Efficient Spectrum Utilization Research Articles

Secure Radio Resource Management in Cloud Computing using CRN

Limited processing power in Cognitive Radio Networks (CRNs) creates challenges for secure radio resource management, particularly in dynamic spectrum access environments. This paper proposes a novel cloud-assisted CRN framework that addresses this limitation. By offloading spectrum sensing and allocation tasks to the cloud's vast computational resources, CR users can achieve a more secure and efficient spectrum utilization. The cloud platform prioritizes robust security protocols to safeguard spectrum information and communication, ensuring confidentiality, integrity, and user trust. This innovative approach optimizes spectrum usage, improves Quality of Service (QoS) for CR users, and strengthens the CRN's defense against unauthorized access and malicious attacks

Enhancing spectrum sensing efficiency in multi‐channel cognitive device‐to‐device networks: Medium Access Control layer strategies and analysis

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

Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance

The ability to rapidly understand and label the radio spectrum in an autonomous way is key for monitoring spectrum interference, spectrum utilization efficiency, protecting passive users, monitoring and enforcing compliance with regulations, detecting faulty radios, dynamic spectrum access, opportunistic mesh networking, and numerous NextG regulatory and defense applications. We consider the problem of automatic modulation classification (AMC) by a distributed network of wireless sensors that monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve modulation classification accuracy, we consider federated learning (FL) where each individual sensor shares its trained model with a centralized controller, which, after aggregation, initializes its model for the next round of training. Without exchanging any spectrum data (such as in cooperative spectrum sensing), this process is repeated over time. A common DNN is built across the net- work while preserving the privacy associated with signals collected at different locations. Given their distributed nature, the statistics of the data across these sensors are likely to differ significantly. We propose the use of adaptive federated learning for AMC. Specifically, we use FEDADAM -an algorithm using Adam for server optimization – and ex- amine how it compares to the FEDAVG algorithm -one of the standard FL algorithms, which averages client parameters after some local iterations, in particular in challenging scenarios that include class imbalance and/or noise-level imbalance across the network. Our extensive numerical studies over 11 standard modulation classes corroborate the merit of adaptive FL, outperforming its standard alternatives in various challenging cases and for various network sizes.

Rotating Target Detection Using Commercial 5G Signal

Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of a high-frequency band, high bandwidth, and a large number of base stations, which give it significant advantages for use in passive radar. Therefore, in this paper, we introduce a passive radar target detection method based on 5G signals and design a rotating target speed measurement experiment. In the experiment, this paper validated the method of detecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors.

Simulation of association rule mining based on sensor networks in Chinese language learning recommendation system for college students

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.

Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links.

ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects

With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite–terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated.

Performance study of organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic technology

Organic photovoltaic cells are considered to be an effective solution for maintaining excellent electrical energy output in high temperature environment. However, the application of OPV alone has a low utilization rate of the solar spectrum. The combined application of OPV and other technologies is explored to improve the utilization efficiency of the solar spectrum. Based on the above gaps, this study proposes an organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic (PC/TC-OPV/T). By integrating organic photovoltaic cells, photocatalytic technology, thermal catalysis technology and photothermal components, the utilization efficiency of the solar spectrum is improved, and the triple functions of formaldehyde removal, electrical energy and hot water are realized. Therefore, this study proposes a new air purification-thermal-electrical model, establishes the heat transfer balance equation inside the system and the air purification and mass transfer balance equation in the air duct, proposes the performance evaluation index of the system, and uses Ansys Fluent software to predict the working characteristics of the system under different operating conditions, and draw the following conclusions: (1) Under the synergistic action of photocatalytic and thermal catalytic technologies, the formaldehyde removal efficiency is 84.4%, which is 30% higher than that of photocatalytic alone. (2) The electrical efficiency of the new system benefits from high solar irradiance and environment temperature, enabling it to achieve the efficiency of 17.84% within the research scope. (3) Under the research conditions, the instantaneous water flow heating efficiency of the system is up to 69.6%.

Spectrum and Power Efficient Anti-Jamming Approach for Cognitive Radio Networks Based on Reinforcement Learning

Background: Spectrum scarcity, spectrum efficiency, power constraints, and jamming attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs) enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently by the SU to ensure a high data rate transmission. In addition, the mobility of the secondary users (SUs) makes power consumption a matter of concern in wireless networks. Because of the open environment, the jamming attack can easily deteriorate the performance and disrupt the connections. background: Various anti-jamming schemes have been proposed to mitigate the attacker's impact on Cognitive Radio Networks (CRNs), some of the proposed schemes aim to increase channel capacity or improve spectrum-efficient gain. However, few of them have considered the secondary user's (SU’s) power consumption. Objectives: We aim to enhance the performance of CRN and establish more reliable connections for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending the network lifetime. Methods: To achieve our objectives, we propose an anti-jamming approach that adopts frequency hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ reinforcement learning to address this game. Results: SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the power consumption in the presence of a smart jammer. Simulation results show that the low channel gain leads the SU to select a high number of data channels. However, when the channel gain is high, the SU increases the number of control channels to guarantee a more reliable connection. Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of used channels. The proposed strategy achieves better performance in comparison with myopic learning and the random strategy. Conclusion: Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate number of control and data channels to ensure a reliable, efficient, and long-term connection.

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

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

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

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

Spray: A Spectrum-efficient and Agile Concurrent Backscatter System

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

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

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

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

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

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

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

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

ShuffleFormer: An efficient shuffle meta framework for automatic modulation classification

Automatic modulation classification (AMC) plays an essential and fundamental part in wireless communication systems, which can greatly enhance the efficiency of spectrum utilization. With the rapid development of internet of things, it has been an urgent task to effectively deploy AMC method in edge devices which usually have limited computing power. Hence, there is a pressing need for a high-performance AMC method, especially in terms of high accuracy and low computation complexity in modulation recognition. Although numerous AMC methods have been proposed, it is still a challenge to develop a novel AMC method with both high recognition accuracy and limited parameters. Recently, MetaFormer, as the promising technology, has achieved outstanding performance in processing long sequences. Inspired by MetaFormer, in this paper, an efficient end-to-end deep ShuffleFormer AMC approach is proposed to realize high performance modulation recognition. In the proposed approach, firstly, the PreBlock is proposed to extract deep information from input signals, in which both the channel and temporal attention are utilized to enrich the useful features. Then, bidirectional gated recurrent unit (BiGRU) is employed to further refine the abundant information for feature extraction. Furthermore, ShuffleFormer module is proposed, which innovatively fuse shuffle blocks into MetaFormer to further enhance its both local and global feature extraction ability with less parameters. Experimental results demonstrate that the ShuffleFormer AMC approach has more superior recognition performance than other contrastive methods, i.e., at least 5.97% average accuracy improvement with almost the same number of parameters, and less 41.4% parameters with a competitive average accuracy.

Joint task offloading and resource allocation for secure OFDMA-based mobile edge computing systems

With the advent of the fifth generation (5G) era, Mobile edge computing (MEC) has become a promising technology. In order to increase the spectrum utilization efficiency and mitigate interference between users, Orthogonal Frequency-Division Multiple Access (OFDMA) technology is introduced into MEC systems. However, uplink offloading signals from users to servers are vulnerable to malicious eavesdropping. We utilize physical layer security, specifically, jamming caused by servers and co-frequency interference provided by users in non-orthogonal multiple access (NOMA) groups to address this issue. This paper investigates an OFDMA based MEC system which consists of multiple users, multiple servers and an eavesdropper. For this system, an innovative algorithm is designed to minimize latency of the system through optimizing resource allocation and task offloading policy which has lower computation complexity while ensuring its security. The proposed scheme can decrease system latency and ensure secure communication according to experimental results.

Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption

Cognitive Radio Sensor Networks (CRSNs) have emerged as a promising solution for efficient utilization of the limited frequency spectrum. One of the key challenges in CRSNs is achieving spectrum efficiency by avoiding interference and maximizing the use of the available spectrum. Particle Swarm Optimization (PSO) techniques have been widely used to optimize the spectrum allocation and improve the spectrum efficiency of CRSNs. In this paper the study provides an overview of the research on spectrum efficiency in CRSNs using PSO techniques and also discussed the key parameters that affect the spectrum efficiency, such as the swarm size, sensor's threshold and maximum number of iterations and highlights the importance of identifying the optimal combination of these parameters. This paper also emphasizes the need for further research and development in this area to improve the efficiency and effectiveness of PSO-based optimization techniques for CRSNs and to adapt them to various real-world scenarios. Achieving spectrum efficiency in CRSNs is critical for enabling effective wireless communication systems and improving the overall utilization of the available frequency spectrum.

Unimodular waveform design for integrated radar communication and jamming

To further solve spectrum congestion and improve spectrum utilization efficiency, we research a novel integrated radar communication and jamming (IRCJ) system based on a multi-input-multi-output (MIMO) platform in this study. We trade off the beampattern matching error, multiuser interference (MUI) energy, and spectrum matching error as the optimization cost function and design an integrated transmitted waveform that can detect radar targets, achieve high-speed downlink communication, and jam enemies simultaneously. Furthermore, we add a constant modulus constraint to ensure the unimodular property of the transmitted waveform. The proposed optimization problem is multivariable, non-convex, and quartic. To tackle this, we develop an iterative algorithm based on a cyclic manner and majorization-minimization (MM) framework. Finally, the simulation results verify the effectiveness of the proposed algorithm.