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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, with the promising assumption of using adaptive directional microstrip antenna on user equipment, the problem of joint antenna selection, spectrum assignment, and transmit power allocation for mm‐wave device‐to‐device communications underlying cellular networks is tackled, with the goal of enhancing system throughput and energy efficiency. To address the complexity of this problem, a method based on multi‐agent distributed deep reinforcement learning in which an autonomous intelligent agent is deployed for each user equipment is proposed. The performance evaluation demonstrates its superiority over existing strategies, resulting in improved system performance, reduced outage probability, and enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm's flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman's coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization. [ABSTRACT FROM AUTHOR]

Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment of monitoring devices leads to huge costs of operation and maintenance. It indicates that an approach is needed to reduce the number of monitoring devices, but prevent the degradation of data granularity. Therefore, this paper focuses on the accurate construction of the spectrum map. It aims to infer the fine‐grained spectrum situation of the target region based on coarse‐grained observation. In order to solve this problem, an inference framework based on deep residual network is developed in this paper. In the case of rule deployment for sensing nodes, it adopts the idea of super resolution to improve the accuracy of the spectrum map. The framework is composed of two major parts: an inference network, which generates fine‐grained spectrum maps from coarse‐grained counterparts by using feature extraction module and upsampling construction module; and a fusion network, which considers the influence of environmental factors to further improve the performance. A large number of experiments on simulated datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity. [ABSTRACT FROM AUTHOR]

Purpose: To describe urologic complications associated with the surgical management of placenta accreta spectrum and determine their risk factors. Methods: 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. Results: 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). Conclusion: 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. [ABSTRACT FROM AUTHOR]

The development of space-based Internet of Things is limited by insufficient allocable frequency resources and low spectrum utilization. To meet the demand for massive access users under the condition of restricted frequency resources, a multi-dimensional hybrid multiple-access method for space-time-frequency-code division based on user distribution (MHSTFC-UD) is established. It divides the beam cell of a low orbit satellite into the central and edge area and dynamically adjusts the radius of the central area and the allocation of frequency resources according to the distribution of users. The optimization model for the radius of the central area and the allocation of frequency resources is established and solved by the genetic algorithm. Also, it takes the edge area as the protection interval to realize the full-frequency multiplexing between the beam cells in the time domain, space domain and code domain. The simulation results show that compared with the traditional method of frequency reuse in two or three dimensions, the multi-dimensional hybrid multiple-access method can improve the maximum access capacity of a single satellite user by one to three orders of magnitude. Moreover, the MHSTFC-UD can increase users by an additional 11.5% to 33.1% compared to fixed area division and frequency resource allocation. [ABSTRACT FROM AUTHOR]

Objective: To conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of international collaboration, tracking the emergence of research hotspots, and detecting trends through keyword bursts. Methods: In this bibliometric study, we identified and retrieved publications on AI applications in RDs spanning 2003 to 2023 from the Web of Science (WoS). We conducted a global research landscape analysis and utilized CiteSpace to perform keyword clustering and burst detection in this field. Results: A total of 1501 publications were included in this study. The evolution of AI applications in RDs progressed through three stages: the start-up period (2003–2010), the steady development period (2011–2018), and the accelerated growth period (2019–2023), reflecting this field's increasing importance and impact at the time of the study. These studies originated from 85 countries, with the United States as the leading contributor. "Mutation", "Diagnosis", and "Management" were the top three keywords with high frequency. Keyword clustering analysis identified gene identification, effective management, and personalized treatment as three primary research areas of AI applications in RDs. Furthermore, the keyword burst detection indicated a growing interest in the areas of "biomarker", "predictive model", and "data mining", highlighting their potential to shape future research directions. Conclusions: Over two decades, research on the AI applications in RDs has made remarkable progress and shown promising results in the development. Advancing international transboundary cooperation is essential moving forward. Utilizing AI will play a more crucial role across the spectrum of RDs management, encompassing rapid diagnosis, personalized treatment, drug development, data integration and sharing, and continuous monitoring and care. [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]

With the large-scale distributed energy resources (DERs) interfaced by power electronic converters connected to the power grid, the traditional synchronous generation is gradually replaced, and the inertia and primary frequency regulation capability of the power system is weakened, which seriously threatens the frequency security of the power grid. In view of the above problems, a hierarchical control strategy by coordinating the distributed wind turbine (WT) and photovoltaic (PV) is presented to improve the transient frequency (TF) and steady-state frequency (SSF). For the system control level, the optimal frequency regulation coefficients of multiple WTs and PVs clustering systems are determined by the optimal control principle with minimum frequency regulation energy, while considering the constraints of TF and SSF. For the WT/PV control level, the frequency regulation coefficients of WTs and PVs are solved by the model predictive controller (MPC) and alternating direction method of multipliers (ADMM), with the minimum control cost of WT and PV. It achieves the optimal allocation of frequency regulation power and improves the computing efficiency. Finally, the validity of the proposed method is tested in the modified IEEE 10-machine 39-bus system. [ABSTRACT FROM AUTHOR]

We introduce and study conflict-free colourings of t-subsets in hypergraphs. In such colourings, one assigns colours to all subsets of vertices of cardinality t such that in any hyperedge of cardinality at least t there is a uniquely coloured t-subset. The case t = 1 , i.e., vertex conflict-free colouring, is a well-studied notion that originates in the context of frequency allocation to antennas in cellular networks. It has caught the attention of researchers both from the combinatorial and algorithmic points of view. A special focus was given to hypergraphs arising in geometry. Already the case t = 2 (i.e., colouring pairs) seems to present a new challenge. Many of the tools used for conflict-free colouring of geometric hypergraphs rely on hereditary properties of the underlying hypergraphs. When dealing with subsets of vertices, the properties do not pass to subfamilies of subsets. Therefore, we develop new tools, which might be of independent interest. For any fixed t, we show that the n t t-subsets in any set P of n points in the plane can be coloured with O (t 2 log 2 n) colours so that any axis-parallel rectangle that contains at least t points of P also contains a uniquely coloured t-subset. For a wide class of "well-behaved" geometrically defined hypergraphs, we provide near tight upper bounds on their t-subset conflict-free chromatic number. For t = 2 we show that for each of those "well-behaved" hypergraphs H, the hypergraph H ′ , whose hyperedges are obtained by taking all the unions of two hyperedges from H, admits a 2-subset conflict-free colouring with roughly the same number of colours as H. For example, we show that the n 2 pairs of points in any set P of n points in the plane can be coloured with O (log n) colours such that for any two discs d 1 , d 2 in the plane with | (d 1 ∪ d 2) ∩ P | ≥ 2 there is a uniquely (in d 1 ∪ d 2 ) coloured pair. We also show that there is no general bound on the t-subset conflict-free chromatic number as a function of the standard conflict-free chromatic number already for t = 2 . [ABSTRACT FROM AUTHOR]

In order to solve the problem of frequency modulation power deviation caused by the randomness and fluctuation of wind power outputs, a method of auxiliary wind power frequency modulation capacity allocation based on the data decomposition of a "flywheel + lithium battery" hybrid-energy storage system was proposed. Firstly, the frequency modulation power deviation caused by the uncertainty of wind power is decomposed by the successive variational mode decomposition (SVMD) method, and the mode function is segmented and reconstructed by high and low frequencies. Secondly, a mathematical model is established to maximize the economic benefit of energy storage considering the frequency modulation mileage, and quantum particle swarm optimization is used to solve the target model considering the charging and discharging power of energy storage and the charging state constraints to obtain the optimal hybrid-energy storage configuration. Finally, the simulation results show that, in the step disturbance, the Δfmax of the hybrid-energy storage mode is reduced by 37.9% and 15.3%, respectively, compared with single-energy storage. Under continuous disturbance conditions, compared with the single-energy storage mode, the Δfp_v is reduced by 52.73%, 43.72%, 60.71%, and 47.62%, respectively. The frequency fluctuation range is obviously reduced, and the frequency stability is greatly improved. [ABSTRACT FROM AUTHOR]

Specific emitter identification technology plays a very important role in spectrum resource management, wireless network security, cognitive radio etc. However, in complex electromagnetic environments, the variability and uncertainty of signals make it very difficult to extract representative feature representations of the signals. At the same time, the feature extraction capability of the recognition model is also a factor that needs to be considered. To address these issues, a wavelet residual neural network model based on attention mechanism is proposed for specific emitter identification. First, multi‐level wavelet decomposition is performed on all received signals to obtain their wavelet detail coefficients at different scales. Next, all the wavelet detail coefficients are used as the feature input for the attention‐based residual network, and perform parallel feature extraction at multi scales. Finally, the feature representation capability of all coefficients are compared, and the model's recognition results based on it are obtained. The recognition rates on the three datasets are 94.7%, 93.21%, and 86.1%, respectively, all of which are superior to recent state‐of‐the‐art algorithms. In addition, through ablation experiment, the validity of each component of the model has been verified. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has been proposed as an emerging distributed machine learning framework, which lowers the risk of privacy leakage by training models without uploading original data. Therefore, it has been widely utilized in the Industrial Internet of Things (IIoT). Despite this, FL still faces challenges including the non-independent identically distributed (Non-IID) data and heterogeneity of devices, which may cause difficulties in model convergence. To address these issues, a local surrogate function is initially constructed for each device to ensure a smooth decline in global loss. Subsequently, aiming to minimize the system energy consumption, an FL approach for joint CPU frequency control and bandwidth allocation, called FCBAFL is proposed. Specifically, the maximum delay of a single round is first treated as a uniform delay constraint, and a limited-memory Broyden-Fletcher-Goldfarb- Shanno bounded (L-BFGS-B) algorithm is employed to find the optimal bandwidth allocation with a fixed CPU frequency. Following that, the result is utilized to derive the optimal CPU frequency. Numerical simulation results show that the proposed FCBAFL algorithm exhibits more excellent convergence compared with baseline algorithm, and outperforms other schemes in declining the energy consumption. [ABSTRACT FROM AUTHOR]

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Summary: Signal‐to‐noise ratio (SNR) estimation is crucial for spectrum management and data transmission. However, the existing classical methods in satellite‐to‐ground (SG) communication links, particularly for broadband transmission and under ultra‐low SNR conditions, often encounter substantial estimation errors. In this paper, a novel SNR estimation method based on time‐domain channel impulse response (CIR) reconstruction is proposed. Least square (LS) algorithm in frequency domain and inverse fast Fourier transform (IFFT) with a rectangular window are employed to reconstructed CIR. The noise energy is calculated by computing the average energy outside the window. The signal power is obtained by subtracting the noise energy from the total energy inside the window. In addition, a numerical simulation with a signal bandwidth of 400 MHz is performed to evaluate the effectiveness of the proposed algorithm in real SG communication scenarios. The simulation results show that compared with existing classical methods, even under ultra‐low SNR conditions, the proposed algorithm exhibits more accurate estimation ability and stronger resistance to frequency offset interference in nonterrestrial network (NTN) channels. [ABSTRACT FROM AUTHOR]

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings. [ABSTRACT FROM AUTHOR]

Summary Owing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing. [ABSTRACT FROM AUTHOR]

Europe's semi-natural grasslands support notably high levels of temperate biodiversity across multiple taxonomic groups. However, these ecosystems face unique conservation challenges. Contemporary agricultural practices have replaced historical traditional low-intensity agriculture in many regions, resulting in a spectrum of management intensities within these ecosystems, ranging from highly intensive methods to complete abandonment. Paradoxically, both extremes along this spectrum of management intensity can be detrimental to biodiversity of semi-natural grasslands. Moreover, while anthropogenic climate change is an overarching threat to these ecosystems, rapid changes in land use and its intensity often present more immediate pressures. Often occurring at a faster rate than climate change itself, these land-use changes have the potential to rapidly impact the biodiversity of these grasslands. Here, we divide the ecological processes, threats, and developments to semi-natural grasslands into three sections. First, we examine the different impacts of agricultural intensification and abandonment on these ecosystems, considering their different consequences for biodiversity. Second, we review seminal works on various evidence-based management practices and offer a concise summary that provides support for various conservation and management strategies. However, the socio-economic factors that drive both abandonment and intensification in semi-natural grasslands can also be used to develop solutions through strategic governmental and non-governmental interventions. Accordingly, we conclude with a way forward by providing several key policy recommendations. By synthesizing existing knowledge and identifying research gaps, this essay aims to provide valuable insights for advancing the sustainable management of semi-natural grasslands. The biodiversity of Europe's semi-natural grasslands is threatened by contemporary agricultural intensification and abandonment. Shipley et al. explore these threats, review management practices, and offer policy recommendations to support sustainable conservation efforts. [ABSTRACT FROM AUTHOR]

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input–multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time–frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation. [ABSTRACT FROM AUTHOR]

Current cutting-edge solutions to the spectrum shortage problem are unable to meet the growing demand for a limited spectrum. A key dimension beyond state-of-the-art solutions is to exploit the free spectrum more effectively. Although various schemes have been proposed for trading spectrum, few studies have focused on optimal admission of spectrum requests for maximizing service providers' (SP's) profit. Thus, this timely study presents a novel intelligent admission scheme for spectrum requests from the perspective of a non-cooperative game, in which the information of all participants (customers and providers) is incomplete to others, and each player wishes to maximize its benefit. The proposed control admission policy may evict clients in-service to release spectrum for serving certain, e.g., wealthy clients. Evicted clients are compensated using a dynamic strategy that adopts greedy game theory to capture the conflict of interest between SP and evicted users. Simulation experiment results validate and demonstrate the feasibility and efficiency of the proposed scheme, compared to a benchmark reinforcement learning approach and another widely used scheme for admission and eviction control of cognitive radio users. [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]

Background: Immune‐checkpoint inhibitors (ICIs) often cause immune‐related adverse events (irAEs). The spectrum of irAEs and their managements has been partially clarified, however the knowledge on time‐course of irAEs is not well understood. Methods: A retrospective study based on the medical record was performed. The study subjects were consisting of patients with various types of solid tumors for whom ICIs (nivolumab, pembrolizumab, durvalumab, atezolizumab, nivolumab plus ipilimumab) were used between April 2016 and October 2021. We focused on irAEs developed more than 1‐year after commencement ICIs (delayed irAE group) and compared with irAEs developed within 1‐year (non‐delayed irAE group) in terms of types and severity of irAEs. Results: A total of 336 patients were enrolled in the study. Eighty‐eight patients (26.2%) developed irAEs and 248 did not. Most of the patients developing irAEs were treated using PD‐L1/PD‐1 inhibitors. Eighty‐one patients (24.1%) in non‐delayed irAE group and 7 patients (2.1%) in delayed irAE group developed irAEs. The median onset of irAEs in the delayed irAE group was 18.6 months (range: 13.5–24.3). The types of irAEs observed in delayed irAE group were dermatitis (2 cases), pneumonitis (2 cases), nephritis (1 case), arthritis (1 case), and gastritis (1 case). The severity of irAEs was almost mild (≤G2), but one patient (.3%) developed G3 nephritis. Conclusion: PD‐L1/PD‐1 inhibitors frequently caused various irAEs but their severities were mostly tolerable. Few patients developed delayed irAE with mild toxities. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Open Radio Access Networks (Open RAN), the dynamic and unpredictable nature of network conditions presents significant challenges for traditional spectrum allocation strategies. This paper introduces an innovative framework that leverages Federated Learning (FL) to refine and enhance spectrum allocation within Open RAN. Utilizing the decentralized architecture of FL, our model introduces a system that is not only more adaptive to real-time changes but also offers enhanced robustness for spectrum management. We delve into the advantages of this approach, such as significant improvements in data traffic management, latency reduction, and overall network capacity enhancement. Additionally, we address potential implementation challenges, providing strategic countermeasures to ensure the successful deployment of our FL-based framework. Through this exploration, our paper underscores the transformative potential of integrating FL with Open RAN, marking a significant step forward in the application of AI technologies for optimizing wireless communication networks. This contribution opens new avenues for research in AI-driven spectrum allocation, setting a foundation for future empirical validations and the development of more efficient, intelligent telecommunication infrastructures. [ABSTRACT FROM AUTHOR]

In multi-user cognitive-radio internet of things (CR-IoT) network, accurate estimations of data arrival are critical for secondary users to allocate channels. In the context of the data arrival model with long-term variations of rate, improving the accuracy of the performance evaluation of channel allocation protocols is an open issue. Thus, to evaluate the performance of various channel allocation protocols with predefined models of data arrival, a queuing analysis framework is developed using a probability allocation vector (PrA). The time-varying feature of data arrival is described by a Markov process including various data arrival states in the proposed framework. A dynamic probability allocation vector (DPrA) protocol capable of adjusting allocation strategy according to the arrival states by constructing the PrA is proposed. For comparative analysis, a maximum throughput allocation (MTA) protocol for conventional data arrival model is also evaluated under the proposed framework. Numerical results show that the DPrA protocol outperforms the MTA protocol in various performance metrics. Furthermore, the proposed modeling method for data traffic can provide convenience and effectiveness when designing channel allocation protocols in a CR-IoT network. [ABSTRACT FROM AUTHOR]

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations.

Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications. [ABSTRACT FROM AUTHOR]

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks. [ABSTRACT FROM AUTHOR]

Background: A parotid abscess (PA) is a complication of an acute bacterial parotitis with a potentially life-threatening course. To date, data on the diagnosis and therapy of PA is sparse and mostly consists of case reports or case series. Therefore, this study aimed at comprehensively analyzing the microbiological spectrum and the therapeutic management in a bi-institutional setting. Methods: A retrospective clinical chart review was performed to identify all patients surgically treated for PA at two tertiary care centers in Germany. Data on demographics, clinical management and microbiological data including species identification, pathogenicity, type of antibiotic therapy, adjustment of antibiotics, antibiotic sensitivity testing, and smear test results were extracted. Intervention-related variables and etiology were analyzed for their statistical association with outcome variables. Results: Overall, 85 patients were included. Most patients (92.9%) underwent surgical incision. Around half of the patients (45.9%) were treated under local anesthesia. No facial nerve palsy was observed. The most frequently detected pathogens were Streptococci (n = 23), followed by Staphylococcus aureus (n = 6) including one case of methicillin-resistant Staphylococcus aureus. Most patients (68.2%) received an aminopenicillin ± beta-lactamase inhibitor as empiric antibiotic therapy. In 6 cases the antibiotic therapy was modified after receiving the antibiogram. Four patients (5.2%) presented with recurrent PA. Etiology was idiopathic (42.4%), followed by tumorous (12.9%), obstructive, and immunosuppressive (each 11.8%). Patients with a dental focus (p = 0.007) had a longer duration of hospitalization. Conclusion: The results show that the surgical therapy of PA under local anesthesia is safe. A dental examination should routinely be performed to rule out a dental focus. Obtaining a microbiological specimen in order to modify antibiotic therapy if necessary and a histopathological specimen to rule out a tumorous etiology is obligate. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system's response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios. [ABSTRACT FROM AUTHOR]

Cognitive satellite networks can enhance spectrum efficiency by enabling satellite networks to dynamically share idle spectrum with terrestrial networks. However, when taking into consideration the inter-cell or internet interferences in various application circumstances, dynamic spectrum optimization in cognitive satellite networks remains challenging due to the diversified QoS demands of secondary terrestrial users. To address this issue, this paper proposes an efficient spectrum allocation algorithm which is based on graph coloring technique and combined with analytic hierarchy process so as to meet the requirements of dynamic spectrum allocation to secondary terrestrial users with diversified QoS demands and yet without causing severe inference to primary satellite users. In this paper, we combine graph coloring algorithm and analytic hierarchy process so as to assign the heterogeneous spectrum bands to secondary users in an orderly manner. The method of graph coloring is applied to control obvious interference caused by the secondary users. We also provide numerical results to evaluate the impacts of different parameter settings on the performances of the proposed solution. [ABSTRACT FROM AUTHOR]

Considering the growth potential of ultra wide band (UWB), the government revised the technical requirements in November 2022 to enable services to use the technology through mobile communication devices such as smartphones. This study estimates the economic impact of an unlicensed spectrum management policy by analyzing cases of technical requirement revisions. Through inter-industry analysis, the revision of technical requirements is expected to induce a production effect of KRW 7.1787 trillion and an added-value effect of KRW 2.2151 trillion over the next 10 years. As an unlicensed spectrum is expected to be utilized in various industrial fields, regulators are implementing active management policies. However, analyses of the contribution of these policies to the national economy are lacking. This study not only addresses this academic gap but also provides foundational data for market-based spectrum management policies based on economic feasibility. [ABSTRACT FROM AUTHOR]

This study reviewed the legal and regulatory improvements implemented by the International Telecommunication Union (ITU) and major countries in response to the evolving radio environments due to the increase in the number of non-geostationary satellite networks. In addition, domestic regulations, such as the radio waves act and radio regulations related to geostationary and non-geostationary satellite networks, were examined. The proposed improvements to these regulations are discussed, considering the technical characteristics of satellite networks and spectrum management. [ABSTRACT FROM AUTHOR]

Changes to US wildfire policy in 2009 blurred the distinction between fires managed for resource benefits and fires with primarily suppression objectives, making management strategies difficult to track. Here, qualitative text is coded from a sample of 282 Wildland Fire Decision Support System Relative Risk Assessments completed on wildfires between 2010 and 2017 to examine the prevalence of different strategies and their associations with risk. Suppression is used most, associated with high risk. Managers discuss intent to suppress even when it is untenable. Monitoring, confine, or point protection are used much less commonly and when risk is low. The Southwest region discusses a diversity of strategies, leveraging landscape barriers from past management to support them; the Northwest discusses suppression or monitoring and rarely links strategy selection to barriers. Based on associations between physical barriers to fire spread, risk, and strategy, creating more barriers may provide a path forward to better implement fire policy. Study Implications: Systematic analysis of text data in wildfire decision documents provides insights into how fires are managed. Most wildfires are still aggressively suppressed despite federal fire policy promoting the use of fire to enhance resources. When managers discuss risk during wildfires, it is evident that physical barriers to fire spread (e.g. rivers, roads, trails, rocky scree), including mechanical fuel treatments, prescribed fires, and previous wildfires, are important factors in operational fire planning. However, management strategies promoting the use of wildfire to enhance ecological resiliency or reduce transmission of future fires to values are used sparingly. Southwest fire managers are relying on past wildfires, fuel treatments, and prescribed fires more so than the Northwest to engage in a full spectrum of fire management strategies. This finding suggests that, at least in some geographies, ongoing investments in fuels management will pay dividends in reducing risk and broadening opportunities to meet federal policy goals. [ABSTRACT FROM AUTHOR]

Summary: Modern satellite communication systems are designed to serve dispersed users with changing operational requirements. Allocating resources to meet these requirements becomes highly complex when the size, location, and allocated frequency of satellite beams are flexible. We developed solution techniques that generate feasible beam patterns and frequency allocations to maximize the total demand met across all users. In our approach, an integer program selects an optimal set of beams from a heuristically generated pool of feasible candidate beams. The challenge is in how to efficiently build a pool of good quality candidate beams from exponentially many possible solutions. An innovative column generation‐style heuristic to generate mathematically justifiable beams is presented to address this challenge. We also derived two other heuristic candidate beam generation algorithms to compare and contrast performance and robustness characteristics of different algorithmic choices. We tested the performance of our three new approaches on 12 operational instances that vary in user distribution, user numbers, and demand distribution. While the methods performed differently under varying operational scenarios, the column generation‐based methods provided the best trade‐off between computation time and solution quality in most cases. We further tested our two best performing algorithms for scalability. Our column generation‐based methods were able to provide good quality solutions with up to 400 beams and 5,000 users. Our work provides valuable insights for real‐life implementation: an end‐user of our system can select the solution approach based on their business need, computational (time) budget, and the desired solution quality. [ABSTRACT FROM AUTHOR]

Internet-of-Vehicles (IoV) plays an important part in Intelligent Transportation Systems and is widely regarded as one of the most strategic applications in smart city development. Smart spectrum resource management has received much attention from the research community as it is believed to be a promising approach for solving the spectrum resource challenge of IoV and Intelligent Transportation Systems. In this article, we propose a dynamic spectrum optimization technique based on a deep learning method for user mobility prediction. For this purpose, based on the Exploration and Preferential Return (EPR) model which can be used to investigate the movement trend and aggregation behavior of the target, we adopt the D-Exploration and Preferential Return (D-EPR) model as a deep learning technique to train a Long-Short Term Memory (LSTM) recurrent neural network (RNN) in order to predict the future locations of IoV nodes. With the predicted user's mobility, a graph theoretic algorithm is then applied to achieve spectrum reuse and optimization. Besides, our proposed deep-learning-based user mobility prediction is able to identify the user position. This paper then compares the performances of mobility prediction by traditional methods and our proposal. The outcomes of spectrum efficiency and network capacity are also provided to show the effectiveness of the proposed solution. Numerical results are provided to evaluate the prediction accuracy and network utility of our proposal with various memory histories, user distribution mechanisms and node dwell time. [ABSTRACT FROM AUTHOR]

Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) communication is a promising technology in fifth‐generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and network quality assurance. This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. The proposed algorithm aims to optimize overall throughput and maximize the signal‐to‐interference noise ratio (SINR) while ensuring low computational complexity. The study introduces the following two key techniques: staggered training and decentralized execution. Staggered training improves agent performance and minimizes computational complexity by training agents one at a time in a sequential manner. This allows agents to learn from each other's mistakes and avoid local minima. Decentralized execution enhances scalability and system robustness by enabling agents to learn and act independently without relying on communication with other agents. In the event of agent failure, the remaining agents can continue operating. The findings of this work demonstrate a significant improvement in energy efficiency (EE) and an enhancement in the quality of service (QoS) of the network. Overall, the algorithm proves to be a promising solution for resource allocation in multiagent D2D networks, offering notable improvements in EE and QoS while maintaining scalability for large networks. [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]

A method for constructing a feedback matrix to solve the spectrum allocation (spectrum control; pole assignment) problem for a linear dynamical system is given. A new proof of the well-known theorem about the connection between the complete controllability of a dynamical system and the existence of a feedback matrix is formed in the process of constructing the cascade decomposition method. The entire set of arbitrary elements affecting the nonuniqueness of the matrix is identified. Examples of constructing a feedback matrix in the case of a real spectrum and in the presence of complex conjugate eigenvalues as well as for the case of multiple eigenvalues are given. The stability of the specified spectrum under small perturbations of system parameters with a fixed feedback matrix is studied. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology for beyond fifth-generation (B5G) networks to support significant features, such as spectrum efficiency, high data rate, and reduced power consumption. Efficient spectrum allocation and optimum power control in D2D networks is one of the major issues that arise due to dynamic traffic arrivals. This article proposes a novel approach to maximize the spectral efficiency of the D2D network. The formulated problem is a mixed-integer nonlinear programming problem (MINLP). However, due to its complexity, the global optimal solution is difficult to solve directly. A two-stage optimization algorithm is presented: optimal resource allocation algorithm (ORAA) by utilizing the concept of queue dynamics and optimal power control algorithm by adapting the Lyapunov stability method. The proposed method achieves higher spectral efficiency up to 21.6% and latency is minimized up to 39.89% over the benchmark schemes. The proposed method shall find immense use in smart traffic management to support Intelligent transportation systems. [ABSTRACT FROM AUTHOR]

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. [ABSTRACT FROM AUTHOR]

Objective: Management of placenta accreta spectrum (PAS) with the placenta kept in situ aims to preserve fertility and minimize blood loss. However, this method is associated with a risk of coagulopathy and subsequent bleeding. The aim of this study was to evaluate the occurrence and pathophysiology of coagulopathy in cases of PAS managed conservatively. Methods: We reviewed our database for cases of PAS in which the placenta was kept in situ. In addition, we performed a systematic review of articles on PAS in which the placenta was left in situ and was complicated by coagulopathy. PubMed was searched for publications between 1980 and 2023. Our eligibility criteria included studies in which no additional interventions were performed other than keeping the placenta entirely in situ, and in which coagulopathy was reported. Results: After screening and selection of full‐text articles, 10 studies were included in the review. A review of our databases yielded a case series of PAS managed conservatively with the placenta kept in situ. When adding our case series to the results of our systematic review, a total of 87 cases were found to have been managed conservatively, with 28 cases of coagulopathy. Of these, the time at which coagulopathy developed was known in 11 cases. The median time at development of coagulopathy was 58 (interquartile range, 50–67) days postpartum. Conclusions: Our findings highlight that conservative management of PAS with the placenta in situ poses a risk of coagulopathy. Keeping the placenta in situ after delivery prolongs the risk factors that are integral to PAS. The pathophysiology behind coagulopathy is comparable with that of concealed placental abruption, due to the disrupted uteroplacental interface and the collection of blood in the placenta. Therefore, the presence of large placental lakes could be an indicator of developing coagulopathy. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Highly dynamic Internet of Vehicles spectrum sharing can share spectrum owned by vehicle-to-infrastructure links through multiple workshop links to achieve efficient resource allocation. Aiming at the problem that the rapid variations in channel states in highly dynamic vehicular environments can make it challenging for base stations to gather and manage information about instantaneous channel states, we present a multi-agent deep reinforcement learning-based V2X spectrum access algorithm. The algorithm is designed to optimize the throughput of V2I user under V2V user delay and reliability constraints, and uses the experience gained from interacting with the communication environment to update the Q network to improve spectrum and power allocation strategies. Implicit collaborative agents are trained through an improved DQN model combined with dueling network architecture and long short-term memory network layers and public rewards. With lagged Q-learning and concurrent experience replay trajectories, the training process was stabilized and the non-stationarity problem caused by concurrent learning of multiple agents was resolved. Simulation results demonstrate that our presented algorithm achieves a mean successful payload delivery rate of 95.89%, which is 16.48% greater than that of the randomized baseline algorithm. Our algorithm obtains approximately the optimal value and shows performance close to the centralized brute force algorithm, which provides a better strategy for further minimizing the signaling overhead of the Internet of Vehicles communication system. [ABSTRACT FROM AUTHOR]

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE). [ABSTRACT FROM AUTHOR]

This paper proposes a core and spectrum allocation algorithm for elastic optical networks based on multi-core fibers. In this context, the fragmentation and crosstalk mitigation algorithm (FraCA) is proposed. FraCA implements mechanisms to reduce spectral fragmentation and inter-core crosstalk in the network, proving efficient when compared with six other algorithms reported in the literature. The numerical results show that when compared with the most competitive of the six algorithms, FraCA achieves a gain of request blocking probability of at least 16.87%, a gain of bandwidth blocking probability of at least 43.95%, and a mean increase in spectral utilization of at least 4.36%. [ABSTRACT FROM AUTHOR]