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Multi-Access Edge Computing (MEC) reduces latency, provides high-bandwidth applications with real-time performance and reliability, supporting new applications and services for the present and future Beyond the Fifth Generation (B5G). Radio Network Information Service (RNIS) plays a crucial role in obtaining information from the Radio Access Network (RAN). With the advent of 5G, RNIS requires improvements to handle information from the new generations of RAN. In this scenario, improving the RNIS is essential to boost new applications according to the strict requirements imposed. Hence, this work proposes a new RNIS as a service to the MEC framework in B5G networks to improve MEC applications. The service is validated and evaluated, and demonstrates the ability to adequately serve a large number of MEC apps (two, four, six and eight) and from 100 to 2000 types of User Equipment (UE). [ABSTRACT FROM AUTHOR]

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type. [ABSTRACT FROM AUTHOR]

The internet of everything (IoEs) has become a well-known tool for transforming the concept of connected environments into reality in near future. The cognitive radio network is a potential candidate that addresses the issues of efficient spectrum utilization for next generation connected environments. Further, the spectrum mobility plays a significant role in cognitive connected environments during communication, for switching the channel on the appearance of primary user (PU) throughout the cognitive users' (CUs') data transmission. The performance of spectrum mobility relies on the ability of the system: 1) to detect the appearance of PU as soon as possible and 2) to stop the data transmission as-well-as switch to another channel. The potential approaches to detect the appearance of PU during CUs' data transmission are the "spectrum prediction (SP)" and "spectrum monitoring" (SM). The SP relies on the pre-available information about the channel and PUs' activities which is well explored technique, however, the SM is a real-time approach and is in its infancy. In this paper, several SM techniques with their effects on the spectrum mobility are illustrated. Moreover, the concept of imperfect SM is introduced and its effects on various performance metrics are investigated. Further, a potential approach of cooperative SM is proposed to diminish the effects of imperfections. In addition to this, the potential issues as well as research challenges regarding these techniques are presented. [ABSTRACT FROM AUTHOR]

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations. [ABSTRACT FROM AUTHOR]

In order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ε‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio networks (CRNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users (SUs) to opportunistically access the underutilized licensed spectrum bands. However, efficient channel assignment in CRNs, especially with user mobility and power control considerations, remains a challenging problem. Traditional channel assignment algorithms often struggle to adapt to changing network conditions, resulting in suboptimal performance. To address these challenges, this research presents a novel Taylor Tuna Optimizer‐based hybrid deep Q‐network (TayTO‐based HDQNet) for channel assignment in CRNs with user mobility and power control considerations. The proposed TayTO‐based HDQNet combines the Elman neural network architecture with the reinforcement learning framework of deep Q‐networks, tuned using the Taylor Tuna Optimizer. This hybrid approach enables intelligent channel assignment decisions while considering power consumption constraints, leading to efficient communication in CRNs. To train the HDQNet agent, a comprehensive dataset is generated through simulations of various CRN scenarios, incorporating channel availability, interference levels, and power consumption for different channel assignment decisions. The HDQNet agent undergoes iterative training using this dataset to develop an optimal channel assignment policy. Experimental results demonstrate the effectiveness of the proposed TayTO‐based HDQNet approach done by MATLAB, achieving a high success rate of 98.47% and surpassing the reliability scores of previous studies. This highlights the improved performance and reliability of the proposed approach for channel assignment in CRNs. [ABSTRACT FROM AUTHOR]

Summary: The discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s. [ABSTRACT FROM AUTHOR]

In this study, a new probabilistic approach is proposed to balance the traffic loads of secondary users (SUs) among channels. Furthermore, an adaptive load-balancing model is formulated to meet the time-varying radio environment. We employ the overall system time of SUs to evaluate the latency performance of the proposed approach. Besides, we derive the optimal channel selection probability to minimize the overall system time. Simulation results clearly verify the validity of our approach by comparing it with other alternative channel selection strategies. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the physical layer security (PLS) performance of nonorthogonal multiple access (NOMA)‐enabled overlay cognitive radio networks (NOMA‐OCRNs) in the presence of an external passive eavesdropper. Here PLS is expressed in terms of the secrecy outage probabilities (SOPs) experienced by the primary user (PU) and secondary user (SU). We obtain approximate expressions for the SOPs of both PU as well as SU assuming a jamming‐free environment, where both primary and secondary destination nodes are half‐duplex devices. To improve the SOP performance, we propose a jamming‐assisted framework, where full‐duplex destination nodes are employed, which are capable of transmitting jamming signals to confound the eavesdropper. Approximate expressions for the SOPs of PU and SU are derived for the jamming‐assisted framework as well. It is demonstrated that the proposed jamming‐assisted framework significantly reduces the SOPs compared to the jamming‐free scenario. We also determine optimal power allocation coefficients (OPACs) for PU and SU at the secondary transmitter that maximizes the total secrecy throughput of the jamming‐assisted NOMA‐OCRN with FD destinations. It is shown that the suggested OPAC significantly enhances the total secrecy throughput, compared to the default selection of the PAC. [ABSTRACT FROM AUTHOR]

• Reconfigurable graphene antenna. • Cognitive radio network. • Innovative solution for X-band satellite communications. • Improvement in satellite communication performance. This study introduces a graphene-based antenna tailored for X-band satellite communication within a cognitive radio network. Leveraging graphene's distinctive traits high conductivity and mechanical flexibility, the antenna design achieves reconfigurability in frequency, polarization, and radiation pattern. This adaptability enables dynamic adjustments to environmental shifts, bolstering the efficiency and dependability of satellite communication. Operating on software-defined radio technology, the cognitive radio system monitors and allocates the available frequency spectrum, allowing real-time adjustments to antenna parameters. Given the widespread usage of the X-band in satellite communications, this antenna proposal stands as an appealing solution for such systems. Its potential applications encompass providing broadband access in remote regions, supporting emergency communication networks, facilitating maritime and aeronautical communications, and enabling self-sustaining wireless networks. The approach involves a novel graphene antenna with slots to facilitate frequency reconfigurability. At 9 GHz, the graphene antenna achieves a gain of 6 dBi, surpassing the 4.3 dBi gain of a copper antenna. By varying the chemical potential of graphene from 0 to 4 eV , we achieved a high gain of 6.4 dBi at 9 GHz and an excellent reflection coefficient of −42 dB at 11.63 GHz. The graphene antenna's improved gain and tuning capabilities surpass traditional materials, showcasing adaptability across diverse frequencies, promising versatility for varied communication needs, and enhancing overall system reliability and effectiveness. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, a group of secondary users, in the absence of primary users, attempt to utilize the spectrum without causing interference by employing opportunistic access techniques. However, the sudden reemergence of primary users in the spectrum results in user interference and escalates the frequency of channel switching in a dynamic environment. In such scenarios, analytical methods like Markov processes and game theory are utilized to forecast spectrum mobility. Additionally, given that cognitive radio networks are deployed across various platforms as mobile networks, user mobility can exacerbate the inherent instability of cognitive radio networks, posing challenges in routing. To address these challenges, this study has developed a reactive routing protocol with several advantages: (1) leveraging cognitive radio capabilities and mobility, as well as intersection models, (2) minimizing the frequency of channel switching, (3) preventing user interference, (4) utilizing the availability of channels in the AODV protocol, (5) omitting the use of a common control channel, and (6) establishing a stable path through the calculation of Link Expiration Time (LET). Numerical simulations demonstrate that, in comparison to AODV and CAODV protocols, the proposed protocol has reduced channel switching by approximately 80% with an increase in the number of nodes. Furthermore, in this protocol, control overhead has decreased by over 90% in scenarios involving an escalation in both the number of nodes and their speed. [ABSTRACT FROM AUTHOR]

Interference channel with a cognitive relay (IFC-CR), as one of the complex channels, consists of two transmitter-receiver pairs, where, one relay knowing some information of transmitted messages cooperates with transmitters to improve the achievable rate region and overall communication performance. The cognitive relays hold a pivotal role in the context of cognitive radio networks (CRN), where efficient spectrum utilization is a paramount concern. To study the impact of a cognitive relay in a wireless interference channel it is necessary to compute the rate region of wireless IFC-CR. In this paper, the capacity inner and outer bounds of IFC-CR known for discrete alphabet and memoryless channels are extended to the continuous alphabet wireless version. Due to High computational complexity, the gap between the outer and inner bounds is determined through Numerical Results. Various scenarios about transmitter power levels and noise variance are considered to encompass a diverse range of realworld conditions. The inner and outer bounds provided in this paper become valuable tools for various aspects of practical analysis, for example, the inner bound can be used to investigate the coverage region and the outer bound for the outage probability, thereby, facilitating practical decision-making in wireless communication system design and optimization. [ABSTRACT FROM AUTHOR]

According to the urgent low latency and the heavy computation tasks demands required for sixth‐generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial‐intelligence (AI) methods, and matching theory enabled framework, in which the multi‐access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G‐enabled cognitive radio (CR) networks, so called cloud‐assisted CR‐MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated‐intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised. [ABSTRACT FROM AUTHOR]

This article delves into the investigation of parameters and associated issues in the context of a Bayesian game theory-based approach for selecting the target channel for secondary users in a licensed heterogeneous cognitive radio network. In such networks, cognitive or secondary users can enhance their activity within a licensed spectrum by dynamically selecting an available spectrum, thereby addressing the scarcity issue in the radio spectrum. The study adopts a game model where each user is treated as a network player, with the channel payoff or utility function serving as a crucial parameter for target channel selection. The game theory approach assumes that each player possesses knowledge of other players' payoffs, although this assumption may only hold for some games. In auction-based games, for instance, other players' payoffs or profits are unknown, introducing uncertainty. This type of game model falls under the Bayesian game model. The article presents the Bayesian battle of the sexes approach to address uncertainty in other players' payoffs or profits and proposes possible causes for channel selection. The best response for secondary users and channel payoffs is computed using this approach. The Bayesian Nash equilibrium calculates the average payoff, taking into account various spectrum handoff parameters like waiting time and inactive probability. This comprehensive analysis aims to calculate the overall network characteristics in the given heterogeneous cognitive radio environment. [ABSTRACT FROM AUTHOR]

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks' capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks. [ABSTRACT FROM AUTHOR]

The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the throughput and outage performance for an unmanned aerial vehicle (UAV)‐assisted non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network are examined under m‐Nakagami fading channel. To be more specific, a secondary source transmitter (SUT) broadcasts a NOMA‐enabled mixed signal to secondary destinations via UAV relay, which operates in full‐duplex (FD) mode and employs decode‐and‐forward protocol. In our approach, an energy harvesting (EH) circuit is employed at both SUT and UAV to harvest energy from the PU radio frequency (RF) signals. These secondary transmitters are also incorporated with energy detector (ED) circuit as well, which detects the activity of the primary user (PU). The status of PU to be busy or idle is confirmed on the basis of the energy detector's results. In turn, this drives SUT and UAV to switch between underlay and overlay protocols in an adaptive manner to increase spectrum efficiency (SE). The closed form mathematical expressions of the secondary outage probability for NOMA‐based UAV equipped CR networks under imperfect successive interference cancellation (i‐SIC) conditions have been evaluated. The administration of the power allocation policy at the SUT and UAV is assessed while maintaining the PU quality of service (QoS). Finally, MATLAB simulation testbed has been used to verify all the analytical closed form expressions. [ABSTRACT FROM AUTHOR]

Aiming to increase throughput in Wireless Networks, such as in Wireless Sensor Network and the Internet of Things, platforms emerged in which devices have two radios, and also data transfer protocols that prioritize maximum throughput and energy efficiency, using two different paths simultaneously. The usage of dual radios allowed simultaneous transmissions between wireless devices, which, besides increasing network throughput, can also improve network stability, delivery rate, transmission cost, and energy consumption per transmitted byte. However, one path may be much longer than the other, causing high latency. First, in this work, we present the problem formulation to find two disjoint paths with the same parity size for platforms with two heterogeneous radios to reach the network maximum flow, while also minimizing the longest path, which reduces latency. Second, we show that the problem is NP-Complete. Next, we present a solution based on integer linear programming. Moreover, we tested the solution on almost 5,700 instances obtained from an actual testbed and the results show a reduction in latency while maintaining the high throughput. [ABSTRACT FROM AUTHOR]

This paper examines the detection challenges faced by the cognitive radio network in the scenario where the node position of the secondary user changes rapidly, hence causing a significant amount of large-scale fading. To analyze this we have adopted a natural generalization of the fluctuating two-ray fading (FTR) channel that takes into account the simultaneous effects of both small and large-scale fading. Hence, in this direction, we have presented a composite fading model where small-scale fading is characterized as FTR distributed and large-scale fading as inverse gamma distributed. We derive exact analytical closed-form expressions for the composite probability distribution function (PDF) and cumulative distribution function (CDF) and then obtain a set of new statistical results. These results are further extended to obtain the analytical expressions for (i) Single user detection probability (ii) multi-user detection probability and (iii) average area under the receiver operating characteristic for energy detection-based spectrum sensing. A thorough investigation of the derived expression is used to quantify the degree of deterioration observed in the detection performance due to large-scale fading effects and the extent of performance benefit obtained by exploiting the cooperative spectrum sensing technique under different scenarios. The validity of the derived expressions is verified by Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Based on the data from the oblique-incidence sounding radio path network at mid- and subauroral latitudes of the Asian region in Russia, we investigated features of medium-scale traveling ionospheric disturbances (MS TID) within the 2016 winter and spring seasons. We revealed characteristic diurnal variations in the occurrence probability and recording time of MS TIDs along different-orientation paths. We analyzed the effect of the 2016 February minor sudden stratosphere warming (SSW) and the 2016 March final warming on the MS TID characteristics in the Asian region of Russia. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) is a technology that can alleviate the scarcity of radio resources, improve communication efficiency, and reduce electromagnetic radiation pollution. However, traditional research mostly concentrates on a single optimization function, which is too constrained to achieve global consideration. We suggest a multi-objective optimization problem (MOP) with the objectives of transmission rate and power efficiency. Then, we introduce a fairness factor with the minimum protection rate to ensure the quality of data transfer for each secondary user (SU). We use the ellipsoid set to characterize the uncertain parameters under the actual channel state information (CSI). In the worst case, the semi-infinite programming (SIP) problem is transformed into a secondorder cone programming (SOCP) problem. The original problem is linearly combined using the weighted-sum method to construct a single objective problem (SOP), which is then turned into a solvable convex optimization problem and resolved using the Lagrange dual algorithm and sub-gradient method. The simulation results demonstrate the ability of our proposed algorithm to balance power and transmission rate optimization by adjusting the weighting values, while maintaining good robustness. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) is developed to provide effective spectrum usage. CR is much significant in improving the efficiency of the global internet in applications. The evolutionary measurement technology is utilised to improve the evaluation of channel-state information. The outcome attained very few spectrums sensing in CR for complex mobility. A good optimisation method is needed to improve the accurate channel state prediction in successful channel access. Thus, this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation (MLF-AEO) Optimisation Strategy. This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users, Outage Probability, and throughput. The superiority of the proposed algorithm is thoroughly verified by various simulation results. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems. [ABSTRACT FROM AUTHOR]

Inspired by the stringent requirements of energy efficiency and spectral efficiency for future wireless communication networks, a novel Spatial Modulation Assisted Cooperative Cognitive Radio Network with Energy Harvesting (SM-CCRN-EH) is proposed in this paper. It is well known that Cognitive Radio (CR) is a promising solution to mitigate the effect of spectrum scarcity using unlicensed bands in spectrum. In the proposed CR network, SM is applied at both Primary Transmitters (PT) and Secondary Transmitter (ST) to improve spectral efficiency. The ST acts as a Decode and Forward (DF) energy harvested relay node. It uses multiple antennas for receiving signal from PT and single antenna for signal transmission. Energy harvesting at the ST is possible using time switching or power splitting protocol to improve the energy efficiency of the system. In order to quantify the reliability of the proposed system, closed form expressions are derived for outage probability and Bit Error Rate (BER) at the Primary Receiver (PR) and Secondary Receiver in both time slots. Computer simulations are performed to verify the accuracy of derived analytical expressions. It is observed that there is an excellent agreement for both analytical results and computer simulations. [ABSTRACT FROM AUTHOR]

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Cognitive radio is providing the solution to the challenges of spectrum scarcity. The central idea behind this technology is to use spectrum without interfering with the rights of primary users, allowing multiple users to use the spectrum at the same time. Thus, the goals are to avoid high-cost spectrum resetting and improving overall spectrum utilization. This technology brings the TCP/IP protocol stack's Physical, Data Link, and Network Layers up to date with reference to Cognitive radio technology. Furthermore, due to the dynamic nature of cognitive radio Networks, these can be easily physically or mentally hurt, influenced, or attacked: to a slew of new security flaws. We concentrated on various attacks that target the Physical, Medium Access Control, and Network layers in this paper. A comparison of current defenses against outside and inside attacks is also included, as is a discussion of the current detection mechanisms and their countermeasures. Moreover, the systematic review is conducted to look into various attack vectors or malicious activities that may degrade the performance of cognitive radio networks. In addition to this, various paper discusses various recent tools and techniques that can be used to diagnose potential threats on wireless ecosystem. This survey also highlights the fundamental security challenges for cognitive radio networks. [ABSTRACT FROM AUTHOR]

Cognitive radio network (CRN) is getting growing interest of the researchers due to its wide applicability for spectrum sharing with massive number of active devices in Internet of Things (IoT). In existing schemes, a large part of the spectrum may remain underutilized which was assigned to service providers as primary users (PU). Secondary user (SU) may be the alternative to use the spectrum. The main challenge is that SU keeps on sending the packets in an incremental manner until a free channel is found in real conditions. It results in excessive communication and packet loss. This paper presents an efficient tree-centric approach where a centralized base-station maintains a tree of available channels and intelligently allocate to the SUs as per the real-time availability of channels. It analyzes for the effective usage of these channels by the SUs. It also manages the authenticated SUs to avoid any interference due to the hidden node problem. A number of extensive simulations are performed using network simulator. The existing instantaneous approaches require 10 to 12 attempts to send request for acquiring free channel with an average delay of 315 ms, the Hybrid-P1 and Hybrid-P2 approaches consumed 4 requests each with a delay of 128 ms to 135 ms. Our proposed CDC mechanism achieves a channel access mostly in 1 to 2 requests with an average delay of about 72 ms. Results are extracted from the trace files that prove the dominance of proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we suggest the use of Intelligent Reflecting Surfaces (IRS) to improve the spectrum sensing process using the energy detector and the throughput of Cognitive Radio Networks (CRN). The Primary Source (PS) harvests energy using the received signals on n r antennas from node A. The harvested energy is employed to transmit data to Primary Destination PD. The transmitted signal is reflected by IRS so that all reflections have a null phase at Secondary Source SS where spectrum sensing is performed. Spectrum sensing using IRS equipped with N = 32 reflectors offers 27 dB and 30 dB gain when compared to spectrum sensing without IRS for a number of antennas n r = 2 and 3 (Alhamad and Boujemaa in Wirel Pers Commun, 2021). We also suggest the use of IRS to increase the harvested energy and improve the spectrum sensing process. We also derive the primary throughput of CRN where the PS harvests energy using received signals on multiple antennas and there is an IRS that reflects PS signals to the PD. We derive the secondary throughput when the SS harvests energy and an IRS is located between the SS and the Secondary Destination SD. [ABSTRACT FROM AUTHOR]

The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R 2 , respectively, which is better than all the baseline deep learning models. [ABSTRACT FROM AUTHOR]

One important area of study in cognitive radio ad hoc networks is multi-channel path scheduling. Cognitive radio networks have trouble communicating and using the spectrum effectively because of weather and dispersion. Optimizing multichannel path scheduling enhances network performance and reliability in a cognitive radio ad hoc net. The Optimizing Multichannel Path Scheduling (OMPS) model methodically tackles various scheduling problems. For the OMPS model, this domain is new. It effectively resolves multichannel path scheduling. The computer method used in the study is called Differential Evolution. During optimization, several factors are carefully considered, including Channel Fade Margin, Cross-Correlation and Coherence Time, Spectral Efficiency, Interference Level, Power Consumption, Retransmission Rate, Access Probability, and Propagation Delay. To increase the scheduling efficiency of the DE algorithm, many steps are meticulously planned: initialization, mutation, crossover, fitness evaluation, selection for iteration evolution, and termination. Latency, Packet Delivery Ratio (PDR), Spectrum Utilization, Interference Level, Energy Efficiency, and Established Path Success Rate are all assessed by the OMPS model. These indicators assess the effectiveness and dependability of the network. OMPS performs better in crucial simulations than the existing model. The demonstration demonstrates decreased latency for real-time applications, greater packet delivery ratios (PDRs), improved spectrum efficiency, channel interference, energy efficiency, and connection formation odds, as well as increased throughput that enhances network resource utilization. To do this, a variety of multichannel path scheduling situations are simulated. [ABSTRACT FROM AUTHOR]

On January 1, 2024, a strong earthquake occurred in Japan, causing deaths and damage to Japanese infrastructure. This study discusses the electromagnetic and solar signals that anticipated the strong earthquake. This earthquake was preceded by a series of electromagnetic signals recorded by the RDF network developed by the Radio Emissions Project in Italy and by peculiar solar activity that occurred in the days before the disastrous quake. The authors believe that there may be a relationship linking telluric activity with space weather, evidenced by electromagnetic signals detectable in the phases preceding the earthquake in a time window of about five days. The results of the experiment show that the data can be compared with other known cases in the scientific literature, in the context of a crustal diagnosis and seismic precursor candidates. [ABSTRACT FROM AUTHOR]

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 the Whale Optimization Algorithm (WOA) and Grey Wolf 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. [ABSTRACT FROM AUTHOR]

The information flows of communication systems during aircraft ground handling operations at one of the largest airports in the Russian Federation are studied to determine the minimum sufficient resource of the radio communication system as one of the communication channels. The technological schedule of aircraft maintenance is given and its example shows the complexity and parallelism of aircraft maintenance processes. Also on the basis of the technological maintenance schedule the list of necessary communication resources with description of specific works of each group and their expected load is developed. Three stages of research for modernization of communications are described. At the first stage, the available and necessary conditions for the functioning of communication systems under conditions of frequency resource shortage are established and the communication technology to be implemented is determined (in this case it referred to the construction of DMR Tier III radio network). At the second stage, statistics on the use of the radio network is accumulated, specific tasks, the work of groups, and their radio exchange is analyzed. At the third stage, based on the data obtained earlier, a conclusion is made about certain weaknesses of the system, recommendations are developed for its modification to reduce the number of customer service failures and improve the efficiency of aircraft ground handling operations, as well as for the work of emergency and security services. The study showed several key indicators, namely: the availability of communication channels, the number of communication channels to fulfil the technological service schedule, retention of the communication channel, and strict adherence to negotiation regulations.

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%. [ABSTRACT FROM AUTHOR]

Some protocols operated in the MAC layer and the open-source interconnections model to share the packet delivery and the network channel to deliver the packet is done. Transmitting two or more messages through one channel causes problems in packing and information delivery. This causes the lagging of the data to reach the destinations. So, the method of multiple hubs and the host must be created for the packet and the information delivery. This makes the fast transmission of data and messages. Method: The Tunnel monitoring system and the FANET are used for analysis in this study for the research, enabling the mobile radio networks for the examination. The FANET-based flying network system allows the paradigm for accessing human analysis. Then the drone-related data can be helped. The tunnel monitoring system navigates the information purpose and routing of the drone flying and gathering the data. Then based on human analysis, 40% of the data can be analyzed using the system's formation. Result: These results support the coverage and the tunnel monitoring process for detecting the navigation system. Conclusion: Also, some tunnel-based detection using drones is found in this study based on the wireless muometric navigation system that can be enabled using Q-learning. [ABSTRACT FROM AUTHOR]

Clustering protocols and simultaneous wireless information and power transfer (SWIPT) technology can solve the issue of imbalanced energy consumption among nodes in energy harvesting-cognitive radio sensor networks (EH-CRSNs). However, dynamic energy changes caused by EH/SWIPT and dynamic spectrum availability prevent existing clustering routing protocols from fully leveraging the advantages of EH and SWIPT. Therefore, a multi-hop uneven clustering routing protocol is proposed for EH-CRSNs utilizing SWIPT technology in this paper. Specifically, an EH-based energy state function is proposed to accurately track the dynamic energy variations in nodes. Utilizing this function, dynamic spectrum availability, neighbor count, and other information are integrated to design the criteria for selecting high-quality cluster heads (CHs) and relays, thereby facilitating effective data transfer to the sink. Intra-cluster and inter-cluster SWIPT mechanisms are incorporated to allow for the immediate energy replenishment for CHs or relays with insufficient energy while transmitting data, thereby preventing data transmission failures due to energy depletion. An energy status control mechanism is introduced to avoid the energy waste caused by excessive activation of the SWIPT mechanism. Simulation results indicate that the proposed protocol markedly improves the balance of energy consumption among nodes and enhances network surveillance capabilities when compared to existing clustering routing protocols. [ABSTRACT FROM AUTHOR]

The development of new telecommunication services requires the implementation of advanced technologies and the next generations of networks. Currently, the Long-Term Evolution (LTE) is a widely used standard. On the other hand, more and more mobile network operators (MNOs) are implementing the fifth-generation (5G) New Radio standard in their networks. It allows for increasing throughput, spectral, and energy efficiency and maximizing coverage, while reducing latency. The effectiveness of the introduced changes is measured by assessing the quality of service (QoS) in mobile networks. The paper presents the result evaluation of the QoS measurement campaign carried out using the drive test method in an urban area for four MNOs. We analyze the data transmission scenario, which is the basis of most modern telecommunications services. The result comparison provides an assessment of the 5G service implementation advancement by MNOs. In this analysis, we consider many QoS metrics (e.g., session time, throughput, and round-trip time) and parameters defining the radio signal quality (i.e., reference signal received power, signal-to-interference-plus-noise ratio). Our work also included searching for relationships between these parameters, using a correlation analysis. It allows for the selection of uncorrelated parameters to assess the quality of network, i.e., MNO evaluation, in terms of the provided QoS. [ABSTRACT FROM AUTHOR]

The Cognitive Radio (CR) is an imminent technology, intended to make more effective use of the available spectrum by giving access to licensed frequency bands by unlicensed Secondary Users (SUs) without affecting Primary licensed Users (PUs). Depending on the region where the energy is being observed, each CR communicates local decisions or the seen energy to the Fusion Center (FC). This study presents the many plots that discuss an enhanced double threshold through the Cooperative Spectrum Sensing (CSS) approach. The FC then combines local decisions with the measured energy values to reach a final decision. The usage of several machine learning methods in spectrum decision with the myopic decision is estimated. The system seeks to enhance the long-term overall performance of the SU. [ABSTRACT FROM AUTHOR]

Cognitive radio network (CRN) is a pioneering technology that was developed to improve efficiency in spectrum utilization. It provides the secondary users with the privilege to transmit on the licensed parts of the spectrum if the licensed user is not utilizing it. The cognitive radio must, however, relinquish the spectrum when the primary user decides to reoccupy it. By exploiting the unused portion of the spectrum, a cognitive radio helps in making the use of the radio spectrum more efficient. Furthermore, the most important capability that a cognitive radio (CR) must possess is spectrum sensing. A CR must be able to correctly determine the status of the target spectrum with the help of spectrum sensing. This is a very challenging task and several methods have been investigated over the years. In this work, the state of the art of different spectrum sensing techniques for a variety of CRNs is presented. Both conventional and modern spectrum sensing techniques for different types of primary user signals are discussed in this work for Narrowband and Wideband signals. Legacy techniques such as energy detection are most commonly used due to their simplicity in implementation. However, this comes at the cost of poor performance at low SNR (signal-to-noise ratio) values. This issue is countered by methods that use statistical information of the primary signal to make a more informed decision on spectrum occupancy. Several techniques that make use of the power of machine learning algorithms are also discussed which show clear improvement in performance. The primary challenge in such techniques is selection of the best features. The most commonly used features are also discussed. Furthermore, spectrum sensing techniques that consider the 5G signal as the primary user signal of the network are discussed. It is observed that there is a significant need for research in additional spectrum sensing techniques for 5G cognitive radio networks. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio network (CRN) is one form of wireless communication for solving the spectrum underutilization problem. It is mainly used for sensing and learning the electromagnetic environment and changes according to the environment. CRNs mainly depend on the cooperation functionality for making the network work efficiently. The main technology in cognitive radio is spectrum sensing. The cooperative spectrum sensing (CSS) scheme is used for estimating the high transmit power in the cognitive radio networks, and the result is that it satisfies the interference constraints. It develops the communication overhead for the local observation dissemination between the secondary users. It is used for speed and accurate detection techniques of the user, and it also identifies the spectrum holes without any interference to others while sharing with other users. It is based on the ensemble support vector machine (SVM) with CSS for producing high performance in CRNs with the use of artificial intelligence (AI) techniques. [ABSTRACT FROM AUTHOR]

Recently, it has become critical to offer adequate spectrum bands for Internet of Things (IoT) applications. The convergence of Cognitive Radio Networks (CRNs) with IoT is a significant step toward a more intelligent world. Remarkably, cognitive radio has gained a great attention recently as of being essential in dealing with wireless spectrum scarcity problems. More and over, the continuously growing demand for wireless communication induces researchers to construct secondary networks using the wasted spectrum. Furthermore, the choice of routing algorithms used in CRN is a fundamental factor since they play an integral role in the best path selection and facilitate the communication process within the network. As a result, various studies have recently concentrated on the quality of service as one of the significant routing measures used to establish the optimum paths for Cognitive Radio Ad-hoc Networks (CRAHN). Hence, this work presents a routing protocol that considers route stability. Specifically, to achieve a high throughput, we have an efficient stability function being involved in constructing the routing paths, thereby not only improving the throughput, but also minimizing the possibility of network disconnections. This work supports the approach that focuses on building a fully distributed network where the nodes communicate less than the ordinary CRAHNs by presenting a routing protocol without a common control channel. Inevitably, several simulation experiments and scenarios are operated with a distinct Java simulator to evaluate the proposed protocol's performance. The results of the study show that our proposed protocol provides a significant improvement in throughput over its counterparts. [ABSTRACT FROM AUTHOR]

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. [ABSTRACT FROM AUTHOR]

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. [ABSTRACT FROM AUTHOR]

The investigation of a decentralized radio network dedicated to unmanned aerial systems (UASs) was presented in the paper. Two frequencies (315 MHz; 434 MHz) and five different configurations of Gaussian frequency-shift keying (GFSK) were taken into account. Three different algorithms for decentralized networks were investigated and their influence on the network capacity was measured. The research was done both for static and dynamically changed unmanned aerial vehicle (UAV) positions. In order to quantify the research three different parameters were determined: RSSI, nP (number of data packets in one second), and F (frequency of data update). [ABSTRACT FROM AUTHOR]

In this paper, the impact of the bias networks on a radio frequency (RF) power amplifier (PA) is analyzed and discussed comprehensively. Both the different sizes and positions of the bias microstrip line circuit have been studied for a broadband class AB PA. Both simulations and measurements show that the performance of the PA can be greatly improved with the adjustment of the bias circuit by effecting the harmonic impedance of the whole matching circuit. Compared with the existing works, the fabricated PA based on the proposed technique provides the best performance, thereby demonstrating the effectiveness of the developed bias circuit optimization method. [ABSTRACT FROM AUTHOR]

In this article; a TDMA-based cognitive radio network is modeled and the call performance of secondary users in this network is analyzed for different primary and secondary user traffic parameters and for different numbers of time slots. In our model, primary users utilize Time Division Multiple Access technique as the media access control mechanism, differing from well-known classical approaches, and by means of an opportunistic approach, secondary users take advantage of time slots not used by primary users. In addition, in the proposed network model, it is assumed that primary users have higher priority in accessing the channel than secondary users and are not affected by secondary users' channel occupation. The performance of the cognitive radio network is analyzed in detail in terms of call-block and call-drop probabilities using a two-dimensional continuous Markov chain. In addition, the Monte-Carlo simulation of the proposed network model has been performed, and the obtained analytical results have been verified by the simulation results. According to the simulation results obtained, when the SU (secondary user) arrival rate is λ_s=0,07 and the number of time slot is N=4, the call block probability of SU is 0.0347, when the number of time slot is N=6, this value is improved by 95% to 0.00172 and when the number of time slot is N=8, value is improved by 99% to 0.00034. [ABSTRACT FROM AUTHOR]

New cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use. [ABSTRACT FROM AUTHOR]