Spectrum Management Research Articles

Spectrum Sensing and Resource Allocation for Frequency Hopping Based CRN Using Hazelnut Tree Search Algorithm

This paper presents a novel spectrum sensing and resource allocation method for Cognitive Radio Networks (CRNs) using the Hazelnut Tree Search (HTS) algorithm in a frequency-hopping framework. CRNs enable secondary users (SUs) to utilize unused licensed spectrum without causing interference to primary users (PUs). The proposed Spectrum Sensing and Resource Allocation using Hazelnut Tree Search in CRN (SS-RA-HTS-CRN) method detects PU presence, prompting SUs to switch channels to avoid interference, guided by a designated guide node (GN). The HTS algorithm selects optimal sensing nodes and improves spectrum sensing, enhancing resource allocation reliability. Simulations in NS-3.26 show that SS-RA-HTS-CRN achieves delay reductions of 28.93%, 31.9%, and 22.45%, delivery ratio improvements of 28.21%, 17.41%, and 11.565%, and packet drop rate reductions of 35.4%, 44.3%, and 23.12% compared with Energy-Aware Resource Allocation with Backtracking Search Optimization (SS-RA-BSO-CRN), Spectrum Sensing with Hybrid Particle Swarm and Gravitational Search (SS-hybrid PSOGSA-CRN), and Spectrum Sensing using Modified Spider Monkey Optimization (SS-MSMO-CRN). The proposed SS-RA-HTS-CRN method significantly enhances efficiency, reliability, and adaptability in CRNs, making it a promising solution for dynamic spectrum management.

EPID-20. PRIMARY INTRAMEDULLARY PILOMYXOID ASTROCYTOMA OF SPINAL CORD (PIPASC): A SYSTEMATIC REVIEW OF CASE REPORTS AND CASE SERIES OF TWO DECADES

Abstract BACKGROUND Pilomyxoid Astrocytoma (PMA), new variant of pilocytic astrocytoma, is more aggressive and relatively rare, mostly seen in children and commonly found in brain. Primary intramedullary pilomyxoid astrocytoma of spinal cord (PIPASC) is extremely rare. The distinct aggressive and pathological behavior, poor prognosis and the diversity in response to various treatment modalities makes the PIPASC an oncologic enigma. We aim to evaluate the clinical manifestations, histopathologic spectrum and onco-surgical management of this neoplasm. MATERIALS AND METHODS After an extensive literature search using PubMed Central and Google Scholar, eight case reports and one case series of patients with spinal PA were retrieved and included using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines without applying any filter of time, study type or place. Case presentations of 12 patients were analyzed in this review. RESULTS Pediatric preponderance of the neoplasm (75%) with the mean age of 7.90 ± 4.51 years was noted. Sensory deficits and scoliotic deformity were the most common presenting symptom and sign in 83.33% and 33.33% of patients, respectively. Thoracic spine (50%) was most commonly affected region. MRI showed the most consistent findings of cord compression in 100% cases and the lesion appeared hypointense on T1- weighted images (T1WI) and hyperintense on T2-weighted images (33.33%). The average post treatment survival duration was found to be more than 2 years. CONCLUSIONS Occurrence of PIPASC among pediatric population is rare. They have possibility of extraneural metastasis, can differentiate into more malignant glioma and get recurrent due to which they require a long-term follow-up of the patients. Further studies are required to investigate the utility of chemotherapy and radiotherapy in its management.

Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks.

For any radio frequency (RF) sensor (receiver) to function optimally, the ambient noise field strength, converted to electrical power by the transducer (antenna), must be lower than the in-ternal noise of that sensor. Therefore, knowledge of the expected ambient noise level is essential for the design of sensors for earth observation, atmospheric research, radio astronomy or navigation. The International Telecommunication Union (ITU) provides a model that predicts ambient man-made noise levels, differentiated by frequency, origin and environment. This is entirely empirical model is based on data from the 1960's and 1970's. In recent years, 90,205 noise measurements have been collected to update the model. The analysis of that data set presented here is essential as it shows a pitfall to avoid: despite to size of the data set it is sparce over the parameter space, and unacceptable biases occur when a purely empirical model is based on them. The paper proposes another approach: to create a mathematical model based on physics that can be fine-tuned and validated using these collected measurements, without producing the biases. A revolutionary side effect of such a model would be the linking of two currently isolated domains, that of spectrum management and electromagnetic compatibility.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

Secure Unmanned Aerial Vehicle Communication in Dual-Function Radar Communication System by Exploiting Constructive Interference

In contrast from traditional unmanned aerial vehicle communication via unlicensed spectrum, connecting unmanned aerial vehicles with cellular networks can extend their communication coverage and improve the quality of their service. In addition, the emerging dual-functional radar communication paradigm in cellular systems can better meet the requirements of location-sensitive tasks such as reconnaissance and cargo delivery. Based on the above considerations, in this paper, we study the simultaneous communication and target sensing issue in cellular-connected unmanned aerial vehicle systems. Specifically, we consider a two-cell coordinated system with two base stations, cellular unmanned aerial vehicles, and potential aerial targets. In such systems, the communication security issue of cellular unmanned aerial vehicles regarding eavesdropping on their target is inevitable since the main beam of the transmit waveform needs to point to the direction of the target for achieving a sufficient detection performance. Aiming at protecting the privacy of cellular transmission as well as performing target sensing, we exploit the physical layer security technique with the aid of constructive interference-based precoding. A transmit power minimization problem is formulated with constraints on secure and reliable cellular transmission and a sufficient radar signal-to-interference-plus-noise ratio. By specially designing the transmit beamforming vectors at the base stations, the received signals at the cellular users are located in the decision regions of the transmitted symbols while the targets can only receive wrong symbols. We also compare the performance of the proposed scheme with that of the traditional one without constructive interference. The simulation results show that the proposed constructive interference-based strategy can meet the requirements of simultaneous target sensing and secure communication, and also save transmit power compared with the traditional scheme.

On mitigating reactive jamming with dynamic resource allocation in optical IRS and UAV-assisted FSO-based networks

Free space optics (FSO) offers a promising opportunity to enhance next-generation network’s capacity with its unlicensed spectrum and wide bandwidth. However, jamming attacks, coupled with inherent anomalies in the FSO-based channel, threaten the performance of these networks. This is especially problematic for security-sensitive applications that demand a resilient communication infrastructure. To address this issue, optical intelligent reflecting surfaces (IRS) and unmanned aerial vehicles (UAVs) can provide promising solutions. This work introduces an efficient approach for mirror element assignment in UAV-assisted FSO-based networks, aimed at mitigating reactive jamming attacks while satisfying users’ quality-of-service (QoS) requirements. To ensure network reliability, we formulate an optimization problem that enhances overall network performance by simultaneously allocating resources such as mirror elements with awareness of jamming attacks. The formulated optimization problem is a binary linear programming problem, which is generally NP-hard. To address this, we introduce a batch-based sequential fixing linear programming procedure called the Reactive Jamming-Aware Mirror Element Allocation (RJA-MEA) scheme. This scheme optimally assigns mirror elements to satisfy the users’ rate demands. In this paper, the performance of the RJA-MEA scheme is compared with reference schemes such as Reactive Jamming Unaware-Mirror Element Allocation (RJU-MEA), Reactive Jamming-Aware Equal Mirror Element Allocation (RJA-EMEA), and Reactive Jamming Unaware-Equal Mirror Element Allocation (RJU-EMEA) schemes. The simulation results reveal that the proposed RJA-MEA scheme surpasses existing reference schemes, thereby significantly improving the overall network sumrate performance.

Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

AI Telco Research: Advancements in Telecommunications Scientific Discovery

This article explores the transformative impact of Artificial Intelligence (AI) on telecommunications research and development. It examines how the integration of AI technologies is revolutionizing various aspects of the industry, including network optimization, spectrum management, and scientific discovery. The article highlights the synergy between human expertise and AI capabilities in enhancing research processes, improving data analysis, and driving innovation. Key areas of focus include AI-powered data analysis, network behavior simulation, signal processing enhancements, and the evolving nature of human-AI collaboration in telco research. The article also addresses challenges such as data privacy and algorithmic bias, while emphasizing the potential of AI to unlock new possibilities in telecommunications, from self-optimizing networks to quantum communication systems.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

Identifying risk factors for urologic complications in placenta accreta spectrum surgical management.

To describe urologic complications associated with the surgical management of placenta accreta spectrum and determine their risk factors. A retrospective study was conducted on all patients diagnosed with abnormal invasive placentation who underwent surgery and delivered between 2002 and 2023 at a single expert maternity centre. Intra-operative and post-operative complications were described, with a special focus on urologic intra-operative injuries, including vesical or ureteral injuries. Univariate and multivariate analyses were performed to determine risk factors of intra-operative urologic injuries associated with placenta accreta spectrum surgical management. Additionally, using the Clavien-Dindo classification, the effects of intra-operative urologic injury and ureteral stent placement on post-operative outcome were evaluated. A total of 216 patients were included, of which 47 (21.48%) had an intra-operative bladder and/or ureteral injury. Placenta percreta was associated with a higher rate of intra-operative urologic injury than placenta accreta (72.34% vs. 6.38%, p < 0.001). Multivariate analyses showed that patients who had placenta percreta and bladder invasion or emergency hysterectomy were associated with more intra-operative urologic injuries (OR = 8.07, 95% CI [2.44-26.75] and OR = 3.87, 95% CI [1.09-13.72], respectively). Patients with intra-operative urologic injuries had significantly more severe post-operative complications, which corresponds to a Clavien-Dindo score of 3 or more, at 90 days (21.28% vs. 5.92%, p = 0.004). Surgical management of placenta accreta spectrum is associated with significant urologic morbidity, with a major impact on post-operative outcomes. Urologic complications seem to be correlated with the depth of invasion and the emergency of the hysterectomy.

An efficient beam sweeping scheme with backup paging occasions in NR-Unlicensed Spectrum

With the increasing demand of user applications and the shortage of cellular spectrum, New Radio (NR) access to Unlicensed Spectrum (NR-U) is getting significant attention. Listen Before Talk (LBT) procedure is used to sense unlicensed channel availability prior to transmission. The transmissions are subjected to LBT procedure failure, which brings extra delay as they are postponed to the next available transmission opportunity. This excess delay also affects time-critical procedures, such as paging. The directional communication in NR requires beamforming, which further complicates paging procedure as additional paging message transmissions are needed to cover all the beams. In this work, we propose an NR-U paging mechanism (called FF_BP) consisting of Full and Fast Paging Cycles with Backup Paging Occasions (POs). Full Paging uses a normal NR-U paging cycle and broadcasts a paging message on all the beams. On the other hand, Fast Paging, which targets delay-sensitive mobile users, uses a shorter paging cycle and broadcasts a paging message on selected beams only. The Backup POs deal with LBT procedure failure impairments while paging broadcast on selective beams reduces paging resource usage. We analyze FF_BP using a discrete-time semi-Markov chain model and validate the model by extensive simulations. The simulation results show that FF_BP outperforms several baseline mechanisms in terms of average paging delay and paging resource usage.

Transitioning from Administrative to Market-Based Spectrum Management in Sri Lanka: A Critical Analysis

Purpose: In Sri Lanka, the Radio Frequency Spectrum (RF spectrum) has traditionally been overseen managed and regulated by the government regulatory body(s), mainly accommodating the administrative RF spectrum management regime. This approach entails allocating and assigning RF spectrum to licensed operators solely through administrative means, with the regulatory authority having sole control and rights over it. However, in line with global trends, in recent years, many countries have begun to transition towards market-based approaches to RF spectrum management from legacy administrative regime, aiming to improve efficiency, promote innovation, and foster competition in the telecommunications sector. Currently, the regulatory landscape in Sri Lanka is also exploring the transition towards a market-oriented spectrum management regime. In this context, this legal research article critically analyses the viability and appropriateness of the transition from administrative RF spectrum management to market-based RF spectrum management in Sri Lanka in a regulatory context, highlighting various dimensions to be considered while migrating. Design/methodology/approach: In this research paper, the authors have considered the regulatory aspects of the transition from an administrative to a marked-based approach for the RF spectrum in Sri Lanka. Hence, the authors shall be carrying out a legal research using the doctrinal research methodology, which is commonly used in legal research. This research article considers John A. Posner’s law and economic theory and John Lock’s property rights theory as the theoretical foundation. Further, this research paper uses qualitative research methodology. By using the said methodological approach, the authors analyse the current administrative RF spectrum management regime in Sri Lanka comparing it with global trends towards market-based approach and thereby evaluate the primary objectives behind the transition, the current RF spectrum framework, and the anticipated challenges that they would encounter with the transition. Findings: The analysis revels that RF spectrum management in Sri Lanka under the administrative system is restrictive and hinders innovation and competition, compared to a more liberalized market-based approach. A market-based approach, characterized by auctions, in primary markets and spectrum sharing, trading, and leasing in secondary markets identified as a potential solution to these limitations. However, the study also highlights several challenges, including the need for regulatory reform, capacity building, and the development of a robust legal framework to support the transition. Originality: This paper contributes to the existing body of knowledge by providing a detailed analysis of the potential shift in RF spectrum management practices within Sri Lanka, in the regulatory context, a subject area that has not been extensively explored in the local context. It offers a comparative perspective with international practices and provides actionable recommendations tailored to Sri Lankan conditions. Implications: The findings suggest that transitioning to a market-based RF spectrum management regime could significantly improve the efficiency and dynamism of Sri Lanka's telecommunications sector. However, careful planning and implementation are crucial to address potential challenges and ensure that the transition supports long-term goals of innovation and competition. The recommendations aim to guide policymakers in developing a framework that balances market efficiency with regulatory oversight.

Energy‐efficient channel selection algorithm with reduced collision probability in cognitive radio networks

AbstractThe growing demand for radio spectrum, driven by widespread communication devices, has led to spectrum congestion. Cognitive radio networks (CRNs) offer a solution by allowing secondary users (SUs) to access primary users' (PUs) channels without causing interference. This paper presents an energy‐efficient channel selection algorithm aimed at reducing collision probability and energy consumption during spectrum sensing. A prediction‐based model forecasts PU channel availability, enabling selective sensing. The results show a significant reduction in energy consumption, with the proposed method consuming 57.9% energy compared to 63.7% for a recent existing method. The proposed approach enhances spectrum management in next‐generation wireless systems by minimizing interference and optimizing energy use.

Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing.

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.

Integrated prediction model before conservative management of placenta accreta spectrum : hospital based cohort study.

Integrated prediction model before conservative management of placenta accreta spectrum : hospital based cohort study.

Legitimacy considerations in regulation for local mobile communication network business in Finland and the UK

The article explores the legitimacy of local 5G and upcoming 6G mobile communication networks in Europe. The paper paves the way for a new perspective for understanding the legitimacy of local 5G/6G mobile communication networks by applying the business model-based ecosystem perspective and legitimacy as a theoretical foundation. As regulations are expanding to consider the novel deployment model of local 5G and 6G mobile communication networks, the legitimacy of such networks is under-explored. Analyzing the regulatory approaches taken by two national regulatory authorities (NRA) in Europe, Finland and the UK, we identify seven legitimacy-related themes including innovation orientation, awareness and knowledge of how to build and design local networks, European harmonized spectrum management, competition, cybersecurity and risk assessment and equipment availability. Our study lays the groundwork for exploring how the application of the business model-based ecosystem perspective can be utilized to advance regulatory initiatives for local mobile communication network businesses. It also identifies which legitimacy-related aspects are involved in setting regulatory priorities.

Unlicensed Spectrum Access and Performance Analysis for NR-U/WiGig Coexistence in UAV Communication Systems

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

Modeling and Performance Analysis of LBT-Based RF-Powered NR-U Network for IoT.

Energy harvesting combined with spectrum sharing offers a promising solution to the growing demand for spectrum while keeping energy costs low. New Radio Unlicensed (NR-U) technology enables telecom operators to utilize unlicensed spectrum in addition to the licensed spectrum already in use. Along with this, the energy demands for the Internet of Things (IoT) can be met through energy harvesting. In this regard, the ubiquity and ease of implementation make the RF-powered NR-U network a sustainable solution for cellular IoT. Using a Markov chain, we model the NR-U network with nodes powered by the base station (BS). We derive closed-form expressions for the normalized saturated throughput of nodes and the BS, along with the mean packet delay at the node. Additionally, we compute the transmit outage probability of the node. These quality of service (QoS) parameters are analyzed for different values of congestion window size, TXOP parameter, maximum energy level, and energy threshold of the node. Additionally, the effect of network density on collision, transmission, and energy harvesting probabilities is observed. We validate our model through simulations.

Evaluating the Effectiveness of 5G Technology in Enhancing Mobile Network Performance in South Korea

Purpose: The aim of the study was to evaluate the effectiveness of 5G technology in enhancing mobile network performance in South Korea. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: South Korea's adoption of 5G technology has significantly enhanced mobile network performance. The deployment of 5G has resulted in vastly improved download and upload speeds, reduced latency, and better network reliability compared to previous technologies. This advancement has enabled smoother and more reliable mobile internet experiences, facilitating new applications such as augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) devices. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) &amp; resource-based view (RBV) theory may be used to anchor future studies on evaluating the effectiveness of 5G technology in enhancing mobile network performance in South Korea. Develop practical guidelines for telecom operators on the deployment and optimization of 5G networks. These guidelines should address best practices for infrastructure development, spectrum management, and network configuration to maximize performance. Advocate for the establishment of comprehensive regulatory frameworks that support the widespread deployment of 5G technology.