Cognitive Radio Systems Research Articles

Robust transmission design for active IRS-aided multiuser MIMO cognitive radio systems with non-linear energy harvesting models

Robust transmission design for active IRS-aided multiuser MIMO cognitive radio systems with non-linear energy harvesting models

A Multiplier-Less FRM-Based Reconfigurable Regulated Bank of Filter for Spectrum Hole Detection in IoT

A promising solution for the detection of spectrum holes in the Internet of Things networks is the cognitive radio (CR) system, which is used to identify spectrum holes effectively. The intention of this work is to design a low-complexity Reconfigurable Regulated Bank of Filter (RRBF) structure for spectrum hole detection in IoT networks. The RRBF structure is designed by utilizing the Frequency Response Masking (FRM) approach and the Cosine Modulation Technique (CMT). Using the RRBF structure, multiple sharp non-uniform channels are generated for efficient spectrum hole detection in IoT networks. With the aid of an example, the performance and computational complexity of the RRBF structure are demonstrated. The result shows that the RRBF structure has a fewer multipliers than other existing methods. To obtain hardware-efficient realization, the RRBF structure is made of multiplier-less by incorporating Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with Common Sub-expression Elimination (CSE) optimization techniques.

COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK

Cognitive radio (CR) is an integral system in telecommunications technology that gives unlicensed users access to licensed spectrums via dynamic spectrum access (DSA), to promote spectral efficiency. A significant operation in cognitive radio system is spectrum sensing. This paper evaluates and compares two of the major non-cooperative sensing techniques (energy and cyclostationary feature detector (CFD)) in order, to determine which gives better performance. Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels.

Математическая модель функционирования когнитивной радиосистемы

The functioning of a promising cognitive radio system in the context of an electronic conflict is considered. As a result of the analysis, the correspondence of the required intellectual abilities of the cognitive radio system, the tasks to be solved and the intelligent algorithms proposed by the researchers for the realization of these abilities was revealed. The aim of the work is to develop a mathematical model that allows us to describe the adaptation of a conflict–resistant cognitive radio system and assess the complexity of the corresponding algorithms. Two groups of methods were used: general scientific methods – abstraction, generalization, analysis, synthesis, as well as special methods of graph theory, criteria importance theory, algorithm theory and set theory. Two new mathematical models are presented. The first model describes the state of the subscriber of the radio system from a combinatorial point of view, the second describes the functioning of the cognitive radio system in conditions of electronic conflict. The latter model is formalized using graph theory – an r-weighted multigraph is constructed, the vertices of which are identified with the states of the subscribers of the radio system, and the corresponding weight vectors are assigned to the edges. The elements of each vector of weights qualitatively characterize the functioning of the cognitive radio system according to the selected indicators. An example is given with indicators characterizing the functioning of the radio system: stealth, noise immunity, energy efficiency and information transfer rate. The graph model allows you to generalize the description of various methods for obtaining knowledge about the environment and managing radio system resources, and also allows you to evaluate the computational complexity of adaptation algorithms. The graph model also makes it possible to describe the intellectual abilities of a radio system both from the standpoint of a production approach and from the standpoint of reinforcement learning. The given estimates of the computational complexity of some tasks allow us to divide the tasks into two groups: those solved by subscribers and the control subsystem. The consistency of proposals for the separation of computational tasks with the principles of transfer learning is shown.

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

Reconfigurable graphene antenna for a network cognitive radio: A novel solution for X-band satellite communications

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.

A Rule-Based Approach with Fuzzy Logic for Adaptive Network Optimization

This study introduces an adaptive network optimization framework grounded in fuzzy logic and rule-based machine learning for cognitive radio systems. The primary objective is the concurrent minimization of interference, congestion, and bit error rate, coupled with enhancements in throughput and signal-to-noise ratio. A comprehensive set of rules, guided by linguistic variables for qualitative network aspects, is established alongside membership functions for quantitative analysis.The incorporation of machine learning into our approach enables adaptability to diverse network conditions, resulting in overall improved efficiency. The positive impact of machine learning is particularly evident in the reduction of congestion during specific timeframes. Specifically, on Saturday at hour 6, congestion decreases from the conventional 3.055 to 2.86. This notable improvement underscores the efficacy of machine learning in expediting the sensing mechanism of secondary users, facilitating the rapid identification of unused channels from primary users. The findings contribute to the advancement of cognitive radio systems, providing a robust and adaptable solution to address the intricate dynamics of modern wireless networks.

Dynamic Node Management for Energy Optimization in Cognitive Radio Systems

Energy efficiency and spectrum sensing performance are two crucial parameters in cognitive radio networks. Enhancing energy efficiency should not come at the cost of sacrificing spectrum sensing performance. This paper introduces a novel sensing node grouping algorithm to address the challenge of balancing energy efficiency and sensing performance. The proposed algorithm first prioritizes spectrum sensing training for each node and estimates their reliability. Nodes with reliability values exceeding a predefined threshold are selected for cooperative spectrum sensing. The selected nodes are divided into two groups using a staggered grouping approach, ensuring equivalent sensing performance between the groups. The groups alternate in performing spectrum sensing operations, with one group engaging in sensing while the other participates in data transmission or remains idle. The optimal number of cooperative nodes is determined through a greedy algorithm, formulated as a constrained optimization problem. The objective is to maximize energy efficiency subject to a constraint on the error probability, ensuring the highest possible energy efficiency while satisfying the required sensing performance criteria. Experimental results demonstrate that the proposed algorithm effectively maximizes energy efficiency while fulfilling the sensing performance requirements. Compared to traditional algorithms, the dynamic grouping algorithm significantly enhances spectrum sensing performance and improves energy efficiency. The algorithm is adaptable to various network configurations and environmental conditions, addressing the challenges faced by existing solutions. This research contributes to the advancement of cognitive radio networks and their applications in various domains, offering a comprehensive and adaptive solution to optimize energy efficiency and spectrum sensing performance.

Outage Analysis of a Cognitive Radio System with Nakagami-m Fading and Cooperative Decode and Forward Relaying

Outage Analysis of a Cognitive Radio System with Nakagami-m Fading and Cooperative Decode and Forward Relaying

Multi-ARIS Backscatter Enabled Downlink NOMA Communication for Cognitive Radio Systems

Multi-ARIS Backscatter Enabled Downlink NOMA Communication for Cognitive Radio Systems

Robust Beamforming Design for RIS-Assisted Cognitive Radio Systems with Hardware Impairments

Robust Beamforming Design for RIS-Assisted Cognitive Radio Systems with Hardware Impairments

Physical layer security‐oriented energy‐efficient resource allocation and trajectory design for UAV jammer over 5G millimeter wave cognitive relay system

AbstractThis work focuses on the optimal trajectory of a rotary‐wing unmanned aerial vehicle (UAV) jammer and the system power allocation with the objective of maximizing the total secure energy efficiency (EE) of a slotted relay‐assisted millimeter wave (mm‐Wave) cognitive radio system. The UAV flies from starting location to final one in a round of communication mission that is overheard by a malicious eavesdropper. The network terminals are equipped with multiple‐input multiple‐output (MIMO) antenna array. This work first develops a practical approximation propagation model by addressing three key challenges: path loss, small‐scale Nakagami‐m fading channel gains, and transceiver antenna beamforming gains. By using the approximate model, the upper and lower bounds of the ergodic rate of each communication link are derived. Second, with the objective of maximizing secure EE, the optimal UAV trajectory and system resource allocation framework is established by considering the constraints of interference at primary receiver (PR), information‐causality relationship, rotary‐wing UAV propulsion energy and so forth. The optimal framework is solved with a tractable CVX tool by using the derived upper and lower bounds and successive convex approximation. Finally, an iterative optimizing algorithm is established by using Dinkelbach's method. In the simulation, the work presents the comparison of UAV trajectories, secure rates, and secure EEs of three schemes, that is, the proposed EE scheme, the non‐EE scheme where only the optimal secure rate is considered, and the EE scheme with the consideration of all communication‐related energy consumption. The simulations show that the EE schemes achieve smoother UAV trajectory than non‐EE, but the non‐EE scheme has higher secure rate. The impact of communication energy consumption is negligible. In addition, the effect of MIMO antenna array and mm‐Wave on system performance is exploited.

Evaluation of energy detection based spectrum sensing in cognitive radio networks

Spectrum sensing is the process of identifying the free (unused) spectrum of the main user without making any interference or upsetting the rights of licence users. This procedure takes place without creating any interference and without disturbing the rights of licence users. It means identifying gaps in the spectrum that are not being utilised. The most effective method for locating gaps in spectrum use is to search for key users. The development of an energy detector and a detector based on cyclostationarity for an OFDM-based cognitive radio system, as well as the implementation of these detectors and an evaluation of their performance, are the primary focuses of this project. The idea behind cognitive radio is to make the most of the underutilized spectral resources by utilising unused spectrums in an opportunistic way. This is accomplished via the notion of cognitive radio. Main users of the spectrum are typically incumbent licensees, while secondary users of the spectrum are users who wish to make opportunistic use of the spectrum when main users are not actively using it. This is an example of a cognitive radio system. The cognitive radios are required to do spectrum sensing to determine whether or not the spectrum is accessible. Both the energy detector and the cyclostationary detector are derived, then we implement them, and then we assess them. Plotting the Receiver Operating Characteristics (ROC) in clean and noisy situations allows us to assess the performance of the detectors.

High-resolution photonic-assisted microwave frequency identification based on an ultrahigh-Q hybrid optical filter

Photonic-assisted microwave frequency identification has been extensively studied in civil and defense applications due to its distinct features including wide frequency coverage, large instantaneous bandwidth, high frequency resolution, and immunity to electromagnetic interference. In this paper, we propose and experimentally demonstrate an approach for high-resolution frequency identification of wideband microwave signals by linearly mapping the microwave frequencies to the time delays of the optical pulses. In the proposed system, an ultrahigh-Q hybrid optical filter is a key component, which consists of a fiber ring resonator (FRR) and a silicon photonic racetrack micro-ring resonator (MRR). The FRR has an ultra-narrow bandwidth of 7.6 MHz and a small free spectral range (FSR) of 292.5 MHz, while the MRR has a bandwidth of 167.5 MHz and a large FSR of 73.8 GHz. By precisely matching the resonance wavelengths of the FRR and the MRR, a hybrid optical filter with an ultrahigh Q-factor and a large FSR is realized, which is much preferred to realizing a high resolution and a wide measurement range for microwave frequency identification. An experiment is performed and different types of microwave signals are identified. A frequency measurement range as broad as 33 GHz from 2 to 35 GHz, a frequency resolution as high as 15 MHz and a measurement accuracy as high as 5.6 MHz are experimentally demonstrated. The proposed frequency identification system holds great advantages including high frequency resolution, high measurement accuracy, and wide frequency coverage, which can find extensive applications in next-generation electronic warfare and cognitive radio systems.

A Unified Rate-Splitting Framework for Secure Spectrum Sharing via Joint Precoding Optimization

To handle the security problem of cognitive radio (CR) systems, secondary users (SUs) are able to be designed to assist the primary user (PU) in secure transmission. To realize the spectrum sharing among CR users, we adopt the rate-splitting multiple access (RSMA) properly, with nonorthogonal multiple access (NOMA) and space division multiple access (SDMA) as its special cases. In this article, we set up a unified rate-splitting framework to guarantee the safe spectrum sharing through multiple multiaccess strategies, without the knowledge of wiretap channels. The precoding vectors at the transmitter are conjointly designed to boost the transmission of common stream, which is from both PU and SU. Besides, the private stream of the downlink CR system can be well secreted in the high-power common signal. As for NOMA and SDMA, when the message of SU is regarded as the friendly jamming signal, the security transmission requirement of PU can be similarly guaranteed via precoding design. Simulations validate that the rate-splitting framework we established can be effective in ensuring the spectrum sharing security for the secure users through RSMA, and the security for NOMA and SDMA is also able to be promoted through adjusting our designed framework.

Covert Communication in Two-Hop Cooperative Cognitive Radio System

Covert Communication in Two-Hop Cooperative Cognitive Radio System

Modified Threshold-based Spectrum Sensing Approach for VANETs

The Primary User (PU) signal detection in Cognitive Radio (CR) is crucial and is achieved through spectrum sensing techniques. The Energy Detection method is a commonly used technique, and selecting a proper threshold is essential to enhance the efficiency of the CR system. This research paper demonstrates the maximum achievable throughput and validates a Modified Threshold (MT) approach. The authors consider a scenario with multiple antennas at the receiver, where these antennas are correlated and subjected to mobility effects, and they employ the Energy Detection (ED) for spectrum sensing. The study analyzes the system's performance over a Nakagami-m fading channel, considering available correlations among the antenna elements. To compute important statistical values, the Moment Generating Function (MGF) method is employed. The research employs specialized mathematical functions, such as the Lauricella and confluent hypergeometric functions, to derive closed-form expressions for the Probability of Detection when employing the diversity technique. The results indicate a significant enhancement in the performance of the proposed algorithm when utilizing the modified threshold parameter across a wide range of Signal to Noise Ratio (SNR) values. Additionally, increasing the number of branches in the antenna system further improves detection performance. Interestingly, under high fading parameter conditions (m=4), the detection probability is found to be superior with exponential correlation among the L antenna elements compared to other available correlated branches.

Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses

The paradigm known as Cognitive Radio (CR) proposes a continuous sensing of the electromagnetic spectrum in order to dynamically modify transmission parameters, making intelligent use of the environment by taking advantage of different techniques such as Neural Networks. This paradigm is becoming especially relevant due to the congestion in the spectrum produced by increasing numbers of IoT (Internet of Things) devices. Nowadays, many different Software-Defined Radio (SDR) platforms provide tools to implement CR systems in a teaching laboratory environment. Within the framework of a ‘Communication Systems’ course, this paper presents a methodology for learning the fundamentals of radio transmitters and receivers in combination with Convolutional Neural Networks (CNNs).