Dynamic Spectrum Access Research Articles

Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the frequency channels to the on-demand unlicensed secondary users known as cognitive radios (CRs). Although existing literature has developed various dynamic spectrum access mechanisms for CBS, there is still a dearth of studies due to the wide range of assumptions made in the solutions. Most of the existing works study the CBS scheduling problem scheme by adopting optimization-based methods and rely on the prior knowledge of the network parameters such as primary users’ activity. Moreover, the impact of channel switching costs on the network throughput has not been well studied. In this paper, we aim to maximize the CRNs total throughput, and we formulate the CBS scheduling problem as a non-stochastic (i.e., adversarial) combinatorial multi-armed bandit problem with semi-bandit feedback and arm switching costs. We propose two novel online learning algorithms for CBS scheduling with and without channel switching costs, where their regret performances are proved sublinear order-optimal in time as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{1/2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2/3}$ </tex-math></inline-formula> , respectively, offering throughput-optimal scheduling for CRNs. Experiments on the synthetic and real-world spectrum measurement data complement and validate our theoretical findings.

Throughput optimization of interference limited cognitive radio-based Internet of Things (CR-IoT) network

The Internet of Things (IoT) technology allows massive devices to connect to the internet for data exchange. It is anticipated that in near future, trillions of IoT devices will be connected to the internet. To deploy these devices, the requirement of the spectrum is increasing day by day. Most of these devices transmit data over unlicensed frequency bands that cause severe interference to each other while exchanging the data as these bands are becoming overcrowded. Therefore, to overcome spectrum scarcity and interference problems among these devices, a novel communication paradigm called cognitive radio-based internet of things (CR-IoT) is evolving at a very fast pace that integrates cognitive radio technology into the IoT devices. The technology has the potential to overcome the spectrum scarcity and interference problem by allowing dynamic spectrum access to conventional IoT networks. Such devices continuously monitor spectrum availability to transmit the data by incorporating an intelligent sensing mechanism into the devices. However, the performance of the sensing unit in terms of the probability of detection and the probability of false alarm, significantly deteriorates due to the noise uncertainties, especially in low signal-to-noise ratio environments. For the efficient utilization of the spectrum, the probability of detection and the probability of false alarm of the spectrum sensor should be high and low, respectively. Both sensing parameters are greatly influenced by the selection of the sensing threshold. Moreover, these IoT devices deal with short packet transmissions, the optimum sensing time is another crucial parameter that governs the performance of these devices.While addressing these two important issues, the convex optimization problem is formed over sensing time and sensing threshold, and the concavity on sensing threshold is proved. Further, an iterative algorithm is proposed for the CR-IoT system that intelligently adapts the sensing threshold to meet desired sensing performance in terms of Pd and Pf especially, in low SNR regions, and also optimize the sensing time to overcome the sensing throughput tradeoff. The simulation results are presented to validate the effectiveness of the proposed algorithm. It is demonstrated that the proposed algorithm increases the CR-IoT system throughput by 95% as compared to the conventional scheme at the received signal to noise ratio equals −20 dB while satisfying the requirement of Pd and Pf as per IEEE 802.22 standard.

A game theory based approach for distributed dynamic spectrum access

A game theory based approach for distributed dynamic spectrum access

A Survey on Dynamic Spectrum Access Techniques in Cognitive Radio Networks

The idea of Cognitive Radio (CR) is to share the spectrum between a user called primary, and a user called secondary. Dynamic Spectrum Access (DSA) is a new spectrum sharing paradigm in cognitive radio that allows secondary users to access the abundant spectrum holes in the licensed spectrum bands. DSA is an auspicious technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this paper, a survey of spectrum access techniques using cooperation and competition to solve the problem of spectrum allocation in cognitive radio networks is presented.

A Survey on the Communication Protocols and Security in Cognitive Radio Networks

A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. Most of the active research conducted in the area of CRNs has been so far focused on spectrum sensing, allocation and sharing. There is no comprehensive review paper available on the strategies for medium access control (MAC), routing and transport layer protocols, and the appropriate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mechanisms that have been proposed in the literature for communication protocols (at the MAC, routing and transport layers), in the context of a CRN, as well as discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and analysis of the strategies for MAC, routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for someone to further delve onto any particular aspect in greater depth.

Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network.

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.

RDRL: A Recurrent Deep Reinforcement Learning Scheme for Dynamic Spectrum Access in Reconfigurable Wireless Networks

Reconfigurable wireless network can flexibly provide efficient spectrum access service and keep stable operation in highly dynamic environment. In this paper, a primary-prioritized recurrent deep reinforcement learning algorithm for dynamic spectrum access based on cognitive radio (CR) technology is proposed. The spectrum Markov state is modeled to capture the evolution behavior to achieve the priority queuing of the primary users and the secondary users. According to the spectrum access strategies of the secondary users under different optimal criteria, we can obtain the best tradeoff benefits of spectrum access fairness and throughput. Furthermore, we proposed a learning-based algorithm for dynamic spectrum access, which allows the secondary users to modify their parameters to select the optimal access policy to maximize network throughput utilization. The Dueling Deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Network (Dueling DQN) with prioritized experience replay combined with recurrent neural network is used to improve the convergence speed. Extensive experimental results demonstrate that the proposed RDRL scheme outperforms the existing Dueling DQN and DQN schemes in terms of convergence speed and channel throughput.

Development of Database Accessibility for Frequency Scheduling of TVWS Broadband Connectivity in Rural Areas Using Dynamic Spectrum Access Technique

As a result of the switchover from analogue to digital transmission, Television White Space (TVWS) presented itself as good opportunity to supplement the existing licensed spectrum to ease the spectrum scarcity. Rural communities were usually not connected due to poor returns to the internet providers to provide broadband access to the areas. This research has prepared the framework and feasibility study for deploying broadband internet services using Television White Space (TVWS) technology in Ugbawka, a rural area in Enugu state, Nigeria. In this work, a Network Ping was run on five websites using three major internet service providers as backhaul to establish facts of poor or even non-existent internet services. Using Ping Plotter 5 Pro, accessing ieee.com using MTN yielded a Round Trip (RT) average of 846.433 ms, with a loss of 83.8% packet over 10mins count. Radio Frequency (RF) Explorer and Carlson transceiver, Customer Premise Equipment (CPE) were used for field trials to determine availability of TVWS Frequencies. A database app was developed by writing some codes in the Basic for Android (B4A) Intermediate Development Environment (IDE). Empirical outdoor propagation model was developed with a 2.15 pathloss exponent while the indoor propagation model gave a pathloss exponent of 3.47 . An algorithm was developed, titled, TVWS Optimization Quadrature Amplitude Algorithm, (TOQA), where throughput of this project performed better by giving 60Mbps and 70 Mbps at Signal-Noise-Ratio (SNR) of 5dB while the conventional algorithm gave 30Mbps and 25 Mbps at same SNR value. The Bit Error Rate was lower than the conventional models used, giving the TOQA values of 10-3 at SNR of 5 dB and 10-6 at SNR of 30 dB while the conventional method gave 10-1 and 10-3 respectively at the same SNR values. With an Average of 56.2% network latency recorded in Ugbawka, TVWS will make a great impact on Internet connectivity if deployed.

Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey

Integrating cognitive radio (CR) with traditional wireless networks is helping solve the problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic spectrum access features of CR provide the functionality to its unlicensed users to utilize the underutilized spectrum at the time of need because CR nodes can sense vacant bands of spectrum and can also access them to carry out communication. Various capabilities of CR nodes depend upon efficient and continuous reporting of data with each other and centralized base stations, which in turn can cause leakage in privacy. Experimental studies have shown that the privacy of CR users can be compromised easily during the cognition cycle, because they are knowingly or unknowingly sharing various personally identifiable information (PII), such as location, device ID, signal status, etc. In order to preserve this privacy leakage, various privacy preserving strategies have been developed by researchers, and according to us differential privacy is the most significant among them. In this article, we provide a thorough survey on how differential privacy can play an active role in preserving privacy of cognitive radio networks (CRN). Firstly, we provide a thorough comparison of our work with other similar studies to show its novelty and contribution, and afterwards, we provide a thorough analysis from the perspective of various CR scenarios which can cause privacy leakage. After that, we carry out an in-depth assessment from the perspective of integration of differential privacy at different levels of CRN. Then, we discuss various parameters which should be considered while integrating differential privacy in CRN alongside providing a comprehensive discussion about all integrations of differential privacy carried out till date. Finally, we provide discussion about prospective applications, challenges, and future research directions. The discussion about integration of differential privacy in different CR scenarios indicates that differential privacy is one of the most viable mechanisms to preserve privacy of CRN in modern day scenarios. From the discussion in the article, it is evident that the proposed integration of differential privacy can pave the way for futuristic CRN in which CR users will be able to share information during the cognition cycle without the risk of losing their private information.

A Two-Dimensional Sybil-Proof Mechanism for Dynamic Spectrum Access

Achieving higher spectrum utilization, auction-based mechanisms has been regarded as a popular tool in dynamic spectrum access (DSA). Recently, Sybil attacks in auction-based DSA mechanisms have been investigated, where a cheating bidder can manipulate an auction by submitting bids under multiple fake identities. Existing Sybil-proof mechanisms in DSA are limited to prevent Sybil attacks from primary users (PUs) or secondary users (SUs). However, both of PUs and SUs may perform Sybil attacks in DSA, i.e., double Sybil attacks. The challenge of solving the double Sybil attacks is that fictitious identities and fake bids can directly affect allocation results, but the malicious bidders cannot be straightforwardly distinguished from all bidders. To resist the double Sybil attacks, we propose STEAM, the first double Sybil-proof and two-dimensional Truthful spEctrum Auction Mechanism for DSA. Specifically, STEAM merges suspicious buyers based on geographic characteristics and sorts sellers by a bid-independent sorting method to minimize the impact of untruthful bids and Sybil attacks on the allocation results. Theoretical analysis and extensive evaluations prove that STEAM is double Sybil-proof, two-dimensional truthful, individual rational and budget-balanced, while the performance loss in various metrics within 8&#x0025; compared to the existing auction-based mechanisms.

A Systems Approach for Solving Inter-Policy Gaps in Dynamic Spectrum Access-Based Wireless Rural Broadband Networks

In this paper, we articulate the challenge of multiple intersecting policies for the realization of rural broadband networks employing dynamic spectrum access (DSA). Broadband connectivity has been identified as a critical component of economic development, especially during the COVID-19 pandemic, and rural communities have been significantly (and negatively) affected by the lack of this important resource. Although technologies exist that can deliver broadband connectivity, such as 4G LTE and 5G cellular networks, the challenges associated with efficiently deploying this infrastructure within a rural environment are multi-dimensional in terms of the different dependent policy decisions that need to be considered. To resolve this issue, we describe how systems engineering tools can be used for representing these intersecting policies such that system configurations can be optimized for efficient infrastructure deployment and operations. One technology requiring increased attention is DSA, where licensed and emerging wireless services can coexist together via spectrum sharing. However, implementation of this technology is challenging, where highly efficient Radio Access Technology (RAT), available spectrum, and user requirements need to be precisely aligned. All these elements to be configured are typically described by independent policies. While DSA is more complicated than previously used spectrum allocation schemes, inter-policy gaps occur that ultimately decrease the network&#x2019;s efficiency. Consequently, a systems engineering framework has the potential to obtain the optimal solutions although the systems and wireless communities conceptualize and scope problems differently, which can impede collaboration. We present the use case where 4G LTE RAT technology employing DSA applied to digital terrestrial television (DTT) frequency bands can yield spectral efficiency loss when the different policy dimensions are not sufficiently accounted for within the use case. Numerical experiments have shown that in an example rural scenario the availability of rural broadband can increase from 1&#x0025; to 21&#x0025; of locations if the inter-policy gaps are removed.

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

In this work, we evaluate the application of four different metaheuristic optimisation algorithms for solving the channel assignment problem in a multi-radio multi-channel Wireless Mesh Network (WMN) using Dynamic Spectrum Access (DSA). The work advances a near optimal channel assignment (CA) in a WMN that uses DSA by applying soft computing methods. While CA in a WMN is well-studied, and CA for secondary user cognitive radio networks has also been studied in the literature, CA for our specific scenario of an infrastructure DSA-WMN is novel. This scenario poses new challenges because nodes are spread out geographically and so may have different allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) to avoid interference within the network and with external interference sources, and 2) maintain connectivity within the network; all while staying within the radio interface constraint, i.e., only assigning as many channels to a node as it has radios. We present a novel algorithm, used alongside the metaheuristic optimisation algorithms, which ensures the feasibility of solutions in the search space. Average Signal to Interference and Noise Ratio (<inline-formula> <tex-math notation="LaTeX">$SINR$ </tex-math></inline-formula>) over the network is used as the performance measure, with the goal of optimisation being to find the highest average <inline-formula> <tex-math notation="LaTeX">$SINR$ </tex-math></inline-formula>. This is a more realistic performance measure than the binary on/off conflict-based measures most common in the literature. Our energy-based method also has the unique advantage that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. The algorithms that are compared in this work are Simulated Annealing (SA), the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE). These algorithms were evaluated through the use of simulation in Network Simulator 3. Various parameters were experimented with for each of the employed algorithms. The resultant best set of parameters was used for the comparison of the four metaheuristic algorithms. It was found that the population-based algorithms (PSO, GA, and DE) all perform satisfactorily for this problem, although DE is superior to the others. SA can give acceptable solutions, but performs poorly in comparison to the population-based algorithms. The paper also considers the computational complexity of the methods. It is found that SA and DE perform well in this regard.

Multi band efficient networks for ad hoc communications

The purpose of the article is to present major goals, assumptions and results of European Defense Agency project MAENA, devoted to mobile ad hoc networks with dynamic spectrum access, which is realized locally by network nodes. They are also supported by additional spectrum management entities, acting in coordinated, centralized way to optimize use of assigned spectral resources and maximize network properties.

Intelligence-based optimized cognitive radio routing for medical data transmission using IoT

&lt;abstract&gt; &lt;p&gt;The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.&lt;/p&gt; &lt;/abstract&gt;

Resource Allocation in Dynamic Spectrum Access Using Deep Reinforcement Learning and Meta Learning

Resource Allocation in Dynamic Spectrum Access Using Deep Reinforcement Learning and Meta Learning

Performance Analysis of Efficient Spectrum Utilization in Cognitive Radio Networks by Dynamic Spectrum Access and Artificial Neuron Network Algorithms

Efficient spectrum utilization is a prominent issue in cognitive radio networks. Owing to this, power allocation policies are proposed which underlay cognitive radio networks together among all prime nodes, secondary nodes, eavesdropper and secondary sender powered by renewable energy that is harvested from primary sender to acquire improved energy efficiency to enhance transmission rate, throughput, and Spectrum Utilization (SU). As a result, there is a need for combination of Dynamic Spectrum Access (DSA) algorithm, Artificial Neuron Network (ANN) algorithm which will make an allotment of obtainable network assets for various elements challenging for their resources. The prime objective of this paper is to intend a route control based multi-path Quality Of Service (QoS) and to find substitute paths between Secondary User (SU) source and SU destination fulfilling QoS metrics, specifically providing maximal throughput and minimal delay. In order that primary substitute channels along the paths are used completely to reduce data packets loss by using Network Simulator 2 (NS2) software tool.

Spectrum Sensing: A Tutorial

Spectrum sensing, combined or not with database information on radio-frequency (RF) spectrum occupation, is envisaged as part of the solution to the spectrum scarcity inherited by the fixed spectrum allocation policy currently adopted around the world. The solution is grounded on the premise of spectrum sharing between primary (incumbent) and secondary networks under a new dynamic spectrum access (DSA) paradigm. This tutorial presents basic concepts, fundamentals and state-of-the-art techniques related to spectrum sensing. Thought as a short course material, it covers from concepts regarding the forms of spectrum sensing and basic fundamentals on detection theory, to modern cooperative spectrum sensing techniques and research challenges, passing through the mathematical model for the signal and the sensing channel, the signal-to-noise ratio wall, practical issues regarding signal processing tasks, and performance metrics. A complete DSA framework is also addressed, which makes use of Internet of things devices equipped with spectrum sensing modules as a means to feed spectrum occupation databases.

Estimation of Primary Channel Mean Period Based on State Transition Probability in Cognitive Radio

Primary channel mean period plays an important role in improving the performance of Dynamic Spectrum Access (DSA), because many algorithms to improve the performance of Cognitive Radio (CR) need to use the channel mean period as a prior knowledge. Secondary Users (SUs) can obtain statistics of the primary channel by spectrum sensing. However, SUs’ estimation of the mean period is inaccurate due to errors in the spectrum sensing in the real world, which will lead to performance degradation of CR systems. In this paper, we use a two-state Markov chain to model channel states, and use state transition probability to analyze the influence of sensing errors on the mean period. At the same time, we derive the estimation formula of the mean period of the original channel. Simulation results confirm that the proposed estimation method is superior to the existing estimation methods, and can accurately estimate the original period even with high sensing error probability.

Estimation of Distribution of Primary Channel Periods Based on Imperfect Spectrum Sensing in Cognitive Radio

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods&#x2019; distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods&#x2019; distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.