jamming interference at the secondary receiver.For the proposed scheme,we first analyze the average secrecy rate,the secondary secrecy outage probability,and the ergodic secrecy rate,and derive their closed-form expressions.Following the above results,we optimally allocate the transmission power such that the secrecy rate is maximized under the secrecy outage probability constraint.For the optimization problem,an AI based simulated annealing algorithm is proposed to allocate the transmit power.Numerical results are presented to validate the performance analytical results and show the performance superiority of the proposed scheme in terms of the average secrecy rate.

Energy Efficient Transmission in Underlay CR-NOMA Networks Enabled by Reinforcement Learning

In order to improve the energy efficiency(EE)in the underlay cognitive radio(CR)networks,a power allocation strategy based on an actor-critic reinforcement learning is proposed,where a cluster of cognitive users(CUs)can simultaneously access to the same primary spectrum band under the interference constraints of the primary user(PU),by employing the non-orthogonal multiple access(NOMA)technique.In the proposed scheme,the optimization of the power allocation is formulated as a non-convex optimization problem.Additionally,the power allocation for different CUs is based on the actor-critic reinforcement learning model,in which the weighted data rate is set as the reward function,and the generated action strategy(i.e.the power allocation)is iteratively criticized and updated.Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning.Furthermore,the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions.According to the simulation results,we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method,while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

Optimal spectrum allocation of primary users in light-handed cognitive radio networks

In traditional cognitive radio(CR) network,secondary users(SUs) are always assumed to obey the rule of"introducing no interference to the primary users(PUs) ".However,this assumption may be not realistic as the CR devices becoming more and more intelligent nowadays.In this paper,with the concept of lighthanded CR,which is proposed to deal with the above mentioned problem by enforcing"punishment"to illegal CR transmissions,the action decisions of primary users(PUs) are modeled as a partially observable Markov decision process(POMDP),and the optimal spectrum allocation scheme with the objective of maximizing their reward is proposed,which is defined by the utility function.Furthermore,a reduced scheme with much smaller state space has been proposed in this paper for lower computational complexity.Extensive simulation results show that the proposed schemes improve the reward significantly compared to the existing scheme.

Power Control and Routing Selection for Throughput Maximization in Energy Harvesting Cognitive Radio Networks

This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes)harvest energy from the environment and use the energy exclusively for transmitting data.The SU nodes(i.e.,relay nodes)on the path,store and forward the received data to the destination node.We consider a real world scenario where the EH-SU node has only local causal knowledge,i.e.,at any time,each EH-SU node only has knowledge of its own EH process,channel state and currently received data.In order to study the power and routing issues,an optimization problem that maximizes path throughput considering quality of service(QoS)and available energy constraints is proposed.To solve this optimization problem,we propose a hybrid game theory routing and power control algorithm(HGRPC).The EH-SU nodes on the same path cooperate with each other,but EH-SU nodes on the different paths compete with each other.By selecting the best next hop node,we find the best strategy that can maximize throughput.In addition,we have established four steps to achieve routing,i.e.,route discovery,route selection,route reply,and route maintenance.Compared with the direct transmission,HGRPC has advantages in longer distances and higher hop counts.The algorithm generates more energy,reduces energy consumption and increases predictable residual energy.In particular,the time complexity of HGRPC is analyzed and its convergence is proved.In simulation experiments,the performance(i.e.,throughput and bit error rate(BER))of HGRPC is evaluated.Finally,experimental results show that HGRPC has higher throughput,longer network life,less latency,and lower energy consumption.

A TRUST MECHANISM-BASED CHANNEL ASSIGNMENT AND ROUTING SCHEME IN COGNITIVE WIRELESS MESH NETWORKS WITH INTRUSION DETECTION

Cognitive Wireless Mesh Networks(CWMN) is a novel wireless network which combines the advantage of Cognitive Radio(CR) and wireless mesh networks.CWMN can realize seamless in-tegration of heterogeneous wireless networks and achieve better radio resource utilization.However,it is particularly vulnerable due to its features of open medium,dynamic spectrum,dynamic topology,and multi-top routing,etc..Being a dynamic positive security strategy,intrusion detection can provide powerful safeguard to CWMN.In this paper,we introduce trust mechanism into CWMN with intrusion detection and present a trust establishment model based on intrusion detection.Node trust degree and the trust degree of data transmission channels between nodes are defined and an algorithm of calcu-lating trust degree is given based on distributed detection of attack to networks.A channel assignment and routing scheme is proposed,in which selects the trusted nodes and allocates data channel with high trust degree for the transmission between neighbor nodes to establish a trusted route.Simulation re-sults indicate that the scheme can vary channel allocation and routing dynamically according to network security state so as to avoid suspect nodes and unsafe channels,and improve the packet safe delivery fraction effectively.