Dynamic Spectrum Access Research Articles

A dynamic spectrum access algorithm based on deep reinforcement learning with novel multi-vehicle reward functions in cognitive vehicular networks

A dynamic spectrum access algorithm based on deep reinforcement learning with novel multi-vehicle reward functions in cognitive vehicular networks

A Low Complexity Cooperative Spectrum Sensor for Cognitive –Radio Network Based on Approximate Computing

With the rapid growth of new wireless technologies, the issue of spectrum scarcity is becoming more prominent due to the limited availability of radio spectrum. Unutilized spectrum bands designated for primary users are known as spectrum holes or white spaces. Assigning these white spaces to secondary users can enhance spectrum utilization. Dynamic spectrum access is crucial for sharing licensed spectrum with secondary users, as opposed to static allocation policies which impede effective spectrum utilization. Spectrum sensing aims to detect licensed users in the spectrum to prevent interference from secondary users. Approximate computing has emerged as a strategy to enhance efficiency by leveraging the fault resilience of various applications. It introduces trade-offs in design optimizations, allowing for significant reductions in VLSI circuit area and energy consumption. A cooperative spectrum-sensing (CSS) algorithm based on approximate computing for cooperative cognitive-radio networks is proposed to reduce computational complexity for efficient hardware design while maintaining detection performance. The hardware-efficient architecture of the cooperative spectrum sensor (CSR) based on the CSS algorithm, combined with resource-sharing architectural optimization, outperforms existing implementations by occupying less area, achieving shorter sensing times, and displaying superior hardware efficiency. Keywords—Approximate Computing, Cooperative Spectrum Sensor(CSR), very-large scale integration(VLSI)

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

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.

Deep-Reinforcement-Learning-Based Distributed Dynamic Spectrum Access in Multiuser Multichannel Cognitive Radio Internet of Things Networks

Deep-Reinforcement-Learning-Based Distributed Dynamic Spectrum Access in Multiuser Multichannel Cognitive Radio Internet of Things Networks

Many-to-one matching based cooperative spectrum sharing in overlay cognitive radio networks

Cooperative Spectrum Sharing (CSS) is a Dynamic Spectrum Access mechanism aimed at providing incentives for both primary users (PUs) and secondary users (SUs) through cooperative communication. This paper addresses the pairing problem among multiple PUs and SUs to allocate optimal fractions of spectrum resources in terms of access time, using the CSS technique. Inspired by Matching Theory, we model the CSS between PUs and SUs as a Two-sided Matching Market and explore different market equilibria. Cooperative strategies among SUs are employed to maximize the gross utility of cooperative SUs and overall utility of the secondary network, in exchange for relay services rendered to PUs. We propose a polynomial-time based many-to-one matching scheme that favors groups of collaborative SUs to attract favorable PUs from the opposite side, leading to optimal matching for SUs. On the other hand, each PU achieves stable matching when paired with a group of cooperative SUs. We provide necessary proofs for the achieved matching equilibrium. Furthermore, we formulate an optimization framework for the optimal usage of PU access time among stable PU-SU pairs and propose a near-optimal solution, achieving 96% accuracy compared to the optimal allocation yield from the benchmark method (analytical). Our proposed many-to-one matching scheme outperforms similar existing schemes and the one-to-one matching scheme in terms of SU utility, satisfaction, participation, and throughput fairness index.

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.

COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK

Cognitive radio (CR) is an integral system in telecommunications technology that gives unlicensed users access to licensed spectrums via dynamic spectrum access (DSA), to promote spectral efficiency. A significant operation in cognitive radio system is spectrum sensing. This paper evaluates and compares two of the major non-cooperative sensing techniques (energy and cyclostationary feature detector (CFD)) in order, to determine which gives better performance. Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels.

Schwarz Information Criterion Based Thompson Sampling for Dynamic Spectrum Access in Non-Stationary Environment

Schwarz Information Criterion Based Thompson Sampling for Dynamic Spectrum Access in Non-Stationary Environment

Spatiotemporal attention aided graph convolution networks for dynamic spectrum prediction

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

A novel hybrid heuristic-based network parameter optimization for spectral and energy efficiency in dynamic spectrum access on wireless mesh network system

The ‘Wireless Mesh Networks (WMNs)' is utilized to enhance the networking services and internet access in metropolitan regions, campuses, local and personal. The ‘Mesh Routers (MR)' creates the connectivity backbone while performing multiple packet transmission tasks along with providing network ingress to the mesh users. This issue can be minimized by incorporating cognitive radio that focuses on inventing the spectrum management and sensing mechanisms. Hence, there is a demand to implement a framework for the ‘optimal Dynamic Spectrum Access' (DSA) in WMN beside with cognitive thoughts. In this paper, the optimization techniques are utilized to exploit the spectral and energy effectiveness of DSA in WMN. So, a novel hybrid approach, called the Iteration-based Position of Fire Hawk and Coyote Optimization (IPFHCO) is developed in this work. The developed hybrid model, IPFHCO can effectively maximize the energy and spectral efficacy in the WMN system than the other conventional approaches while performing several analyses. The simulation outcomes shows that the spectral efficiency of the offered DSA system in WMN is enhanced by 37.5% than the DHOA, 62.5% than the Jaya, 16.66% than the FHO, and 45.83% than the COA, while taking the transmission power value as 25 bps\\Hz.

Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review

As the world continues to seek sustainable and efficient energy solutions, the integration of advanced technologies into smart energy grid management (SEGM) becomes a paramount focus. The advent of Sixth Generation (6G) wireless networks promises to revolutionize the way energy grids are monitored, controlled, and optimized. This review paper explores the potential of 6G wireless networks in the context of SEGM. It discusses the vision and potential techniques that can be harnessed to unlock the full capabilities of 6G networks. The paper delves into the challenges and opportunities presented by 6G technology, addressing issues such as scalability, security, real-time monitoring, and dynamic spectrum access. Moreover, it explores how 6G wireless networks can enable seamless integration with other advanced technologies, such as blockchain and cybertwin, to enhance the resilience and reliability of smart energy grids. The comprehensive review aims to shed light on the transformative role of 6G wireless networks, paving the way for a sustainable and intelligent future in energy grid management.

Federated and Online Dynamic Spectrum Access for Mobile Secondary Users

Users in dynamic spectrum access (DSA) with federated reinforcement learning (FRL) autonomously access channels, avoiding centralized coordination and protecting users’ privacy. However, existing FRL-based DSA mechanisms are limited to ideal network states, i.e., assuming that channel states and users’ interference relationships are unchanged. Besides, users should upload intermediate results simultaneously for federated aggregation. The above conditions are impractical for mobile users since their network states and locations are unstable. Meanwhile, newly connected users have to train their models through local data with numerous computing resources since global models are unsuitable for them. We propose FRDSA, an FRL-based secure and lightweight channel selection mechanism in DSA for mobile users under dynamic network states. An independent channel selection environment with a virtual group strategy is presented to avoid interference between users under unstable channel states. Furthermore, an asynchronous parameter aggregation method in FRDSA dynamically adjusts the aggregation factors without users simultaneously uploading intermediate results. Simulations based on real trajectory data show that FRDSA significantly reduces approximately 60% interference between mobile users under unstable network states. Newly connected users can directly apply the well-trained global model to access channels autonomously instead of retraining a model, effectively reducing mobile users’ computing resource requirements.

Joint Collaborative Big Spectrum Data Sensing and Reinforcement Learning Based Dynamic Spectrum Access for Cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Deep Reinforcement Learning for Dynamic Spectrum Access: Convergence Analysis and System Design

Deep Reinforcement Learning for Dynamic Spectrum Access: Convergence Analysis and System Design

Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks

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

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Removal of Cyclic Prefix in Adaptive OFDM for Dynamic Spectrum Access using DWT and WT

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Removal of Cyclic Prefix in Adaptive OFDM for Dynamic Spectrum Access using DWT and WT

Enhanced Wireless Network Usage and Loading on Network Delay using Cognitive Radio Network

Wireless communication is one of the fastest-growing fields of communication. Because the user can access and share information anywhere at any time. The communication process will suffer from an overload of traffic. If the node gets overloaded because of heavy traffic, then the performance of the whole network is degraded. So, the usage of the network will be delayed. The proposed work uses the intellectual load balancing approach for Cognitive Radio Network to focus on automatic monitoring of network activity, and the node will continuously balance among numerous nodes. In Cognitive Radio Network the Dynamic Spectrum Access techniques are used to avoid spectrum scarcity and underutilization. The CR base station collect the surrounding about the active primary user and waiting secondary user. To determine the principal user's on/off status of availability, the Markov chain model is employed. The waiting secondary user utilize the spectrum when the primary user is not using it. The secondary user leaves the spectrum and uses the spectrum of another primary user if the primary user is on status. It will assist with dynamic spectrum access method and it will help increase the overall performance of the network. The suggested Load balancing Cognitive Radio Network (LBCRN) sorts the nodes and uses spectrum identification before allocating the node to the secondary user. So that minimise transaction delays, prevent overcrowding of a specific node, automatically lower the level of packet drops, and enhance packet delivery radio levels. As a result, this strategy has helped to lessen the issue of spectrum shortages in the wireless communication industry.

A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models.

Terahertz (THz) band will play an important role in enabling sixth generation (6G) envisioned applications. Compared with lower frequency signals, THz waves are severely attenuated by the atmosphere temperature, pressure, and humidity. Thus, designing a THz communication system must take into account how to circumvent or diminish those issues to achieve a sufficient quality of service. Different solutions are being analyzed: intelligent communication environments, ubiquitous artificial intelligence, extensive network automation, and dynamic spectrum access, among others. This survey focuses on the benefits of integrating intelligent surfaces (ISs) and THz communication systems by providing an overview of IS in wireless communications with the scanning of the recent developments, a description of the architecture, and an explanation of the operation. The survey also covers THz channel models, differentiating them based on deterministic and statistical channel modeling. The IS-aided THz channels are elucidated at the end of the survey. Finally, discussions and research directions are given to help enrich the IS field of research and guide the reader through open issues.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.