Vacant Channels Research Articles

Monitoring and enhancing the co-operation of IoT network rhrough scheduling function based punishment reward strategy

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

P1-xTa8+xN13 (x=0.1-0.15): A Phosphorus Tantalum Nitride Featuring Mixed-Valent Tantalum and P/Ta Disorder Visualized by Scanning Transmission Electron Microscopy.

We report on the synthesis, crystal, and electronic structure, as well as the magnetic, and electric properties of the phosphorus-containing tantalum nitride P1-xTa8+xN13 (x=0.1-0.15). A high-pressure high-temperature reaction (8 GPa, 1400 °C) of Ta3N5 and P3N5 with NH4F as a mineralizing agent yields the compound in the form of black, rod-shaped crystals. Single-crystal X-ray structure elucidation (space group C2/m (no. 12), a=16.202(3), b=2.9155(4), c=11.089(2) Å, β=126.698(7)°, Z=2) shows a network of face- and edge-sharing Ta-centered polyhedra that contains small vacant channels and PN6 octahedra strands. Atomic resolution transmission electron microscopy reveals an unusual P/Ta disorder. Mixed-valent tantalum atoms exhibit interatomic distances similar to those in metallic tantalum, however, the electrical resistivity is quite high in the order of 101 Ω cm. The density of states and the electron localization function indicate localized electrons in both covalent and ionic bonds between P/Ta and N atoms, combined with less localized electrons that do not contribute to interatomic bonds.

P1–xTa8+xN13 (x = 0.1–0.15): A Phosphorus Tantalum Nitride Featuring Mixed‐Valent Tantalum and P/Ta Disorder Visualized by Scanning Transmission Electron Microscopy

We report on the synthesis, crystal, and electronic structure, as well as the magnetic, and electric properties of the phosphorus‐containing tantalum nitride P1–xTa8+xN13 (x = 0.1–0.15). A high‐pressure high‐temperature reaction (8 GPa, 1400 °C) of Ta3N5 and P3N5 with NH4F as a mineralizing agent yields the compound in the form of black, rod‐shaped crystals. Single‐crystal X‐ray structure elucidation (space group C2/m (no. 12), a = 16.202(3), b = 2.9155(4), c = 11.089(2) Å, β = 126.698(7)°, Z = 2) shows a network of face‐ and edge‐sharing Ta‐centered polyhedra that contains small vacant channels and PN6 octahedra strands. Atomic resolution transmission electron microscopy reveals an unusual P/Ta disorder. Mixed‐valent tantalum atoms exhibit interatomic distances similar to those in metallic tantalum, however, the electrical resistivity is quite high in the order of 10‑1 Ω cm. The density of states and the electron localization function indicate localized electrons in both covalent and ionic bonds between P/Ta and N atoms, combined with less localized electrons that do not contribute to interatomic bonds.

Dense and high flexural strength cordierite ceramics doped with Li2O

High-strength cordierite ceramics with Li2O were fabricated by non-pressure sintering. The results showed that the addition of Li2O significantly reduced the sintering temperature and expanded the sintering range. Additionally, the mechanical strength of the samples clearly increased after Li2O doping. In particular, the sample with 0.5 wt% Li2O sintered at 1250 °C showed the best properties, with a flexural strength of 178.6 MPa and a thermal expansion coefficient of 2.1 × 10−6 °C−1. Its flexural strength is much higher than samples without Li2O and the cordierite ceramics prepared by non-pressure sintering reported in the literature. The high density of the sample (2.52 g cm−3) is the main reason for its excellent mechanical properties. Furthermore, first-principles calculations verify that Li+ dopants insert at the vacant channel of α-cordierite crystal with six-fold coordination, which may reduce the anisotropy of the thermal expansion coefficient of α-cordierite, thus improving the strength of ceramics.

Mechanical Performance of Hybrid Fibre Reinforced Magnesium Oxychloride Cement-Based Composites at Ambient and Elevated Temperature

Magnesium oxychloride cement (MOC) is often recognized as an eco-friendly cement and has found widespread application in various sectors. However, research on its resistance against elevated temperatures including fire is very limited. This paper thoroughly investigated the mechanical performance of fibre reinforced MOC-based cementitious composite (FRMOCC) at ambient and elevated temperatures. A recently developed water-resistant MOC was used as the base matrix which was further reinforced using hybrid basalt and polypropylene fibres at various proportions, and a systematic study on the effect of fibre dosage on compressive and tensile strength of FRMOCC was conducted. The specimens were exposed to elevated temperatures ranging from 200 to 800 °C; mechanical performance and phase composition from a microscale study were analysed. The findings revealed that compressive strength, with the increase in temperature, substantially decreased, with values of 30–87% at 400 °C and over 95% at 800 °C. Specimens with 1.5% basalt and 0.5% PP fibre showed the least reduction possibly due to the vacant channels created as a result of the melting effect of PP fibres. Tensile strength was also completely lost at 600 °C and the specimens suffered substantial mass loss exceeding 30% at this temperature, indicating significant matrix decomposition. Additional analysis using X-ray diffraction (XRD) and scanning electron microscope (SEM) revealed the decomposition stages of the matrix and highlighted the instability of the main hydration phases of FRMOCC at elevated temperatures.

Predicting the Crystal Structure of Beryl from the Chemical Composition

Abstract Crystal-structure prediction is a challenging topic. Few models have been developed that use the chemical composition of a known compound to determine a complete crystal structure. A complete structural model should include all major bond lengths and angles, atomic coordinates, polyhedral volumes and distortions, and unit-cell parameters. The mineral beryl is used here to develop such a model. Beryl (Be3Al2Si6O18) is an ideal mineral to show that predicting the crystal structure using chemistry is possible: the framework structure is known, this structure has only two cation sites that experience substitutions, and these substitutions only minimally occur simultaneously. Vacant channel sites are involved in coupled substitutions, allowing alkali cations (typically Na+) to enter the structure, and the channel regularly contains molecular H2O correlated to Na content (Henry et al. 2022). The research employed single-crystal X-ray diffraction and electron probe microanalyses of 80 samples to create a model which was subsequently tested using 33 samples. Results show that the complete crystal structure of beryl can be accurately calculated using the Al-site average ionic radius (Al-SAIR) for octahedrally trending beryl, or the Be-site average ionic radius (Be-SAIR) for tetrahedrally trending beryl. Beryl for which Al-SAIR > (0.45 × Be-SAIR) + 0.414 is considered octahedrally trending and that for which Al-SAIR ≤ (0.45 × Be-SAIR) + 0.414 is considered tetrahedrally trending. Red beryl (differentiated by high Fe and Mn) exhibits a different trend, forming a subset of the octahedrally trending beryl. There is an upper limit to the predictable range of beryl structures of 0.604 Å Al-SAIR or 0.326 Å Be-SAIR. This model makes it possible to explore limitations on the crystal structure of beryl and the potential for unusual cation substitutions, or conversely, to compute the structure of a hypothetical pure endmember beryl. It is robust for true beryl (beryl for which Be and Al are the dominant non-Si cations) up to a high limit of cation substitutions, but not for other beryl-group minerals, including stoppaniite, bazzite, avdeevite, and johnkoivulaite. Future studies on beryl will be able to estimate basic crystal-structure features arising from standard chemical analyses as used in this research. It enables the creation of an extensive beryl database, aids comparisons of natural beryl to synthetics, and helps provide further guidance on provenance studies. It also invites future crystal-structure prediction research. This approach is applicable to broader fields, as crystal structures are linked to the physical characteristics of minerals and rocks in which they form.

Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.

AN IMPROVED CHANNEL SELECTION ALGORITHM (ICSA) FOR SPECTRUM HAND-OFF IN COGNITIVE RADIO AD HOC NETWORK

The exponential growth in wireless communication technology has led to spectrum scarcity. Because of this, the world has moved from Fixed Spectrum Allocation (FSA) Strategy to Dynamic Spectrum Allocation (DSA) Strategy. Cognitive Radio (CR) is a rapidly growing technique that makes use of DSA, where the licensed users, otherwise known as Primary Users (PUs), share their channel with the unlicensed users, known as Secondary Users (SUs). The SUs can use the PUs channel when they are not being used for transmission, but they have to vacate to another vacant channel when the PUs arrive in their channel. Switching to another vacant channel on the arrival of the PUs to their channels is known as spectrum hand-off. Finding another suitable vacant channel for the SUs to continue their interrupted transmission is challenging. Several researchers have used different techniques to address the challenge of target channel selection for spectrum hand-off by considering channel occupancy alone. Still, they suffer challenges like the high number of spectrum hand-offs, high delay, and low throughput. Therefore, this research focuses on developing an Improved Channel Selection Algorithm (ICSA) that considers channel occupancy serially with signal quality requirements for selecting a particular backup channel for spectrum hand-off. The simulation was carried out using Network Simulator (NS), and the results were plotted using Matlab. The results showed that the ICSA had better performance when compared with the Novel Proactive Hand-off Scheme (NPHS) regarding the number of hand-offs, average delay, and average throughput.

Hybrid Machine-Learning-Based Spectrum Sensing and Allocation With Adaptive Congestion-Aware Modeling in CR-Assisted IoV Networks

Unlicensed cognitive-radio (CR)-assisted Internet of Vehicles (IoV) users can access licensed providers’ radio spectrum and concurrently utilize the dedicated channel for data transmission in vehicular communication. Optimizing channel access in cognitive IoV networks can help maximize available spectrum resources. This article proposes a novel sensing and communication integrated framework, dubbed as the CR-assisted IoV network (CRAV-Net), using a cluster-based hybrid optimization approach with adaptive congestion-aware modeling for dynamic high-mobility vehicular networks in an urban city context. In CRAV-Net, intelligent hybrid learning spectrum agents are introduced, which perform spectrum sensing (SS) using a deep learning (DL) model. It dynamically learns the multilevel spatial and temporal graphical features from input spectrograms through layer-by-layer propagation. It efficiently predicts the spectrum occupancy in the primary spectrum, without a priori knowledge of the radio environment. Then, to assign the vacant channels to the secondary vehicles, a support vector machine classifier is trained based on several learning features, including the vehicle stay time, vehicle density, and network capacity, to select the optimal resource route. The proposed framework achieves an overall accuracy of 99.74% in SS using the custom data set, outperforming state of the art by 12.60% at −25-dB signal-to-noise ratio. In addition, it brings a performance gain of 0.81% in SS accuracy when evaluated on real-world signals. Furthermore, in optimal network node allocation, the proposed framework achieves a mean accuracy of 98.45%, outperforming the existing methods by 0.63% and 18.32% in terms of accuracy and allocation time, respectively.

Distributed Artificial Intelligence Empowered Sustainable Cognitive Radio Sensor Networks: A Smart City on-demand Perspective

Smart cities are claimed to be smart if the new technologies are capable of providing desired sustainable outcome. The sustainable properties of smart city applications require less energy consumption and efficient resource allocation. The Internet-of-Things (IoT), 5G, and fog networks have emerged as the most crucial researched areas due to their numerous applications for smart cities to provide the desired sustainable outcome. The sustainable properties of Wireless Sensor Networks (WSNs) play a vital role in the deployment of these technologies into the physical world and efficient utilization of the available spectrum is a major problem faced here. As a potential solution of this, Cognitive Radio (CR) merged with WSN as Cognitive Radio Sensor Networks (CRSNs) make the smart perspective with high resource management through cooperative communication. The proposed work establishes a dynamic correlation between Secondary Users/nodes (SUs) in a single cluster according to their statistical behavior at the time of performing smart cooperative communication in CRSNs to improve sustainability of smart world IoT applications. Distributed Artificial Intelligence (DAI) is used to calculate the real-time resource allocation to these clusters using their respective Coordinator Agent (CoA) based on the dynamic behaviors. To improve sustainability in the smart city applications, the time delay in the prediction of vacant channels is reduced which results in making these applications become more energy efficient. The effectiveness of the proposed work is illustrated with mathematical analysis and simulation results confirm its better sustainable performance compared to the existing techniques.

A Novel MAC Protocol with Fusion Center and Adaptive Full-Duplex Communication for Cognitive Radio Networks

Cognitive radio network is an intelligent and adaptive communication technology used to effectively utilize the radio spectrum to provide better communication. It enables the secondary (unlicensed) users to communicate over vacant channels without disrupting the communication of the primary (licensed) users. This paper proposes a novel contention-free MAC protocol for full-duplex and half-duplex cognitive radio networks. A novel concept of a fusion center-based channel allocation scheme is proposed to provide better channel allocation and reduced energy consumption. The proposed technique is compared with the existing techniques in terms of the average number of channels being sensed by each user, average wait time, collisions, throughput, and average energy consumption. The number of collisions increases as the maximum allotted time of communication increases in existing approaches. The experiment is performed with 20, 40, 60, and 80 s. The existing approaches show for 20 s, the number of collisions is 65, 61, 68, and 62; for 40 s, the number of collisions is 93, 96, 95, and 92; for 60 s, the number of collisions is 122, 126, 133, and 130; for 80 s, the number of collisions is 176, 170, 173, and 175. The proposed method approaches the number of collisions will become zero as the maximum allotted time of communication increases. The experimental results show that the proposed approach performs better than existing approaches. Moreover, it can be observed that the average waiting time of secondary users when a collision occurs is reduced with the application of a fusion center-based channel allocation scheme.

Stress analysis of vascularized glass fiber composites exposed to bending loading

Vacant vascular channels within fiber-reinforced composites offer various functionalities ranging from self-repair and healing, damage detection, to thermal management. However, these channels affect the structure of the composite and can alter the stress distribution when subjected to mechanical loads. This study aims to investigate the bending load on these vascularized channels produced by the removable solid wire technique. The tests were conducted with the samples with and without channel according to ASTM D7264. Interestingly, the vascularized samples showed a higher maximum flexural modulus and stress than the non-vascularized ones. A three-dimensional finite element model was developed to analyze the stresses at the regions close to the channel. The results revealed the effect of the stacking sequence on the stress distribution. Investigation of stresses near the resin-rich region, generated due to the opened channel, showed that this region is not significantly affected by bending in contrast to the transverse loading.

Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network.

In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system’s MTTR gains range from to over for various parametric values of the system model.

Glass crystallization making red phosphor for high-power warm white lighting

Rapid development of solid-state lighting technology requires new materials with highly efficient and stable luminescence, and especially relies on blue light pumped red phosphors for improved light quality. Herein, we discovered an unprecedented red-emitting Mg2Al4Si5O18:Eu2+ composite phosphor (λex = 450 nm, λem = 620 nm) via the crystallization of MgO–Al2O3–SiO2 aluminosilicate glass. Combined experimental measurement and first-principles calculations verify that Eu2+ dopants insert at the vacant channel of Mg2Al4Si5O18 crystal with six-fold coordination responsible for the peculiar red emission. Importantly, the resulting phosphor exhibits high internal/external quantum efficiency of 94.5/70.6%, and stable emission against thermal quenching, which reaches industry production. The maximum luminous flux and luminous efficiency of the constructed laser driven red emitting device reaches as high as 274 lm and 54 lm W−1, respectively. The combinations of extraordinary optical properties coupled with economically favorable and innovative preparation method indicate, that the Mg2Al4Si5O18:Eu2+ composite phosphor will provide a significant step towards the development of high-power solid-state lighting.

Multi-Winner Spectrum Allocation in Cognitive Radio Networks: A Single-Sided Auction Theoretic Modelling Approach with Sequential Bidding

Cognitive radio (CR) has evolved as a novel technology for overcoming the spectrum-scarcity problem in wireless communication networks. With its opportunistic behaviour for improving the spectrum-usage efficiency, CR enables the desired secondary users (SUs) to dynamically utilize the idle spectrum owned by primary users. On sensing the spectrum to identify the idle frequency bands, proper spectrum-allocation mechanisms need to be designed to provide an effectual use of the radio resource. In this paper, we propose a single-sided sealed-bid sequential-bidding-based auction framework that extends the channel-reuse property in a spectrum-allocation mechanism to efficiently redistribute the unused channels. Existing auction designs primarily aim at maximizing the auctioneer’s revenue, due to which certain CR constraints remain excluded in their models. We address two such constraints, viz. the dynamics in spectrum opportunities and varying availability time of vacant channels, and formulate an allocation problem that maximizes the utilization of the radio spectrum. The auctioneer strategises winner determination based on bids collected from SUs and sequentially leases the unused channels, while restricting the channel assignment to a single-channel-multi-user allocation. To model the spectrum-sharing mechanism, we initially developed a group-formation algorithm that enables the members of a group to access a common channel. Furthermore, the spectrum-allocation and pricing algorithms are operated under constrained circumstances, which guarantees truthfulness in the model. An analysis of the simulation results and comparison with existing auction models revealed the effectiveness of the proposed approach in assigning the unexploited spectrum.

A corner truncated fractal slot ultrawide spectrum sensing antenna for wireless cognitive radio sensor network

SummaryDue to the scarcity of the radio frequency spectrum, the emergent network sharing technique elicits the efficient use of licensed unutilized spectrum. Here, an ultra‐wideband (UWB) microstrip antenna is proposed for wireless cognitive radio sensor network (WCRSN). The antenna is uniquely designed for large spectrum scanning of channel vacancies in licensed frequency bands. Software‐defined radios (SDR) govern the algorithmic controls on spectrum sensing and channel interface using an appropriate antenna. The antenna comprises the effects of fractal slot patch and electromagnetic bandgap (EBG) ground structures on 12 × 18 × 1.6 mm3 FR4 (relative dielectric constant of 4.4) substrate. Circular polarization is achieved through corner truncation to make antenna orientation free in wireless sensing networks. The measured |S11| parameter offers 0.75‐ to 12‐GHz wide spectrum sensing with 2.51‐dBi average gain. The antenna is unique in compactness and includes lower edge bands of frequencies (0.75–3.1 GHz) along with UWB (3.1–12.0 GHz) for a very wide range scanning to utilize vacant channel opportunity in WCRSN through SDR. The proposed structure is experimented and validated for real‐time wideband scanning of the frequency spectrum within the operating limit as per the allotted FCC standard.

Compact Corner Truncated Fractal Slot Antenna for Cognitive Radio Sensor Network

The cognitive radio (CR) opens new paradigms of opportunistic utilization of unused frequency spectrum through spectrum sharing. Dynamic spectrum access (DSA) provides an effective solution to channel congestion with the help of software-defined radio in cognitive radio networks (CRN). Software defined radio (SDR) offers manipulative wide spectrum sensing and reconfigurable transmission properties between licensed primary user (PU) and unlicensed secondary user (SU). The opportunistic spectrum sharing elicits the need for hardware compatibility from a common radiating antenna for extra-large band spectrum scanning. This paper proposes a novel compact antenna for wide spectrum sensing to utilize vacant channel opportunity in wireless cognitive radio sensor network (WCRSN). The antenna design comprises the combinational effects of fractal slots and electromagnetic bandgap structure on compact FR4 substrate 12 × 18 × 1.6 mm2. The circular polarization improves the feasibility of antenna for orientation-free scanning of spectrum through corner truncation. The novel antenna design exhibits large measured impedance bandwidth from 0.79 to 12.0 GHz compatible with Zigbee, Bluetooth, GPS, GSM, LoRa, WiFi and other UWB applications. The antenna offers average gain 2.59 dBi over entire operating frequency range and validates compatibility for spectrum sensing in WCRSN as per FCC standards.

Energy preservation and network critic based channel scheduling (EPNCS) in cognitive radio sensor networks

Cognitive Radio Sensor Network (CRSN) is a distributed network of Cognitive Radio (CR) enabled wireless sensor nodes where there is no guarantee of the availability of vacant channels for data transfer. In CRSN nodes spend energy in radio spectrum sensing to sense the availability of vacant channels and hence energy preservation becomes a major concern. In this proposed work, a novel algorithm—energy preservation and network critics based channel scheduling (EPNCS) algorithm in CRSNs is designed which regulates the slot time for sensor nodes. Depending on environmental data traffic, the sleep time of sensor nodes is varied which reduces power consumption. Scalable dynamic slots are computed for each sensor node based on the average buffer occupancy which leads to optimal channel utilization. Simulation results show that EPNCS improves the network lifetime and throughput of CRSN significantly in comparison with existing protocols.

Noise uncertainty effect on multi-channel cognitive radio networks

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

ICBCA – Improved Cluster Based Channel Allocation in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) operate in an overcrowded electromagnetic environment, as the spectrum is shared by various wireless communication technologies. This gives rise to various challenges related to optimized and efficient spectrum utilization. Cognitive radio (CR) has emerged as a solution satisfying this requirement, as it is capable of adapting to the dynamic radio spectrum. Thanks to the deployment of cognitive radio in WSNs, the spectrum may be utilized in a more efficient manner. CR may identify the vacant channels dynamically, allowing the sensor nodes to effectively communicate with each other. In this paper a clustering algorithm known as improved cluster-based channel assignment (ICBCA) is implemented, forming clusters of CR sensor nodes and then selecting vacant channels for data transmission purposes. Simulation results show that ICBCA outperforms existing clustering algorithms in CR sensor networks