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Summary: Information technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT Automatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs. [ABSTRACT FROM AUTHOR]

The world is progressing at a rapid rate, and with continuous advancements in technology, the need for cooperative spectrum sensing has evolved. Cooperative spectrum sensing is an approach used to enhance detection performance, wherein secondary users collaborate with each other to sense the spectrum and identify spectrum holes. As technology improves over time, the spectrum becomes increasingly allocated to primary users. The role of cooperative spectrum sensing in cognitive radio fulfills the essential requirement of protecting primary users from harmful interference. The progressive evolution of technology has led to a reduction in available spectrum, prompting the emergence of the concepts of cognitive radio and cooperative spectrum sensing. To begin with, this paper introduces the fundamental application of cooperative spectrum sensing algorithms, encompassing primary and secondary users' models and an energy model. Subsequently, three machine learning algorithms, namely K-means, support vector machine, and logistic regression, are explained, and their schematics are presented in detail. The proposed model uses supervised and unsupervised learning techniques to develop a cooperative spectrum sensing framework. It compares the performance of three machine learning algorithms, K-means clustering, logistics regression and support vector machine. The comparison is based on accuracy, recall and precision metrics, and the results show that the K-means clustering algorithm has better performance than the other two algorithms. The findings highlight the superiority of the K-means clustering algorithm over logistic regression and support vector machine. [ABSTRACT FROM AUTHOR]

ABSTRACT The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS). [ABSTRACT FROM AUTHOR]

On October 9, 2022, orbiting X-ray and γ-ray telescopes registered the most powerful explosion ever observed in the Universe—the GRB221009A γ-ray burst at a distance of 2.4 billion light years. The response of the Earth's ionosphere to this unique event was detected in the received signals of very-low-frequency (VLF) radio paths passing through both the daytime and nighttime ionosphere. A disturbance in the night sector was observed as a sudden decrease in amplitude up to 7 dB and a sharp jump in the signal phase up to 20°‒30°. Less pronounced amplitude jumps up to 1.5 dB were found in the daytime sector. At the time of the flare, magnetometers showed only the appearance of a weak burst of the geomagnetic field ∼0.5 nT at the time of the flare. In the daytime, an isolated geomagnetic pulse with an amplitude of up to 1 nT was observed. The appearance of a geomagnetic response to a γ-ray flare is surprising, because its radiation creates additional ionization in the stratosphere, significantly below the conducting E-layer of the ionosphere. The recorded event showed that extragalactic γ-ray bursts cause a noticeable perturbation in the Earth's ionosphere, and the network of VLF radio paths covering the lower ionosphere can be considered as a giant γ-ray detector. [ABSTRACT FROM AUTHOR]

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications. [ABSTRACT FROM AUTHOR]

The growing demand for wireless applications has led to an increasing need for efficient spectrum utilization. Cognitive radio networks (CRNs) emerge as a promising solution to address this demand. CRNs enable unlicensed users to access the available spectrum without causing interference to primary users. However, ensuring the accurate identification of primary user emulation (PUE) attackers is critical due to the sensitivity of these networks. In this paper, an active ensemble neural network-based approach with FlightSpa optimization is employed to enhance the detection of aggressive users. Leveraging neural network (NN) classifiers, the proposed method implicitly captures complex nonlinear relationships between dependent and independent variables, allowing for a comprehensive analysis of network nodes and precise identification of malicious users. Given the network’s inherent complexity, accurately identifying malevolent users is a challenging task, which the active neural network effectively addresses. The classifier’s parameters are tuned optimally by applying a hybrid FlightSpa optimization strategy, leading to successful optimization and reliable output generation. The performance reveals that the active NN-based FlightSpa optimization achieves impressive results, with detection rates for malicious users in CRNs reaching the highest values of 84.085% for accuracy, 82.351% for sensitivity and 85.820% for specificity with 50 nodes. These outcomes surpass those obtained using earlier techniques, marking a significant advancement in the field of cognitive radio network security. [ABSTRACT FROM AUTHOR]

The basic requirement for cognitive radio networks is that secondary users sense spectrum to detect whether primary users have arrived the system. However, imperfect sensing always occurs due to the interference of unreliable channel and signal, which can negatively affect the performance of both primary users and secondary users. To better study the practical problems of cognitive radio networks, we combine queueing theory with it. In this paper, we consider cognitive radio networks with imperfect sensing based on retrial queues with feedback and priority. Using the method of probability generating function, the performance measures are solved. A bi-objective optimization model is established to minimize both system cost and users' sojourn time. From the perspective of secondary users, we analyze both individual equilibrium strategy and socially optimal strategy. Meanwhile, in order to eliminate the differences between two strategies, we impose admission fees on secondary users and obtain the optimal pricing strategy. [ABSTRACT FROM AUTHOR]

Cognitive radio networks offer an alternative to low spectral availability in some frequency bands due to their high demand for frequency channels. This article proposes to improve the spectral assignment based on the combinative distance-based assessment multicriteria algorithm. The metrics obtained are compared with a simple additive weighting algorithm and a RANDOM selection. To establish the algorithm's performance, five quality-of-service metrics are used: number of handoffs, number of failed handoffs, average bandwidth, average throughput, and cumulative average delay. From the analysis of the results obtained, combinative distance-based assessment (CODAS) presented the best result for the cost metrics with the lowest levels, and for the benefit metrics, the highest levels were obtained. [ABSTRACT FROM AUTHOR]

For a multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, an opportunistic interference alignment (IA) technique has been proposed that allows spectrum sharing between primary users (PUs) and secondary users (SUs) while ensuring zero interference to the PU. The CR system consists of one PU and K SUs where the PU uses space-time water-filling (ST-WF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. The SUs make use of an optimal power allocation algorithm to align their transmitted signals in such a way their interference impairs only the PUs unused eigenmodes. Since the SUs optimal power allocation algorithm turns out to be an optimal beamformer with multiple eigen-beams, this work initially proposes combining the diversity gain property of space-time block codes, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. This proposed solution requires availability of channel state information (CSI), and to eliminate the need for CSI, this work then combines Differential Space-Time Block Coding (DSTBC) scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates. Simulation results confirm the accuracy of the proposed solution. [ABSTRACT FROM AUTHOR]

Higher energy consumption and poor spectrum sharing were identified as the common problems in spectrum sharing at cognitive radio networks. So to overcome these issues in the present research, a novel Horse herd-based Elman spectrum sharing model is developed. The current study found lower energy consumption and a higher spectrum sharing rate in less time. Subsequently, a channel was created for sharing the spectrum over the primary and secondary users. After that, through the proposed model, the sharing system is monitored. Moreover, the spectrum is transmitted until the false alarm is detected. The system stops the users' spectrum sharing function if a false alarm is detected. However, the spectrum is shared with every user in the designed channel. Then a case study is developed to explain the working process of the model. After that, the performance of the proposed design is detailed, and in the comparison analysis, the performance of the proposed model is compared with the existing models. Consequently, the performance of the proposed model is validated based on the throughput, outage probability, bit error rate, energy consumption, data transmission, and spectral efficiency. [ABSTRACT FROM AUTHOR]

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models. [ABSTRACT FROM AUTHOR]

Summary By transmitting concurrently signals of different users, non‐orthogonal multiple access (NOMA) meliorates spectral efficiency. Moreover, intelligent reflective surface (IRS) is introduced as a bridge between a NOMA transmitter and a distant NOMA receiver. This role enhances spectral efficiency and communication reliability. Furthermore, energy scavenging underlay networks (ESUNs) enable unlicensed transmitters to operate using scavenged energy on licensed spectrum allotted to licensed transmitters, enhancing both spectral and energy efficiencies. Although IRS‐assisted NOMA further meliorates spectral efficiency and communication reliability for traditional ESUNs, it faces challenges like fading severity and nonlinear energy scavenging (nlES), which directly impact scavenged energy and communication reliability. Thence, the paper assesses three pivotal performance metrics—energy efficiency, outage probability, and throughput—for IRS‐assisted NOMA in ESUNs, shortly RaNOehU, considering realistic factors such as relatively‐general Nakagami‐m fading and nlES. Demonstrative results reveal that the quantity of reflective elements, fading severity, and nlES dramatically influence the performance of RaNOehU. Notably, appropriate selection of power/time division parameters and desired data rate can hinder complete outage and attain optimum performance. In addition, the proposed IRS‐assisted NOMA is shown to outperform its reference, which is IRS‐assisted orthogonal multiple access, emphasizing the advantages of NOMA. [ABSTRACT FROM AUTHOR]

The proposed study introduces a novel anti-jamming approach for cognitive radio networks (CRNs) by integrating the Stackelberg game model with direct sequence spread spectrum (DSSS) techniques. This innovative combination enhances the security and performance of CRNs by optimizing resource allocation and fortifying network resilience against jamming attacks. The Stackelberg game model provides a strategic framework where the Defender and Adversary dynamically adjust their strategies to achieve Nash equilibrium, ensuring strategic stability. The application of DSSS further improves signal robustness, mitigating interference from jamming attempts. Simulation results demonstrate significant improvements in network security, resource utilization, and overall performance, validating the efficacy and advantages of the proposed scheme in maintaining reliable communication in the presence of adversarial threats. [ABSTRACT FROM AUTHOR]

Summary Due to the rise in information rate prerequisite and the heterogeneity level, the modification in network traffic in the upcoming wireless communication (WC) encompasses innovative challenges in the case of energy efficiency (EE) and spectrum management. To tackle this issue, several existing techniques have been imposed but none of the frameworks provided effective solutions to compatible with recent WC applications. This framework introduces an innovative deep learning (DL)–based distributed cognitive radio network (DCRN). The proposed scheme emphasizes single base station (BS) management, where resource effectiveness is obtained by solving active resource allocation (RA) problems using a bipartite matching (BM) technique. A DL scheme is emphasized to predict the traffic load (TL) for effective EE using a residual inception‐enriched recurrent convolutional neural network (R‐InceptionRCNN). The proposed method is implemented in Python, and the performance metrics including uplink (UL) achievable capacity per secondary user (SU), UL achievable capacity per SU, cost of energy consumption, EE, and mean energy saving (MES) are scrutinized and compared with conventional techniques. The proposed scheme achieved the overall costs, EE, MES, and UL capacity of 14.33 C/J, 149.99 J/MB, 13.49%, and 22.33 Mbps, respectively, on performing RA and TL prediction in the CRN platform. [ABSTRACT FROM AUTHOR]

The cognitive radio network (CRN) is important for efficiently allocating spectrum. In general, uncertainty in channels and interference decreases the system's efficiency. An energy-efficient spectrum-sharing optimization framework in the cognitive radio network based on the GarraRufa Fish Optimization algorithm and Binary Jaya algorithm (EESS-CRN–GRFOA-BJA) is proposed in thispaper to address the load utilization problem. Firstly, spectrum sharing contains the sensing model that increases its efficiency and the quality of service. The spectrum-scheduling approach is employed for sensing the obtainable spectrum and also scheduling the load to free up the spectrum. Then, the GarraRufa fish optimization algorithm (GRFOA) is used for implementing spectrum sensing and detecting the obtainable spectrum. Here, GRFOA method, along with signal-to-interference-plus-noise ratio (SINR), interference, and capacity constraint efficiently senses the spectrum. Then the Binary Jaya algorithm (BJA) is implemented for scheduling loads to free up spectrum and is used to perform the bandwidth scheduling. The metrics, such as probability detection vs signal to noise ratio (SNR), throughput, energy efficiency, and delay are analyzed. The efficacy of the proposed methods is evaluated and attains 12.35%, 19.54%, and 22.38% higher SNR, 20.45%, 17.12%, and 26.31% higher throughput, and 15.36%, 19.15%, and 21.49% higher energy efficiency than the existing models, such as multi-objective optimization for spectrum and energy efficiency tradeoff in IRS-assisted CRNs withNOMA (EESS-CRN-MOO), Gannet optimization algorithm-enabled framework for spectrum sensing (SS) in OFDM-based cognitive radio network (EESS-CRN-GOA), and Support Vector Machine-Red Deer Optimization algorithm for maximizing spectrum sensing energy efficiency in CRN (EESS-CRN-SVM-RDOA) respectively. [ABSTRACT FROM AUTHOR]

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time–frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time–frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness. [ABSTRACT FROM AUTHOR]

Symbiotic Radio Networks (SRNs) have emerged as an important communication protocol to solve the increasing energy demand and spectrum resource shortage. However, the low bit rates of the devices working in SRNs during backscatter communication, where the surrounding radio frequency resources are used by subsystems different from the main system, make SRNs very vulnerable to external attacks such as eavesdropping and jamming. To solve this problem, the Physical Layer Security (PLS) for SRNs with Signal Emitter (SE), user, jammer, receiver and eavesdropper (ED) is analyzed. While the SE conveys its information to the receiver, the user assists the SE in part of the time period and transmits its information to the receiver in the other part. While ED is overhearing SE and user's information over the wiretap channel, the jammer is trying to prevent ED with the signal it emits. This model, in which the secrecy rate is maximized over time parameters, is the first approach in which PLS analysis is carried out in the presence of a cooperative jammer when the perfect/imperfect Successive Interference Cancellation (SIC) technique is used at the receiver. Numerical results show that the existence of a symbiotic relationship between the user and the SE increases the secrecy rate of the system compared to the non-symbiotic situation. Moreover, adopting the perfect SIC technique at the receiver without energy constraint at the user resulted in a significant increase in PLS performance compared to the imperfect SIC under energy constraint. [ABSTRACT FROM AUTHOR]

In this paper, the joint radio resource management issues in a cognitive radio network driven by radio frequency energy harvesting (CRN-RF-EH) functionalities are investigated. For the CRN-RF-EH, the cognitive radio (CR) node first harvests its required energy directly from the transmitter of spectrum licensed user for its data communication and consequently transmits its data on the licensed frequency of the legacy user using the underlay accessing technique. Thus, RF-EH is an exciting innovation for energizing low-powered next-generation wireless networks (NGWNs). Consequently, due to CRN-RF-EH'd low power limitations, the resource allocation for CRN-RF-EH has to be optimized considering the trade-off among spectral efficiency, energy efficiency, and RF energy supply. Equal allocation of transmission time and/or transmission power may not be efficient for CRN-RF-EH with limited transmission time and power resources. A joint optimal time and power allocation (OTPA) strategy for CRN-RF-EH is proposed to maximise the total spectral efficiency of the CRN-RF-EH. The coupled variables in the formulated joint resource allocation problems create a non-convex optimization problem formulation. For analytical tractability, the non-convex optimization formulation is initially converted to its equivalent standard convex optimization formulation using proper variables and next, it is then solved using the convex optimization technique. The CONOPT solver, a powerful optimization-solving tool for solving convex optimization problems, is utilized to resolve the equivalent standard convex optimization problem formulation. When compared with the baseline biased random time optimum power allocation (BRTOPA) scheme, numerical simulation results show that the OTPA strategy dramatically improves the total spectral efficiency performance. In a severe radio propagation environment with a path loss exponent (PLE) equal to 3.5 such as in urban areas and less severe radio propagation environment with a path loss exponent (PLE) equal to 2.0, such as in rural areas, the OTPA outperformed the BROTPA with a mean performance improvement of approximately 23.96% and 42.94%, respectively. [ABSTRACT FROM AUTHOR]

The performance of the cognitive non-orthogonal multiple access (NOMA) systems with random locations is examined in the present paper. More precisely, the positions of the secondary receivers (SRs) are randomly distributed inside a disk with the centre at the secondary transmitter (ST). The primary networks (PNs), on the other hand, are not necessarily inside the transmission range of the ST. Two user selection schemes namely, path-loss-based and channel-gain-based approaches, are adopted to serve two SRs according to the power domain NOMA technique. Additionally, the impact of the imperfect hardware impairments (HIs) on both transmitters and receivers is also taken into consideration. Furthermore, a recently proposed transmit power allocation at the ST is employed to concurrently maximize the system performance of the secondary networks (SNs) and to strictly protect the quality-of-service (QoS) of the PNs. The correctness of the developed mathematical frameworks is corroborated by simulation results based on the Monte Carlo method. Finally, we unveil that there is no scheme that always outperforms another. Thus, an adaptive scheme can be proposed to optimize the networks' performance. Our findings also reveal that the outage probability (OP) under the harmful effect of HI can be effectively mitigated by increasing the transmit power of the ST. Additionally, increasing the transmit power of the PT is also beneficial for the SNs with the adopted transmit power allocation at the ST. [ABSTRACT FROM AUTHOR]

Spectrum sensing is pivotal in cognitive radio (CR), a burgeoning technology for optimizing radio spectrum utilization. Traditional spectrum sensing techniques like energy detection, matching filter, and cyclic stationary detection have been proposed, which rely on prior knowledge and models. These techniques suffer from challenging issues such as missed detection and false alarms, which impede the effective utilization of the spectrum. Inaccurate assumptions or limited knowledge can hinder detection. To tackle these challenging issues, we propose a novel deep learning-oriented spectrum sensing (DLoSS) technique and highlight the use of deep neural networks (DNNs) for cooperative spectrum sensing (CSS) model. Specifically, we propose a “DLSpectSenNet,” a DLoSS-based model, utilizes structural information from incoming modulated signals for spectrum sensing. Particularly, we combine convolutional neural network (CNN) and long-short-term memory (LSTM) network in series, extracting hidden spatial information and temporal data, respectively. The simulation results using the RadioML2016.10b dataset, show the proposed DLSpectSenNet’s improved detection performance, especially under low SNR conditions, surpassing traditional cooperative algorithms. It outperforms previous models, enabling improved spectrum detection. [ABSTRACT FROM AUTHOR]

Due to the deficiency of radio spectrum resources caused by the progress in technology, cognitive radio networks (CRNs) have made significant progress. CRNs have two types of users, namely, primary users (PUs) and secondary users (SUs). Considering that PUs have a higher priority and diversified data transmission requirements, this study divides PUs into two levels, namely, PU1s with a higher priority and PU2s with a lower priority. On the other hand, the occurrence of failures is inevitable in CRNs, which affects the data transmission of users. In this paper, combined with an adjustable PU packets transmission rate mechanism, a communication failure and repair mechanism with classified PUs based on the single-channel CRNs is proposed, and different preemption principles are set according to different system states. A queueing model is established and analyzed with a Markov chain, the performance index expressions that need targeted research are listed, numerical experiments are conducted, and the system performance change trends are obtained. The comparison experiment shows that the proposed communication failure and repair mechanism with classified PUs can improve the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared with the conventional communication failure and repair mechanisms with unclassified PUs. [ABSTRACT FROM AUTHOR]

A minimal interrupted communication link set-up is the primary objective of the medium access control (MAC) layer. The MAC layer is responsible for accessing the communication channel. At MAC, the control information is shared through the control channel to select a collision-free data channel. Therefore, the sharing of control information prior to data channel selection plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Random data channel selection is used in conventional CR scenarios. The random selection lacks in performance due to the unpredictability of primary user (PU) appearances in the data channels. This unpredictability in PU appearances will increase the number of handoffs in the scenario and sometimes may cause communication link failure. These adverse effects can be avoided if a common data channel selection scheme is implemented. The common data channel selection scheme selects a channel that has fewer chances of PU appearance. In the reported research paper, the work has been carried out in two parts. In the first part, a primary user free channel list (PCL) is formed at each node. Each node shares its PCL with all neighbour nodes. The sharing of PCL helps in implementing a common data channel selection scheme among nodes. The selected common channel is used for data transmission. The conventional OSI layered structure is used to observe the performances. In the second part, a cross-layer framework is proposed to improve the performance. The cross-layering has been done between the network and MAC layer. The proposed cross-layer framework helps in sharing PCL in an efficient manner. The need for an extra beacon to transfer PCL is avoided in the proposed cross-layer design. The performance of control overhead and average delay becomes better. The simulation results show that the proposed cross-layer design approach performs satisfactorily in terms of packet delivery ratio, average throughput, average delay, average energy consumption and control overhead. The observed results are also tested in the worst scenarios. The proposed scheme has applications in small throughput scenarios like cognitive radio aided sensor networks. [ABSTRACT FROM AUTHOR]

Summary Perfect detection of unused/underutilized frequency band of the primary user (PU) in the cognitive radio communication systems is an essential component for its efficient utilization by the secondary/cognitive user (CU) without any interference. In the cooperative spectrum sensing scenario, the presence or absence of PU is determined by analyzing the decisions of each CU engaged in the collaboration. In this paper, we have emphasized on the significance of spectrum sensing duration in the optimal threshold selection approach in the cooperative spectrum sensing scenarios for a range of the received PU signal signal‐to‐noise ratio (SNR) from low to high values. The sensing duration parameter is taken as ts = 2.5 ms in the higher SNR environment, whereas in the lower SNR environment when the optimal threshold condition fails, ts is kept dynamic to satisfy minimum target values of sensing performance parameters. We have computed the spectrum sensing performance parameters and throughput using the same approach and compared the results with those obtained from the constant false‐alarm rate (CFAR), constant detection rate (CDR), and minimized error probability (MEP) threshold selection approaches. The spectrum sensing performance computed by the proposed approach is significantly improved as compared with the other reported approaches. At SNR = −24 dB, global probability of false alarm Qf = 0.002 and global probability of detection Qd = 0.999 are achieved. However, the throughput is relatively less for low SNR values. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks. [ABSTRACT FROM AUTHOR]

Summary: Effective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented. [ABSTRACT FROM AUTHOR]

In cognitive radio network (CRN), effective spectrum management provides better quality of service. The spectrum is limited but the significance of the spectrum is increasing at each network generation. Due to the ineffective spectrum allocation policies, several researches have indicated that a vast segment of the licensed radio is not viably used. A CRN is an intelligent spectrum utilization innovation that provides better spectrum interface. Spectrum sensing detects unused spectrum in the manner that protects interferences to the authorized users. In principle, the secondary user (SU) receives the primary user (PU) signal and reports it to the fusion center for decision or spectrum allocation. The SU cooperates in the detection of the presence or absence of the PU. This type of spectrum sensing is called cooperative spectrum sensing. However, the significance of this type of spectrum sensing is blurred by the security problems. Malicious users can deliberately report misleading information regarding the presence of the PU. Hence, in this paper, a support vector machine learning algorithm is proposed to statistically learn the behavior of the malicious users and it classifies the legitimate SU and malicious users. A particle swarm optimization algorithm is also integrated to learn the smallest possible distinguishable malicious users' energy report deviation from the legitimate SUs. The probability of detection and energy of detection have been applied to evaluate the contribution of the proposed method. Finally, the simulation results have confirmed that better spectrum management can be derived from the proposed statistical approach. [ABSTRACT FROM AUTHOR]

• Low latitude D-region ionospheric disturbances observed by a multi-station VLF network during the December 2019 solar eclipse. • Short propagation paths are used to formulate the relationship between solar obscuration and reduction of D-region electron density. • Based on the observations and calculation, spatial distributions of D-region electron density over the Indian sub-continent are presented. We present the low-latitude D-region (60–90 km) ionospheric disturbances due to the December 2019 annular solar eclipse observed using the multi-station Very Low Frequency (VLF) radio network in West Bengal, India. VLF signals from communication transmitters were received from ten places using low-cost VLF radio receivers. The receivers were capable of recording the amplitudes of VLF signals mainly from the transmitters VTX, India at 18.2 kHz and NWC, Australia at 19.8 kHz. During the solar eclipse, the VTX signal experienced considerable positive, negative, and mixed-type amplitude variations depending on the propagation lengths between the transmitter and receivers, whereas the NWC signal displayed a relatively small amplitude response. We have also found that the highest VLF signal disturbances in each propagation path occurred after the time when the solar obscuration was maximum over the entire path. We used the observations of the VTX signal to formulate the relationship between solar obscuration and D-region electron density reduction during the solar eclipse. On the basis of this, we presented the electron density distribution of the D-region ionosphere during the solar eclipse across the Indian subcontinent. This study shows a useful method for integrating VLF observations and simulations to estimate the D-region electron density distribution over a vast area during a solar eclipse. [ABSTRACT FROM AUTHOR]

The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs). [ABSTRACT FROM AUTHOR]

In cognitive radio networks, channel assembling allows secondary users (SUs) to expand network capacity and improve spectrum utilization. Scheduling strategies only based on heterogeneous service classification cannot guarantee the delivery priority of vital elastic services in special scenarios such as emergency rescue. Therefore, a priority scheduling strategy with reserved queue (Ps-rq) is proposed in this work. A static factor is defined to classify SUs into elastic services and real-time services based on message type, while a dynamic factor is defined to differentiate high-priority elastic services based on information validity, message correlation and message size. The high-priority users in the interrupted elastic services are placed in the reserved queue to ensure its services. Accordingly, the scheduling algorithm and the dynamic channel access process is presented. A continuous-time Markov chain analysis is conducted and all possible transition states, trigger events, transition rates and transition conditions of the system starting from a general state are derived. Furthermore, evaluation indexes of system performance are obtained. Study cases and simulation results prove that the proposed strategy can enhance network capacity, reduce blocking probability and forced termination probability for secondary users, and notably enhance the performance of high-priority elastic services. In addition, we analyze the characteristics of Ps-rq through a comprehensive comparison with four other schemes. The experiment proves that the Ps-rq strategy can effectively improve the service quality of the vital elastic services on the basis of providing fair scheduling. [ABSTRACT FROM AUTHOR]

The rapid expansion of wireless communication systems has spurred a growing demand for adaptable multiple-input multipleoutput (MIMO) antennas capable of accommodating diverse frequency bands and operational environments. This paper presents a compact quad-port wide-band tuning-reconfigurable MIMO antenna tailored specifically for 5G applications operating within the sub-6 GHz spectrum. The proposed design enhances isolation levels (> 18 dB) and augments pattern diversity by utilizing four orthogonal radiating elements. Integrating a C-shaped monopole element with a matching stub facilitates frequency tuning via a varactor diode, ensuring a consistent radiation pattern. The lower and upper resonant bands could be fine-tuned by adjusting the varactor diode's reversebiased voltage within the allowed range of 0.5 to 10 V. These bands are 4 to 5.18 GHz and 5.45 to 6.65 GHz, respectively. The proposed antenna system's four C-shaped elements are placed on a 40 × 40 mm2 ground plane and mounted on a Rogers RT5880 substrate that is 0.8 mm thick with a relative permittivity of 2.2. This design is well suited for various wireless applications and cognitive radio networks due to its compatibility with sub-6 GHz frequency bands and wide-band tuning capabilities. [ABSTRACT FROM AUTHOR]

Cognitive radio networks enable the detection and opportunistic access to an idle spectrum through spectrum-sensing technologies, thus providing services to secondary users. However, at a low signal-to-noise ratio (SNR), existing spectrum-sensing methods, such as energy statistics and cyclostationary detection, tend to fail or become overly complex, limiting their sensing accuracy in complex application scenarios. In recent years, the integration of deep learning with wireless communications has shown significant potential. Utilizing neural networks to learn the statistical characteristics of signals can effectively adapt to the changing communication environment. To enhance spectrum-sensing performance under low-SNR conditions, this paper proposes a deep-learning-based spectrum-sensing method that combines multiple signal features, including energy statistics, power spectrum, cyclostationarity, and I/Q components. The proposed method used these combined features to form a specific matrix, which was then efficiently learned and detected through the designed 'SenseNet' network. Experimental results showed that at an SNR of −20 dB, the SenseNet model achieved a 58.8% spectrum-sensing accuracy, which is a 3.3% improvement over the existing convolutional neural network model. [ABSTRACT FROM AUTHOR]

The identification of the presence of primary user enhances the spectrum efficiency in cognitive radio (CR). The studies suggested that the existence of malicious user adversely affects the system performances; especially the primary user emulation attack (PUEA) has a greater influence in spectrum sensing on the CR network. Moreover, the detection of PUEA is a challenging and complex task and involves constructive design with sensing algorithm. In this study, a support vector machine (SVM) along with energy vectors is designed to improve the spectrum sensing mechanism. The presented approach integrates the SVM with the Bayesian optimization algorithm (BOA) in which SVM aims to detect the malicious user by randomly selecting the primary and secondary users. The BOA aims to optimize the hyperparameters of the SVM, thereby improving the detection performances and maximizes the algorithms convergence speed. The experimental analysis illustrate that the presented approach predicts the PUEA with 98% accuracy and reduces the average node power is 9.7. Moreover, the results demonstrated that the system performance does not vary on implementing it with the large-scale CR network. Finally, the system performances are compared and evaluated with existing techniques in terms of accuracy, and average noise power. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) offer a promising solution for improving spectrum utilization. However, ensuring quality of service (QoS) for heterogeneous secondary users (SUs) during spectrum handoff, particularly under high primary network traffic, poses challenges. This study develops a Markov-based analytical model to evaluate the gain of a non-switching spectrum handoff technique using a hybrid interweave-underlay spectrum access strategy, considering sensing errors. The proposed model assesses the effects of the hybrid spectrum access method for prioritized traffic across multiple SU classes, aiming to meet QoS requirements for delay-sensitive traffic. The study examines the CRN's short-term behavior and realistic queueing scenarios by analyzing the system's transient dynamics. Different spectrum access methods are compared for evaluation purposes. The analysis focuses on evaluating the effectiveness of the enhanced hybrid spectrum access scheme compared to individual interweave and hybrid interweave-underlay spectrum access strategies in terms of QoS provisioning for heterogeneous SUs. The results demonstrate increased throughput and improved spectrum utilization with the suggested scheme, affirming its suitability for satisfying QoS requirements for both delay-sensitive and delay-tolerant users. [ABSTRACT FROM AUTHOR]

In order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed. [ABSTRACT FROM AUTHOR]

Propagation models are essential for the prediction of received signal strength and the planning of wireless systems in a given environment. The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnels. However, carrying out simulations for large‐scale environments is still computationally expensive. A convolutional neural network (CNN)‐based propagation model, which can provide high‐fidelity received signal strength prediction based on results from low‐cost VPE simulations, is proposed. A thorough study of the generalisability, including both interpolation and extrapolation capabilities, of the proposed CNN model is conducted. It is found that the proposed model can achieve significant computational savings while maintaining acceptable accuracy, and its performance is validated in both simulations and actual tunnel cases. [ABSTRACT FROM AUTHOR]

In cognitive radio networks (CRNs), rendezvous is the vital step prior to the communication between two unlicensed secondary users (SUs), where the SUs hop on the same channel at the same time to establish a link. With the dramatic fall in the cost and size of wireless transceivers, it becomes more reasonable to apply multiple radios to achieve significant improvement in the rendezvous performance. However, most of the existing multiradio rendezvous algorithms are proposed for homogeneous CRNs where all the SUs are equipped with an equal number of radios and do not possess backward compatibility to SU with a single radio. In reality, the CRNs are heterogeneous in nature as SUs may have different numbers of radios. In this paper, a composite CH algorithm is proposed for an asynchronous and heterogeneous network to achieve blind rendezvous with full rendezvous diversity. An SU with m number radios are categorized into three groups those follow different channel hopping (CH) algorithms. The upper bound of the rendezvous latency is being evaluated with a brief theoretical and mathematical analysis. Extensive simulations have conducted for different performance metrics, and the results are compared with the state-of-art algorithms. Overall, the proposed algorithm shows better performance in heterogeneous CRNs. [ABSTRACT FROM AUTHOR]

Radio frequency fingerprint identification (RFFI) can distinguish highly similar wireless communication devices to protect physical layer security and improve the security of wireless networks effectively, which has been widely used for spectrum management and physical layer secure communication. However, most RFFI methods show a degradation of performance under low signal-to-noise ratio (SNR) environments. In this paper, we propose a RSBU-LSTM network relying on multiple features to improve the identification accuracy with low SNR. Firstly, we use multiple features of in-phase (I), quadrature (Q), and phase as inputs. Then, we use multiple Residual Shrinkage Building Units (RSBUs) to extract the correlation features within the cycle of signals and preserve as many features as possible in low SNR environments. Finally, we use the long short-term memory (LSTM) to extract the relevant features of the signals of non-adjacent cycles. The experimental results show that the proposed network can effectively complete RFFI in low SNR environments and show better performance than other models used for comparison. [ABSTRACT FROM AUTHOR]

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study. [ABSTRACT FROM AUTHOR]

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks. [ABSTRACT FROM AUTHOR]

Imbalance and unfairness in resource allocation and utilization can lead to a degraded network environment where wireless network users experience poor quality of service (QoS) within the wireless networks. In multi-user wireless radio networks with constrained wireless network resources, such as power and bandwidth resources, uniform allocation of power or bandwidth may lead to unfairness in network resource management. The cooperative cognitive radio relay networks' (CCRRN) radio resource allocation problem among multi-users is investigated in this paper through combined resource optimization. To facilitate fairness in the multi-user networks, the maxi–min criterion is employed. The max–min fairness ensures that when the wireless network attains max–min utility fairness, no user's utility can be improved without degrading the utility performance of any other user. To maximize the worst-case user (max–min) capacity in the CCRRN using the power and bandwidth allocation strategy, a combined optimal resource allocation (COPBA) scheme is proposed. The max–min resource allocation optimization problem developed is observed to be convex. Efficient computer-based numerical optimization solver packages, such as SNOPT, exist that can solve convex optimization problems efficiently. The MATLAB software provides a robust integrated environment where the implementation of the SNOPT solver can be executed. Using the uniform bandwidth optimal power allocation (UBOPA) scheme as a benchmark, the fairness metric of the COPBA is evaluated and compared. Furthermore, the results showed the COPBA outperforming the UBOPA scheme. [ABSTRACT FROM AUTHOR]