Opportunistic Spectrum Access Research Articles

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

Minimizing File Transfer Time in Opportunistic Spectrum Access Model

We study the file transfer problem in opportunistic spectrum access (OSA) model, which has been widely studied in throughput-oriented applications for max-throughput strategies and in delay-related works that commonly assume identical channel rates and fixed file sizes. Our work explicitly considers minimizing the file transfer time for a given file in a set of heterogeneous-rate Bernoulli channels, showing that max-throughput policy doesn't minimize file transfer time in general. We formulate a mathematical framework for static extend to dynamic policies by mapping our file transfer problem to a stochastic shortest path problem. We analyze the performance of our proposed static and dynamic optimal policies over the max-throughput policy. We propose a mixed-integer programming formulation as an efficient alternative way to obtain the dynamic optimal policy and show a huge reduction in computation time. Then, we propose a heuristic policy that takes into account the performance-complexity tradeoff and consider the online implementation with unknown channel parameters. Furthermore, we present numerical simulations to support our analytical results and discuss the effect of switching delay on different policies. Finally, we extend the file transfer problem to Markovian channels and demonstrate the impact of the correlation of each channel.

Analyzing the role of spectrum sensing for security enhancement in beyond 5G waveforms using energy detection

Spectrum sensing (SS) plays a pivotal role in bolstering the security of 5G waveforms. In this abstract, we explore how it contributes to safeguarding 5G networks. By continuously monitoring the radio environment, spectrum sensing allows for the early detection of unauthorized or malicious signals. Cognitive radio (CR) technology is a cutting-edge innovation that can alleviate the need for additional spectrum. It has the potential to influence the course of technology if applied effectively. A CR network allows secondary, unlicensed users to make opportunistic use of primary, licensed spectrum without negatively impacting primary, licensed transmission or customers. Spectrum sensing is the most crucial step in implementing opportunistic spectrum access. In the recent decade, many different spectrum sensing strategies have been proposed in the scientific literature. In this paper, the performance of energy detection-based cooperative spectrum sensing has been measured and visualized using MATLAB simulations.

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks.

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks

AbstractThe internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Spectrum Sharing of the 12 GHz Band with Two-Way Terrestrial 5G Mobile Services: Motivations, Challenges, and Research Road Map

Telecommunication industries and spectrum regulation authorities are increasingly interested in unlocking the 12 GHz band for two-way 5G terrestrial services. The 12 GHz band has a much larger bandwidth than the current sub-6 GHz band and better propagation characteristics than the millimeterwave (mmWave) band. Thus, the 12 GHz band offers great potential for improving the coverage and capacity of terrestrial 5G networks. However, interference issues between incumbent receivers and 5G radio links present a major challenge in the 12 GHz band. If one could exploit the dynamic contexts inherent to the 12 GHz band, one could reform spectrum sharing policy to create spectrum access opportunities for 5G mobile services. This article makes three contributions. First, it presents the characteristics and challenges of the 12 GHz band. Second, we explain the characteristics and requirements for spectrum sharing at a variety of levels to resolve those issues. Lastly, we present several research opportunities to enable harmonious coexistence of incumbent licensees and 5G networks within the 12 GHz band.

Byzantine attack detection using optimized dual‐channel capsule generative adversarial network for securing cognitive radio network

AbstractCognitive Radio Networks (CRN) is emerged as a solution for the problem of spectrum scarcity as it achieves transmission opportunities in under‐utilized spectrum bands for primary users (PUs). Therefore, in this manuscript, an efficient Byzantine attack detection using optimized Dual‐Channel Capsule Generative Adversarial Network and Auto‐Metric Graph Neural Network (AMGNN) to secure Cognitive Radio Network. Here, two types of layers are utilized that is processing layer and decision layer. In processing layer, the dataset generation based on emission matrix from Hidden Markov Model (HMM) contains labeled data for the legitimate secondary users including malicious Byzantine attackers. In decision layer, the generated dataset is given to Dual‐Channel Capsule Generative Adversarial Network (DCCGAN) to classify secondary users (SUs) as Normal or Attacker. Normally, DCCGAN not exposes any optimization techniques adoption to compute optimum parameters for assuring exact prediction. Thus, capuchin search algorithm (CSA) is employed for optimizing DCCGAN weight parameters. Once the attack is detected, then alert information is fed to AMGNN opportunistic spectrum access. Proposed model executed at MATLAB and performance analyzed with performance metrics like system utility rate Vs available spectrum range, Attack detection rate, Accuracy, Packet Delivery Ratio (PDR), and throughput. The proposed method attains higher accuracy of 96.31%, 97.06%, 93.52%, higher system utility rate of 97.22%, 91.39%, 96.41%, and higher attack detection rate of 96.25%, 94.12%, 97.15% analyzed to the existing models, such as BAD‐AESA‐SCRN, BAD‐BMHHO‐ENN‐SCRN, and BAD‐HCM‐EELM‐SCRN respectively.

Energy-Efficient Opportunistic Spectrum Access in Multichannel Cognitive Radio Networks

We study an opportunistic spectrum access problem in multichannel Cognitive Radio Networks in which the secondary user (SU) has an energy cost when switching across channels. Channel switching from one RF chain to another also involves a reconfiguration delay that results in a shorter duration of transmission opportunity for the SU. For an imperfect sensing, we formulate the spectrum access problem in the framework of Partially Observable Markov Decision Process from which we obtain an average energy optimal policy. Also, we propose Efficacy - Efficient channel access with switching policy, for channel sensing and access based on the structure of the optimal policy. We compare the optimum policy with Efficacy, and show that Efficacy has a near optimal average energy performance.

Blockchain based smart contract for cooperative spectrum sensing in cognitive radio networks for sustainable beyond 5G wireless communication

Cognitive radio networks allow opportunistic spectrum access to the unlicensed users, which rely on effective spectrum sensing. Recently, cooperative sensing has been found to improve sensing reliability. However, sometimes the selfish users do not cooperate, and therefore, different incentive design mechanisms like contract theory have been proposed recently, where the cooperation is encouraged by offering appropriate reward to the sensing participants. However, still no secure structure exists for the reward distribution after a successful contract completion, and a user may also deny sensing even after accepting the contract. This work proposes the use of blockchain-based smart contracts, which are based upon a permissioned blockchain that helps in making the transactions private among the participants. Further, the efficiency of this approach is improved by introducing a reputation parameter for the users. This measures the trustworthiness of a particular user based upon the accuracy of their recent sensing results. Further, a money locking approach is proposed from which a penalty is deducted if the user denies or fails to submit a correct reading. Performance analysis of the proposed scheme is done to show the superiority of the scheme.

Reputation-Aware Relay Selection With Opportunistic Spectrum Access: A Blockchain Approach

The highly dynamic nature of cognitive radio systems (CR) and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transportation systems. In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology. In particular, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. We develop an offline module blockchain framework to store the real-time information of participating relays and make it available for future operations. The information is checked and verified by miners. Our system is able to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. We present a thorough numerical analysis to demonstrate the superiority of the proposed scheme in terms of security, credibility, and integrity. On average, the secrecy capacity rate of the overall system increased by 70% compared to the traditional systems. Further, the propsed model perform better in low SNR compared to traditional one.

Spectrum Sensing for a Radar Signal With Pulse Compression in Cognitive Radios

Opportunistic spectrum access to radar bands by a cognitive radio based secondary user has been recognized as an effective means to enhance the spectral utilization of the bands. A key functionality of cognitive radio is the spectrum sensing capability of detecting a primary user signal in the bands of interest. A widely used form of the primary user in radar bands is a pulse radar signal with pulse compression for achieving a higher range resolution and a longer detection range. In this letter, under the assumption that the period of a pulse radar signal is available a priori, we present GLRT based spectrum sensing schemes and provide their sensing performances in terms of the detection and false alarm probability.

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

Blockchain based Scalable model for Secure Dynamic Spectrum Access

Conventional Cooperative spectrum sensing techniques either suffer from single point of failure attack or lack in providing incentives to users which makes them incompatible for Wireless Service Provider (WSP). We propose a dynamic spectrum access framework for WSP which gives prominence to automated sensing and sharing with the use of blockchain. In this system, the opportunity of spectrum access is first examined by sensor nodes and the access right is then allocated to the users when their transactions to WSP are authenticated in a decentralized manner. Apart from using blockchain as a reliable platform for automatic enforcement of spectrum sensing, we propose a novel mechanism for securing our network from the threats designed primarily for Cognitive Radio Networks. In addition to this, our proposed approach enhances the scalability of blockchain networks by using the sidechains for storing data and checkpointing it onto main chain after periodic intervals of time. Extensive simulations in Octave indicate superior performance offered by our proposed model.

Near optimal scheduling for opportunistic spectrum access over block fading channels in cognitive radio assisted vehicular network

With the increasing use of cognitive radio technology in vehicular communications, vehicles will be enabled with cognitive radio in the future. Cognitive radio assisted vehicular networks make cognitive radio enabled vehicles utilize licensed spectrum on highways opportunistically. This work tackles cognitive radio assisted vehicular networks including M primary users (transmitter), M primary receivers, a secondary user (transmitter) with K channels and K secondary receivers. A channel is assigned to each primary user for data transmission whenever they need to use. The secondary user has data backlog. It knows neither the states (good or bad) of M channels, or statistics of channel evolution processes. It selects K of M channels to send data at each time slot. If it senses that a selected channel is in good state, it uses that channel for data packet transmission in same time slot. If not, it chooses another channel at the next time slot. We tackle this problem under average reward criteria for finite and infinite time horizon. This work presents Uniforming Random Ordered Policy (UROP) and shows UROP achieves near-optimal throughput under block fading assumption for generic channel evolution process. Numerical results show that our policy achieves near-optimal throughput (more than 80% of optimal throughput for scarce number of available channels) under average reward criteria whereas MP may achieve less than even 25% of optimal throughput for scarce number of available channels.

Performance Analysis of Cognitive SWIPT Ergodic Capacity Based on Different Selection Policies

The advent of 5G era and the increase of terminal’s numbers have led to increasing demand for spectrum resources and energy simultaneously. Therefore, this paper studies how to combine the cognitive radio (CR) and the Simultaneous Wireless Information and Power Transfer (SWIPT) technology effectively in a multiuser scenario, in which the spectrum utilization and energy efficiency are both improved. Firstly, the system model is designed of above cognitive SWIPT system, in which the cognitive users (CU) can choose spectrum access policy based on the amount of data transmission of licensed user. And then, according to cognitive users’ channel conditions and forwarding power, a concept of trust value and different relay selection policies is proposed. Subsequently, the according system ergodic capacity is analyzed. Finally, simulation results of system performance are provided to validate the analytical results. It shows that cognitive SWIPT technology can improve the system ergodic capacity and the spectrum access opportunities of cognitive users effectively, thereby improving the efficiency of energy and spectrum utilization.

Probability‐based fusion rule in cooperative spectrum sensing with impact of geographic location

This paper investigates the impact of geographic locations of secondary users (SUs) on cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs), where SUs send their local sensing results to a fusion center (FC) and the FC makes a global spectrum decision by a specific fusion rule. The purpose of this paper is to propose a fusion rule that takes into account the impact of geographic locations on CSS, and identify the optimal decision threshold of the proposed fusion rule in order to maximize the average achievable throughput of the secondary network subject to a collision constraint. To quantitatively capture the impact of geographic locations on CSS, a metric called absolute impact is defined, which is determined by the probability of detection and the probability of false alarm. Meanwhile, another metric is defined called relative impact to characterize the impact of SU's incorrect local sensing result on CSS. Based on the defined metrics, a probability-based fusion rule is proposed, and the core idea is that the global spectrum decision of CSS is jointly determined by the local sensing results and the absolute impacts of SUs. Then the average achievable throughput of the secondary network and the collision probability are derived, both of which are proved to decrease with the decision threshold of the proposed fusion rule. By tackling the trade-off between the accuracy of spectrum sensing and spectrum access opportunity caused by the decision threshold, an approximate algorithm based on the idea of the bisection method is proposed to obtain the optimal decision threshold that maximizes the average achievable throughput of the secondary network while protecting primary transmission to a predefined extent. Numerical results are carried out to verify the theoretical analyses and show the flexibility of the proposed fusion rule.