Spectrum Utilization Research Articles

QoT-aware tree selection, routing, modulation, and spectrum assignment for filterless EONs over the C + L-band

Filterless optical networks (FONs) as an economical solution use passive couplers/splitters rather than expensive active filter devices. In this way, after intermediate and destination nodes, lightpaths are dropped and continued, which generates leakage signals in other links and wastes spectrum. Thus, designing efficient resource allocation in FONs by considering the leakage signals and their interfering effects is of utmost importance. On the other hand, a gradual transition from wavelength division multiplexing optical networks to elastic optical networks (EONs) is occurring due to their efficient utilization of spectrum. Furthermore, utilizing the L-band beside the conventional C-band for spectrum assignment offers a wide range of frequency resources. Therefore, in this paper, we propose an integer linear program (ILP) to solve quality of transmission (QoT)-aware tree selection, routing, modulation, and spectrum assignment problems in filterless EONs over the C+L-band. Furthermore, we provide heuristic algorithms to deal with complex large-scale networks. The performance gap of the proposed ILP and heuristic algorithms is evaluated over a small-scale (5-node) network. The results show that the ILP and heuristic algorithms have almost the same performance in terms of spectrum usage and assigned modulation format, and ILP has a slightly higher generalized signal-to-noise-ratio (GSNR) (0.23 dB or 0.8% at optimum launch power). Furthermore, the heuristic algorithms are also examined over a large-scale network (TID region A topology). The results reveal that the GSNR estimation method severely affects the performance in terms of spectrum usage, blocking, and outage. Furthermore, by using the proposed MX5 method, as long as there is a fill margin of approximately 2 dB, there is no outage or blocking over the C+L-band, up to a network throughput of 110 Tbps and conventional C-band transmission with lower throughput (i.e., 40 Tbps). Finally, our extensive numerical results provide a rule of thumb for balancing blocking, outage, spectrum usage, and the number of expensive L-band transponders by selecting the appropriate modulation assignment method.

Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms.

Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023.

Transient analysis of energy-saving strategy for cognitive radio networks using G-queue with heterogeneity

Energy efficiency, high data speeds, and effective spectrum utilization stand as the primary factors governing 5G and beyond networks. A base station (BS) in a cognitive radio network (CRN) that conserves energy during sleep periods is a promising contender for achieving more effective spectrum allocation. An efficient energy-saving strategy is offered in this study to achieve greener communication in wireless cellular networks. This study aims to propose and analyze an energy-saving scheme for the BS, taking into account the heterogeneity and reliability of networks. The entire system is modeled as a three-dimensional Markov chain by establishing a discrete-time preemptive priority queueing model with single vacation, heterogeneous packets, and negative packets such as viruses. Then, the transient analysis of the Markov chain is performed using the recursive method. Utilizing the transient probability distribution, we present numerical results derived from reliability analysis and queueing analysis to substantiate the validity of the proposed model. Furthermore, results based on the energy-saving degree are showcased to affirm the effectiveness of the proposed scheme. Finally, a reward cost function is obtained depending on the successful transmission of secondary packets, and the Quasi-Newton method is used to determine the optimal reward cost.

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

SummaryThe non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.

Spray: A Spectrum-efficient and Agile Concurrent Backscatter System

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we propose Spray , a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype the Spray tag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps.

Spectrum utilization improvement for multi‐channel EH‐CRN with spectrum sensing

AbstractDue to the ever‐growing applications and services of the Internet of Things (IoT), designing energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the 6G space–air–ground integrated networks becomes much more challenging. Fortunately, energy harvesting (EH) and cognitive radio (CR) technologies have been proposed to alleviate these challenges. Inspired by this fact, this paper studies the issue of spectrum reuse in terms of spectrum utilization efficiency (SUE) in the energy harvesting cognitive radio network (EH‐CRN), where multiple primary transceiver pairs, one multi‐antenna secondary transmitter (ST), and one secondary base station (SBS) coexist. To characterize the impact of small‐scale fading and improve the SUE of the EH‐CRN with perfect spectrum sensing (SS), an adaptive scheme concerning SS, channel selection, EH, and data transmission (SCED) scheme are proposed, where the ST selects the channels for SS based on the residual energy, and adjusts the duration of EH and data transmission with respect to the sensing results. Then the Markov decision process problem of SUE is formulated, which is challenging due to the infinite system space and action space. To tackle the Markov decision process problem, the system space and action space are discreted, and divide the ST into the energy‐limited case and energy‐sufficient case according to specific energy condition. Moreover, theoretical results are extended to the EH‐CRN with imperfect SS. Numerical results show that the SUE under the SCED scheme in perfect SS and imperfect SS scenarios is better than that under other schemes.

Efficiency Enhancement in Photovoltaic–Thermoelectric Hybrid Systems through Cooling Strategies

The integration of photovoltaic (PV) and thermoelectric (TE) modules in PV-TE systems has shown potential for expanding the utilization of the solar spectrum, enhancing the total power output, and reducing the space that is required for PV power plants. This paper discusses the characteristics of a practical PV-TE system model. Typically, to boost the power output of the TE component, a significant temperature difference is induced across the thermoelectric generator (TEG) module using various heat removal methods. These cooling techniques not only enhance the TEG module’s efficiency but may also improve the performance of the PV component. In this study, we evaluate the efficiencies of PV-TE systems that are equipped with polycrystalline silicon solar cells and seven distinct TEGs under four different conditions. Initially, the PV-TE hybrid systems are tested without a cooling mechanism at an ambient temperature of 25 °C (Standard Test Conditions EN/IEC 61215). Subsequently, we examine the systems with a passive cooling approach, employing aluminum heat sinks to facilitate improved heat dissipation. Further tests involve an active cooling system using water and then nanofluid as coolants. The results from these assessments aim to establish a benchmark for enhancing the efficiency of future PV-TE systems.

Routing and dynamic core allocation with fragmentation optimization in EON-SDM

The increasing need for rapid data transmission in optical networks has made the effective allocation of spectrum resources a crucial necessity. This study presents a new spectrum allocation approach that incorporates path selection and careful consideration of cross-talk (XT) limitations. We propose a novel fragmentation coefficient and dynamic core-changing-based routing, spectrum, and core allocation (FC-DCC-RSCA) technique. The algorithm begins by searching for the shortest paths using the Dijkstra algorithm. It then proceeds to evaluate fragmentation coefficients (FC) using the Continuous Aligned Slot Ratio (CASR) model. Subsequently, the FC undergoes a filtration process wherein only those falling inside predetermined fragmentation ranges (min and maxFC) are retained. Further spectrum allocation has been done based on XT constraints. The algorithm has better proficiency in dynamic flexibility, enabling it to effectively handle varying traffic loads. By conducting a series of rigorous simulations, our proposed approach exhibits a notable decrease in bandwidth blocking probability (BBP) while also opening the door for considerable improvements in spectrum utilization. The algorithm’s proficiency in adapting to changing network circumstances signifies its importance as a helpful thing in enhancing the allocation of resources within optical networks. It is well-positioned to address the developing requirements of contemporary communication systems. The spectrum utilization of the FC-DCC-RSCA increases by 12.5% and 6.25%, exhibits a reduction in its FR by 13.5% and 2.7%, and has a BBP of 0.051 for the traffic load of 450 Erlang, compared to SBP-PMC and FMDE-RSCA. The algorithm that has been provided provides opportunities for more investigation and enhancement. Potential areas for future research include the examination of strategies to improve energy efficiency, the development of scalable solutions for bigger network infrastructures, and the incorporation of machine learning methodologies to facilitate dynamic decision-making processes. Furthermore, it is worth investigating the potential of the method to be applied in developing optical communication technologies, such as space-division multiplexing. These endeavors hold the potential to enhance the current level of knowledge and practices in the field of optical network management and optimization.

Designing energetic landscape in the multi-bandgap lead sulphide colloidal quantum dots ensemble for efficient solar spectrum utilization

The distinctive nature of the solar spectrum in the near and shortwave infrared region demands the appropriate design of the active materials for efficient infrared photon absorption in a solar cell. Colloidal quantum dots (CQDs) based on lead sulphide (PbS) showed significant absorption in the near and shortwave infrared region through bandgap tuning. Despite their high absorption coefficient, PbS CQDs show a strong excitonic absorption peak, which cannot cover a significant portion of the solar spectrum in the near or shortwave infrared region. Mixing CQDs with different bandgaps can lead to better coverage of the solar spectrum and, hence, better photon absorption. In this work, we have estimated the ideal PbS CQD-based multi-bandgap ensemble combination and active material design strategy for optimum infrared photon absorption. In addition, we have investigated in detail the crucial factors that decide the performance of solar cells based on multi-bandgap CQDs. The simulation shows that careful optimization of the energetic landscape of the ensemble, along with the charge carrier mobility, can improve the efficiency up to 16.48 % under the AM 1.5 G spectrum. The optimized infrared photovoltaic performance with a perovskite filter can reach up to 9.29 %.

In situ fabrication of a plasmonic Bi@Bi2O2CO3 core-shell heterostructure for photocatalytic CO2 reduction: structural insights into selectivity modulation.

The precise design of active sites and light absorbers is essential for developing highly efficient photocatalysts for CO2 reduction. Core-shell heterostructures constructed based on large-sized plasmonic Bi metals are ideal candidates because of the utilization of full-spectrum light and effective charge separation. However, the mechanism of selectivity modulation of large-sized Bi@semiconductor photocatalysts has yet to be explored in depth. Herein, a plasmonic Bi@Bi2O2CO3 core-shell heterostructure was successfully synthesized via a facile solvothermal treatment in deep eutectic solvents, demonstrating highly efficient photocatalytic CO2 reduction. This structure features a sizeable Bi sphere with a thin, epitaxially grown Bi2O2CO3 shell, which allows for the utilization of the entire light spectrum. Additionally, the oxygen vacancies in the Bi2O2CO3 shell can rapidly trap electrons transferred from the Bi core via Bi-O-Bi bonds, thereby forming abundant electron-rich interfaces that serve as the active sites for activating reactant molecules and facilitating the reaction.

A Digital IR-UWB Transmitter With High Spectrum Utilization and AM-PM Distortion Calibra

A Digital IR-UWB Transmitter With High Spectrum Utilization and AM-PM Distortion Calibra

A review on evolution, expectations and key enabling techniques of 5G

For the transmission of data at high speed, the requirement of using a larger bandwidth increases due to the rise in wireless communication technologies like smart devices, mobiles, etc. An abrupt increase in the requirement for internet data due to an increase in internet users demanded time-to-time upgrades in available techniques. A significant modernization has been done by technology in mobile and wireless communication and networking fields. Therefore, the development of mobile communication began with the First Generation (1G) and has progressed continuously to the Fifth Generation (5G). To address the weaknesses of previous generations, each successive generation has been developed with improvements. Fifth Generation (5G) is the latest technology in wireless communication cellular systems, providing the highest internet speeds to everyone, anywhere, anytime. 5G has attracted many global researchers, universities, and research institutes to invent new techniques for enhancement in channel capacity and spectrum utilization of communication systems. This paper describes the evolution of cellular networks, expectations from 5G, the challenges and advantages of 5G, and the techniques on which 5G relies.

Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks

Cognitive radio networks have emerged as a promising solution to the growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling secondary users to opportunistically access underutilized frequency bands while avoiding harmful interference to primary users, cognitive radio networks offer the potential for more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and channel selection, where uncertainties and variations in the radio frequency spectrum are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to handle the inherent imprecision and ambiguity in the decision-making process. Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum opportunities by considering the “fuzziness” in the received signal strength and noise conditions.

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems

Through modeling the characteristics of wireless transmission channels, channel estimation can improve signal detection and demodulation techniques, enhance the spectrum utilization, optimize communication performance, and enhance the quality, reliability, and efficiency of intelligent wireless communication systems. In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems. At first, the channel time‐frequency response matrix between the transmitter and receiver can be represented as 2D images. Then they are fed into the convolutional autoencoder to learn key channel features. To reduce the structural complexity of the deep learning model and improve its inference efficiency, we adopt the method of removing redundant parameters to achieve model compression. Iterative training and pruning based on stochastic gradient descent (SGD) and weight importance evaluation are alternated to obtain a lightweight deep learning model for channel estimation. Finally, extensive simulation results have verified the effectiveness and superiority of the proposed method.

Hybrid spectrum sensing enhancement for cognitive radio in 6G radio system

This article highlights the potential advantages of the proposed hybrid algorithm for 6G. The algorithm's adaptability addresses the dynamic spectral characteristics of 6G environments, enabling seamless transition between different spectral conditions. The hybrid approach holds promise for enhancing spectrum utilization, reducing interference, and optimizing overall communication system performance. As 6G technology evolves, the integration of Energy Detection and Cyclostationary Spectrum Sensing through the hybrid algorithm offers a glimpse into innovative techniques that can shape the future of wireless communication, unlocking the potential for enhanced connectivity, efficiency, and user experience. Several parameters such as Probability of detection (Pd), Probability of false alarm (pfa), Biter error rate (BER) and Power spectral density (PSD) are compared and analysed with the conventional spectrum sensing algorithms. It is seen that the proposed hybrid algorithms obtained a substantial detection performance at the low Signal to noise ratio (SNR).

DRL-based intelligent resource allocation for physical layer semantic communication with IRS

Semantic communication and intelligent reflection surface (IRS) are considered to be promising technologies to solve the scarce spectrum resource problem for the sixth-generation (6G) communication networks. However, there is few research on semantic resource allocation for IRS-enhanced communication networks, which leverages the efficient spectrum utilization of both semantic communication and IRS. In this paper, the resource allocation problem in the IRS-assisted semantic communication network is investigated, and effective semantic spectral efficiency (ES-SE) is defined considering desired semantic similarity for downstream semantic tasks. For the purpose of maximizing the ES-SE, the selection of DeepSCs, allocation of subchannels, reflection array elements of the IRS and transmit beamforming of the base station (BS) are jointly optimized. Considering the necessity of real-time performance and full-link intelligence, a two-stage intelligent approach using dueling double deep Q networks (D3QN)-soft actor critic (SAC) is proposed to tackle the tough resource allocation problem with non-linear programming and coupled variables. Simulation results validate the effectiveness of our designed IRS-assisted semantic communication scheme and demonstrate the superior performance of our proposed intelligent approach.

DataColor: unveiling biological data relationships through distinctive color mapping.

In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.

Smart sensing enabled dynamic spectrum management for cognitive radio networks

Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.

Dynamic Spectrum Assignment in Passive Optical Networks Based on Optical Integrated Microring Resonators Using Machine Learning and a Routing, Modulation Level, and Spectrum Assignment Method

The rising demand for bandwidth in optical communication networks has led to the need for more efficient solutions for spectrum allocation. This article presents a solution to enhance the capacity and efficiency of passive optical networks (PON) using optical microring resonators and dynamic spectrum allocation. The solution relies on wavelength division multiplexing (WDM). It proposes using a support vector machine (SVM) and a Routing, Modulation Level, and Spectrum Assignment (RMLSA) method to manage spectrum allocation based on the bandwidth and distance of multiple requests. The network employs a pulse shaper to physically allocate the spectrum, allowing for the separation of the spectrum generated by the microring resonators into different wavelengths or wavelength ranges (super-channel). Additionally, the SVM and RMLSA algorithms regulate the pulse shaper to execute the allocation. This photonic network achieves improved spectrum utilization and reduces the network blocking probability. Our proposal shows that we successfully addressed 1090 requests with a zero blocking probability, accounting for 81% of the total requests. These request scenarios can simultaneously accommodate up to 200 requests, with a maximum bandwidth of 31 THz. This highlights the efficacy of our approach in efficiently managing requests with substantial processing capacity.

Enhancing solar spectrum utilization in photosynthesis: exploring exciton and site energy shifts as key mechanisms

Photosynthesis is a critical process that harnesses solar energy to sustain life across Earth's intricate ecosystems. Central to this phenomenon is nuanced adaptation to a spectrum spanning approximately from 300 nm to nearly 1100 nm of solar irradiation, a trait enabling plants, algae, and phototrophic bacteria to flourish in their respective ecological niches. While the Sun’s thermal radiance and the Earth’s atmospheric translucence naturally constrain the ultraviolet extent of this range, a comprehension of how to optimize the utilization of near-infrared light has remained an enduring pursuit. This study unveils the remarkable capacity of the bacteriochlorophyll b-containing purple photosynthetic bacterium Blastochloris viridis to harness solar energy at extreme long wavelengths, a property attributed to a synergistic interplay of exciton and site energy shift mechanisms. Understanding the unique native adaptation mechanisms offers promising prospects for advancing sustainable energy technologies of solar energy conversion.