Unlicensed Users Research Articles

The Optimization Algorithm for CR System Based on Optimal Wavelet Filter

5G network is a heterogeneous large-scale network. Cognitive Radio (CR) technology can be used to realize selection based on the communication time, communication resources, and communication requirement so as to improve the system performance of the whole communication system. Cognitive Radio (CR) system based on wavelet packet owns better flexibility and bandwidth efficiency. The optimal wavelet packet filter optimization algorithm is proposed in the paper for guaranteeing un-licensed user’s (un-LU) data transmission rate and optimizing the performance of system. The intelligent search algorithm is used to obtain the optimal wavelet filter. The simulation results show that the Intercarrier Interference (ICI) and bit error rate (BER) performance of the new optimal wavelet filter algorithm without sacrificing any un-LU’s subcarriers is better than other three masking subcarriers algorithms.

Spectrum sensing technique of OFDM signal under noise uncertainty based on Mean Ambiguity Function for Cognitive Radio

In this paper, we consider the Spectrum Sensing (SS) problem in Cognitive Radio (CR). SS is an important task and one of the main functions of CR, as it has the ability to detect the presence/absence of a Primary User (i.e., PU, licensed user) signal in order to allow the Secondary User (i.e., SU, unlicensed user) to access PU frequency band using CR. The common purpose of all SS techniques is performing an efficient utilization of the available radio spectrum, thus reducing the spectrum scarcity problem. In this paper, we propose a new SS technique of OFDM (Orthogonal Frequency Division Multiplexing) signal based on Mean Ambiguity Function (MAF). Furthermore, we exploited some specific algebraic properties of OFDM signal to propose a test statistics for the detector. The theoretical expressions of the OFDM signal MAF and threshold are also derived. The calculation of threshold expression is based on the Probability Distribution Function (PDF) of the proposed test statistics. In addition, we studied the effect of different parameters, e.g., low SNR (Signal to Noise) values, noise uncertainty and number of OFDM symbol on the detection performance of the proposed detector. Moreover, the obtained simulations results are compared to the Energy Detector (Urkowitz, 1967) and a recent spectrum sensing technique of OFDM signal; DC − OFDM detector (Shi et al., 2015). Furthermore, the proposed detector is also implemented and tested using Universal Software Radio Peripheral (USRP) devices.

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

Cognitive radio networks improve spectrum efficiency by employing vigilant and accurate spectrum management techniques. This is done to enable an unlicensed user to use the underutilized spectrum. Spectrum sensing determines if a spectrum hole exists for the unlicensed user, this is accomplished using various techniques such as energy detection, minimum Eigen value detection, and matched filter technique. Conventional energy detection techniques do not achieve high values of detection; hence an improved adaptive energy detection technique has been proposed to improve the efficiency. The proposed methodology exhibits better numerical results than conventional techniques. Another important spectrum management procedure is spectrum handoff. The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.

An Adaptive Coverage Enhancement Scheme Based on mmWave RoF for Future HetNets

For future heterogeneous networks, network coverage is identified as one of the main challenges. This paper focuses on the spectrum sharing and energy consumption problems with millimeter wave radio over fiber (mmWave RoF) utilized to enhance the coverage of the system. The proposed adaptive scheme includes the following steps. First, the system identifies the users as licensed users that primarily own the specific spectrum and unlicensed users that have to purchase the spectrum. Based on the game theory, the spectrum resource is shared among the eNB and low power nodes (LPNs) with a relatively high benefit. Second, an adaptive sleep schedule scheme is performed to reduce the power cost of some LPNs that are scored under the threshold. Third, an adaptive power allocation scheme is utilized to further reduce the power assumption of the system and does not affect the system demand satisfaction. The simulation results finally show that the proposed resource allocation scheme can achieve improved coverage of the network.

A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

Cognitive radio network (CRN) is the next generation wireless network that allows unlicensed users [secondary users (SUs)] to explore and use the underutilised licensed channels (white spaces) owned by licensed users (primary users). The purpose is to increase the spectrum utilization for enhanced network performance. Clustering segregates SUs in a CRN into logical groups (clusters) with each consisting of a leader (cluster head) and member nodes. A budget-based cluster size adjustment scheme is applied to enable each cluster to adjust its number of member nodes in its cluster based on the availability of white spaces in order to improve network scalability. However, cluster size adjustment is prone to attacks by malicious SUs that launch random and intelligent attacks. Hence, we incorporate an artificial intelligence approach called reinforcement learning (RL) into a trust model to countermeasure the random and intelligent attacks. The simulation results show that RL-based trust model increases the utilization of white spaces and cluster size to improve network scalability and enhance network performance despite the presence of RL-based intelligent attacks.

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.

Mitigating the interferences and intruders in cognitive radio networks using an authentication protocol over Reed-Solomon codes

Cognitive radio networks have emerged as a promising solution for spectrum scarcity problem. Secondary users (unlicensed users) accessing cognitive radio network suffer from continuous unintentional interference from primary users (licensed users). Also, the adversary present in the channel tries to thwart the secondary users' communication. To address these two problems, an inherent authentication protocol is defined over Reed-Solomon codes. Reed-Solomon codes with authentication protocol are used with M-ary frequency shift keying modulation technique, for transmissions over cognitive radio network. Similarly, the received message is demodulated and decoded to get the original information bits at the receiver. The performance of the proposed scheme is given in terms of bit error rate. The experimental results show that the proposed system performs better in terms of BER reduction. Also, it is shown that the proposed scheme is resistant to replay attack, man-in-the-middle and impersonation kind of attacks.

Analysis of Efficient Cognitive Radio MAC Protocol for Ad Hoc Networks

Cognitive Radio (CR) is an emerging technology to exploit the existing spectrum dynamically. It can intelligently access the vacant spectrum frequency bands. Although a number of methodologies have been suggested for improving the performance of CR networks, little attention has been given to efficient usage, management and energy efficiency. In this paper, a modern paradigm pertaining to the spectrum allotment and usage, manifested as CR, has been introduced as a potential solution to this problem, where the CR (unlicensed) users can opportunistically deploy the available free licensed spectrum bands in such a way that restricts the degree of interference to the extent that the primary (licensed) users can allow. In this article, we analysis and compare various protocols, in addition, we evaluate CREAM MAC, RMC MAC, SWITCH MAC, EECR MAC protocols related to the CR MAC in term of different parameters such as throughput, data transmission and time efficiency. We conclude the most efficient protocol, which have similar features named as Proposed Efficient Cognitive Radio MAC (PECR-MAC) protocol.

Spectrum Sensing in Cognitive Radio Using Actor–Critic Neural Network with Krill Herd-Whale Optimization Algorithm

Spectrum sensing is the active research area in the Cognitive radio networks that initiates the effective data sharing between the licensed and the unlicensed users of Cognitive Network. Maximizing the detection probability for a provided false alarm rate is a hectic challenge of most of the spectral sensing methods. The paper proposes a spectral sensing method, termed as Krill-Herd Whale optimization-based actor critic neural network. The unoccupied spectrum is optimally determined using the proposed method that allocates the free spectrum bands to the primary users instantly such that the delay is minimized due to the effective functioning of the fusion center. For the effective sensing, the Eigen-value-based cooperative sensing is activated in the cognitive radio. The analysis of the proposed method is progressed based on the performance metrics, such as false alarm probability and detection probability. The proposed spectral sensing method outperforms the existing methods that yield a maximum probability of detection and minimum probability of false alarm at a rate of 0.9805 and of 0.009.

An efficient spectrum handoff decision making scheme for cognitive radio networks

There has been a gigantic spike in the usage and development of wireless devices since the time wireless technology has come into existence. This has contributed to a very serious problem of spectrum unavailability or spectrum scarcity. The solution to this problem comes in the form of cognitive radio networks where the secondary user which is also known as the unlicensed users makes use of the spectrum in an opportunistic manner. The secondary user uses the spectrum in a manner such that the primary or the licensed user does not face interference above a threshold level of tolerance. Whenever a primary user comes back to reclaim its licensed channel, the secondary user using it needs to perform a spectrum handoff to another channel which is free of primary user. Our primary focus is on performing spectrum handoff decision making. The SU selects staying or changing policy based on the average extended delivery time. The spectrum handoff decision making is performed, i.e., the optimal channel for spectrum handoff is decided only if changing policy is adopted which is based on multiple attribute decision making (MADM). This spectrum handoff decision making scheme is later extended using artificial neural networks (ANN) and probabilistic Markov model.

A Review on Spectrum Allocation in Cognitive Radio Network

Cognitive radio is a recent technology that enhances the usage of spectrum resources. Efficient assignment of unused spectrum to unlicensed users is an important problem. Spectrum allocation is an ...

Signal technique for friend or foe detection of intelligent malicious user in cognitive radio network

In cognitive radio networks, security issues arise when intelligent malicious users can attack and decrease the permitted channel for unlicensed users (secondary users). In this paper, we propose a novel scheme, based on friend or foe (FoF) detection technique with physical layer network coding, to enable discrimination between secondary users and intelligent malicious users. Theoretical expressions are derived for probabilities of detection of secondary user, miss detection, and false alarm. In addition, the effectiveness of the proposed approach is evaluated by theoretical analysis and Monte Carlo simulation. Furthermore, an algorithm is proposed for distinguishing between secondary user and intelligent malicious user. Finally, based on the outcome of simulation of probabilities and normalised cross-correlation, it is determined that the proposed scheme outperforms in terms of orthogonal frequency-division multiplexing (OFDM) signal compared with quadrature phase-shift keying (QPSK) signal over cognitive radio network.

Novel QoS-Aware Proactive Spectrum Access Techniques for Cognitive Radio Using Machine Learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%.

Frequency Reuse for Coexistence of Secondary Users and Optimum Utilization of Bandwidth

Cognitive radio technology is a promising technology to address the spectrum underutilization of licensed users, called primary users and extreme requirement of unlicensed users, called secondary users. Day to day spectrum requirement of secondary users is being increased dramatically and results in increased blocking and dropping. Call dropping is more exasperating than call blocking. So to reduce the call dropping of secondary users, some of the researchers proposed channel reservation techniques. But they got improved dropping results at the cost of increased blocking. So frequency reuse is proposed in this paper, which improves both blocking and dropping.

Analysis of Spectrum Sensing Error and Spectrum Handoff for Non-Uniform Unlicensed Users in Cognitive Radio Network

Analysis of Spectrum Sensing Error and Spectrum Handoff for Non-Uniform Unlicensed Users in Cognitive Radio Network

Wireless Transport Protocol Variants for Cognitive Radio Networks

Scarce wireless resources, lead to development of cognitive radio network as a solution to unlicensed users communication in the licensed frequency band. In response to the behavior of licensed users communication, unlicensed users communication need to change from one frequency band to another band. In this communication paradigm, the performance of unlicensed users transmission control protocol gets degraded due to the features of cognitive radio network. To overcome this, several authors suggested quite a few modifications in the existing wireless transport protocol for cognitive radio network environment. This paper gives an overview of different transport protocols used for unlicensed users’ communication in cognitive radio networks

Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.

Efficient Advanced Encryption Standard for Securing Cognitive Radio Networks

During the last decade, the CR (Cognitive Radio) came into view as a major wireless technology to resolve the issue of spectrum secrecy and efficient spectrum utilization. However, due to unlicensed (secondary) users, there are various security threats to the CRN (Cognitive Radio Networks). Some malicious users may access the CRN and mislead the secondary users to vacate the occupied channel, which may stop the communication. In this work, we propose a new cryptographic-based algorithm, CRAES (Cognitive Radio-Advanced Encryption Standard), inspired by the traditional AES to secure the CRN. The data of the primary and secondary users is encrypted at the transmitter and decrypted at the receiver. Unlike the conventional AES, we introduce the data-dependent key-generation and shift-rows process. We also reduce the rounds of AES from 10-6 to improve the computational efficiency without compromising the overall security. The experimental results demonstrate the effectiveness of the proposed CR-AES in terms of better security, reliability, and computational efficiency.

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

Low complexity spectrum sensing technique for cognitive radio using Farrow Structure Digital Filters

In recent years, Cognitive Radio (CR) is found to be a viable solution to meet the next generation spectrum utilization challenges. The basic requirement for CR is spectrum sensing, in which the unlicensed users or secondary users (SU) need to continuously monitor the spectrum for the presence of authorized users or primary users (PU). The spectral efficiency of a CR system depends on the implementation of the spectrum sensing part such that the smallest possible inactive portion of the spectrum is identified. Most of the filter bank based spectrum sensing methods available in the literature are based on fixed filter banks. In this paper, the idea of variable bandwidth filter, based on Farrow structure, is exploited to implement the spectrum sensing technique. It is seen that the proposed method is more efficient than the existing filter bank based techniques and the hardware complexity is drastically reduced.