Primary User Activity Research Articles

Energy Efficient Multimeric Multichannel Routing Protocol for Cognitive Radio Ad Hoc Network

In recent times, wireless networks have seen unprecedented growth. This leads to shortage of scarce resources like radio spectrum. With the conventional approach, it is very tough to manage this frightening number of devices. Therefore, Cognitive Radio Ad Hoc Networks (CRAHNs) envisioned. In CRAHNs, the unlicensed users can communicate in licensed bands opportunistically when it is not in use by licensed users. A special routing protocol is required to implement this objective, as the conventional protocols proposed for mobile ad hoc network will not be suitable for CRAHNs. There are many additional routing challenges involved. For instance, the routing protocols must be dynamic to provide protection to primary users & it must be energy efficient since all the devices involved in conventional ad hoc networks are battery operated. This paper proposes an Energy Efficient Multimeric Multichannel routing protocol for CRAHNs. It is an on demand routing protocol which takes residual energy, the probability of primary user appearance on a channel and route cost as metrics to evaluate the best route between a source and destination. The proposed work is simulated on a custom simulator. The route establishment and route maintenance delay are observed alongside by varying primary users activities. The simulation results prove the efficiency of the proposed approach as it takes less time as compared to Dijkstra algorithm with random channel selection.

Building $k$ -Protected Routes in Multi-Hop Cognitive Radio Networks

In cognitive radio networks (CRNs), the established communication sessions between secondary users (SUs) may be affected or even get interrupted because the SUs need to relinquish a spectrum when the primary users (PUs) become active again. On detecting PU activities, the SUs on the interrupted links either switch to another link operating on another spectrum fragment using the same one-hop path, or the SUs seek for an alternative multi-hop backup path. In either approach, the communication session is destined to experience a delay or even gets interrupted, which is intolerable to quality of service-sensitive applications such as multimedia streaming or audio/video conferencing. In this paper, we study the problem of building ${k}$ -protected routes in CRNs. A ${k}$ -protected route consists of a set of main link s with preassigned backup links and backup path s so that the ${k}$ -protected route is guaranteed to endure ${k}$ PU appearances without being interrupted. For a CRN, we find a ${k}$ -protected route for each session request and maximize the number of sessions that the CRN can support. We propose both centralized and distributed algorithms for the ${k}$ -protected routing problem. Simulation results show that our ${k}$ -protected routes outperform existing opportunistic spectrum switching approaches regarding delay and interruption rate.

Long-term Measurement and Analysis of the Free Proxy Ecosystem

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country, anonymity level, and performance. These lists index hundreds of thousands of hosts discovered via automated tools and crowd-sourcing. A complex free proxy ecosystem has been forming over the years, of which very little is known. In this article, we shed light on this ecosystem via a distributed measurement platform that leverages both active and passive measurements. Active measurements are carried out by an infrastructure we name ProxyTorrent, which discovers free proxies, assesses their performance, and detects potential malicious activities. Passive measurements focus on proxy performance and usage in the wild, and are accomplished by means of a Chrome extension named Ciao. ProxyTorrent has been running since January 2017, monitoring up to 230K free proxies. Ciao was launched in March 2017 and has thus far served roughly 9.7K users and generated 14TB of traffic. Our analysis shows that less than 2% of the proxies announced on the Web indeed proxy traffic on behalf of users; further, only half of these proxies have decent performance and can be used reliably. Every day, around 5%--10% of the active proxies exhibit malicious behaviors, e.g., advertisement injection, TLS interception, and cryptojacking, and these proxies are also the ones providing the best performance. Through the analysis of more than 14TB of proxied traffic, we show that web browsing is the primary user activity. Geo-blocking avoidance—allegedly a popular use case for free web proxies—accounts for 30% or less of the traffic, and it mostly involves countries hosting popular geo-blocked content.

Cooperative spectrum sensing with energy harvesting

In cognitive radio networks, secondary nodes should sense the channel using spectrum sensing algorithms to detect primary activity. When primary user (PU) is idle, secondary users opportunistically transmit over this frequency hole. In this paper, we provide the detection probability (DP) of the energy detector (ED) for wireless energy harvesting systems. PU and relays harvest energy from radio frequency signal received from another node H. The harvested energy is used by PU to transmit its signal and by the relay to amplify PU signal to a fusion center (FC). The FC uses the relayed signal to detect PU activity using the ED. The major contribution of the paper is to show that the DP can be lower bounded using the cumulative distribution function of signal to noise ratio. This new approach is applied to compute the DP of the ED of cooperative spectrum sensing. The derived expressions of DP are valid for relays with arbitrary positions. The theoretical derivations are confirmed using simulation results obtained with MATLAB.

Clustering formation in cognitive radio networks using machine learning

The goal of spectrum sensing is to elevate the detection performance of secondary users (SUs) in a cognitive radio network (CRN). In cooperative spectrum sensing, all secondary users (SUs) of the network deliver their sensing measurement to the fusion center (FC) for the final decision regarding the activity of primary user (PU). The collaboration among large number of SUs might create overhead for the FC. To improve the performance of cooperative spectrum sensing, a novel method is proposed, which segregates the network into clusters. We have used artificial intelligence to make the clusters. The formation of clusters is made based on machine learning affinity propagation algorithm. Using proposed method, SUs share local messages with their neighbors until a highest class of cluster heads are chosen and a corresponding clustering configuration is made. The messages are evaluated depend on measures of similarity between the SUs, which are selected based on the objective of the clustering process. The sensing message of delimited number of SUs is shared with their cluster heads, which is ultimately shared with the FC for final decision. The proposed approach obtains the highest energy and performance efficiency in comparison with conventional clustering schemes.

Hybrid ARQ-CQI Feedback-Based Access Scheme in Cognitive Radio Networks

In this paper, we consider a cognitive radio (CR) network where the primary network’s feedback information is utilized to develop an access scheme for the secondary network to exploit the underutilized primary spectrum resources. Secondary users (SUs) identify the spectrum opportunities by sensing the spectrum for primary users (PUs) activities and by listening to the PUs feedback. The feedback signals monitored in this research work are the channel quality indicator (CQI) and automatic repeat request (ARQ) available in the PUs network. For detecting the PUs activities, SUs employ soft energy sensing, where SUs access the PUs’ channel with access probabilities that are based on the sensed PUs’ energy level. The access probabilities are optimized to maximize the SUs service rate while maintaining the PUs queues’ stability. The system is modeled as a three-dimensional Markov chain (MC) that captures the number of packets in the PUs queues and the state of the two observed PUs’ feedback signals. The performance of the system is evaluated by deriving the SUs service rate and the average PUs packet delay. We compare the performance of the proposed system with other baseline systems utilizing different types of PUs’ feedback signals. Results reveal the improvement in the SUs service rate and the PUs’ delay of the proposed system compared to the baseline systems. This improvement is mainly due to the fact that in our proposed system SUs have access to extra information, in terms of PUs feedback, as compared to other systems. Therefore, SUs in our proposed system can have better inference on the PUs’ activities; thus more collisions between the PUs and the SUs can be avoided, resulting in significant performance gains in terms of SUs’ throughput and PUs’ average delay.Part of this work was published in [1] .

Learning-Theoretic Multi-Channel Spectrum Sensing and Access in Full-Duplex Cognitive Radio Networks with Unknown Primary User Activities

The majority of the opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. In this paper, we consider the problem of optimal opportunistic multi-channel spectrum sensing and access using full-duplex (FD) radios in the presence of uncertain primary user (PU) activity statistics. A joint learning and spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely affected. To address this problem, we formulate a robust optimization problem. Our analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. We analyze the impact of the sensing period on the PU collision probability and the SU throughput, and find the optimal sensing period via convex optimization. We show that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies also demonstrate that the resulting robust solution provides a good trade-off between optimizing the SU's throughput and protecting the PU.

On the estimation of primary user activity statistics for long and short time scale models in cognitive radio

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, non-interfering manner with the primary network. DSA/CR systems utilize spectrum sensing techniques to sense the availability of Primary user (PU). CR users can benefit from the knowledge of PU activity statistics. In this work, comprehensive analysis of estimation of distribution of PU idle and busy periods is carried out using Generalized Pareto and Pareto distributions for long and short time scale models respectively and closed form expression is derived. Moreover, the impact of sensing periods on the accuracy of estimated PU idle/busy periods is studied. Furthermore, the error in proposed estimation of distribution of PU idle and busy periods is quantified using the Kolmogorov–Smirnov test. From this study we conclude that the proposed model is better fit for the real scenarios eliminating practical limitations. Mathematical analysis is substantiated with the simulation results.

A Switching-Based and Delay-Aware Scheduling Algorithm for Cognitive Radio Networks

In this article, an opportunistic inter-frame spectrum scheduler that maximizes the throughput of cognitive radio networks over a span of multiple time-slots is proposed. An optimization problem is formulated to find the optimum inter-frame scheduler while taking into account the switching delay, the primary user (PU) activity, historical information on the PU behavior, the channel quality as well as the secondary user (SU) status. Simulation results show that the proposed inter-frame scheduler significantly improved the overall aggregate throughput and average switching delay of the cognitive radio network when compared to the values obtained when scheduling is done on a slot-by-slot basis.

Effective capacity optimization for cognitive radio networks under primary QoS provisioning

Cognitive radios have emerged as a key enabler for opportunistic spectrum access, in order to tackle the wireless spectrum scarcity and under utilization problems over the past two decades. In this paper, we aim to enhance the secondary user (SU) performance while maintaining the desired average packet delay for the primary user (PU). In particular, we investigate the trade-off between delay-constrained primary and secondary users in cog- nitive radio systems. In the first part of this work, we use the hard-sensing scheme to make a decision on the PU activity and maximize the SU effective capacity subject to an average PU delay constraint. Second, we propose a soft-sensing scheme by dividing the PU energy interval where the PU is decided to be idle into multiple decision. We also maximize the SU effective capacity subject to an average primary user delay constraint; then, we present three modifications for the proposed soft-sensing scheme to allow for low complexity implementation that is comparable to the complexity of the hard-sensing scheme, but with better performance. The numerical results reveal interesting insights comparing our soft sensing to the hard-sensing models in terms of the optimal performance obtained from our optimization solution compared to the unconstrained PU delay baseline system studied earlier in the literature. For instance, the hard sensing system in Akin and Gursoy (IEEE Trans Wirel Commun 9(11):3354–3364, 2010) and Abdel-Malek et al. (CrownCom 156:30–42, 2015) yields a SU effective capacity of only 50 % of the ideal, perfect sensing system. On the other hand, we show that the soft sensing system yields almost 87 % of the perfect sensing performance (at a primary user arrival rate of $$\lambda _p = 0.1$$), which further increases for a larger number of decision sub-intervals.

Activity Pattern Aware Spectrum Sensing: A CNN-Based Deep Learning Approach

In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model. In this letter, we propose a convolutional neural network-based deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing algorithms, our proposed deep learning approach is data-driven and requires neither signal-noise probability model nor primary user (PU) activity pattern model. The proposed algorithm simultaneously takes in the present sensing data and historical sensing data, with which the inherent PU activity pattern can be learned to benefit the detection of PU activity. With extensive numerical simulations, results show that the proposed algorithm outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.

A route stability-based multipath QoS routing protocol in cognitive radio ad hoc networks

Secondary user (SU) mobility, primary user activity, and spectrum sensing inaccuracy are the main challenges to the stability of a quality of service (QoS) route in cognitive radio ad hoc networks (CRAHNs). In this work, a new routing protocol for CRAHNs, referred to as the route stability based multipath QoS routing protocol (SMQRP), is proposed. The proposed protocol finds the optimal primary and alternative paths between an SU source and an SU destination and guarantees the use of the optimal primary and alternative channels along the paths. A proposed routing metric (SMQ) used for path and channel decisions is applied to the proposed protocol. The main idea is to use different routing metrics to select QoS paths with higher stability. The SMQRP was compared in this category with other routing protocols and was evaluated based on CRAHNs parameters. The results of the simulations show significant improvements with respect to the average end-to-end delay, packet drop probability, and throughput.

Analysis of handoff delay for proactive spectrum handoff scheme with PRP M/G/1/K queuing system in cognitive radio networks

Spectrum handoff has a negative impact on the performance of cognitive users (CUs) in terms of handoff delay in cognitive radio (CR) networks. In this study, a pre-emptive resume priority (PRP) M/G/1/K queuing network model is proposed with a finite number of allowable interruptions for proactive decision spectrum handoff scheme in order to minimise the cumulative handoff delay (CHD) and total service time (TST) for CUs. The CHD and TST for different proactive decision handoff schemes: non-switching spectrum handoff, switching spectrum handoff, and random spectrum handoff are modeled under the proposed PRP M/G/1/K queuing network model. Comprehensive results of CHD and TST are obtained to compare the performances of the proactive decision handoff schemes under the proposed PRP M/G/1/K queuing network model. This study also presents an analytical framework to examine the effect of primary users activity and buffer size on spectrum handoff delay performance with a finite number of allowable interruptions in a CR network. Thereafter, the optimal buffer size (K) is estimated for the proposed PRP M/G/1/K queuing network model, which gives performance similar to the infinite buffer size PRP M/G/1 queuing model with negligible (<1%) error.

Spatial⁻Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things.

The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology that can address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatial positions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum.

A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension.

In this work, two novel methodologies for the multiband spectrum sensing in cognitive radios are implemented. Methods are based on the continuous wavelet transform (CWT) and the multiresolution analysis (MRA) to detect the edges of available holes in the considered wideband spectrum. Besides, MRA is also combined with the Higuchi fractal dimension (a non-linear measure) to establish the decision rule permitting the detection of the absence or presence of one or multiple primary users in the studied wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results present these two methods as effective options for detecting primary user activity on the multiband spectrum. The first methodology works for 95% of cases, while the second one presents 98% of effectivity under simulated signals of signal-to-noise ratios (SNR) higher than 0 dB.

Two Time-Scale Resource Management for Green Internet of Things Networks

It is expected that billions of objects will be connected through sensors and embedded devices for pervasive intelligence in the coming era of Internet of Things (IoT). However, the performance of such ubiquitous interconnection highly depends on the supply of network resources in terms of both energy and spectrum. Librating IoT devices from the resource deficiency, we consider a green IoT network in which the IoT devices transmit data to a fusion node over multihop relaying. To achieve sustainable operation, IoT devices obtain energy from both ambient energy sources and power grid, while opportunistically access the licensed spectrum for data transmission. We formulate a stochastic problem to optimize the network utility minus the cost on on-grid energy purchasing. The problem formulation takes into account the different granularity in the changing of harvested energy, power price, and primary user activities. To address the problem, we propose a Lyapunov-based framework to decompose the problem into different time scales, based on which an online two time-scale resource allocation algorithm, is developed which determines the harvested and purchased energy in a large time scale, and the channel allocation and data collection in a small time scale. Furthermore, we analyze the required data buffer and energy buffer to support the proposed algorithm. Extensive simulation results validate the correctness of the analysis and the efficiency of the proposed algorithm.

CogMOR-MAC: A cognitive multi-channel opportunistic reservation MAC for multi-UAVs ad hoc networks

With the rapid increase of the wireless applications, the cognitive radio network as a promising solution has attracted a lot of researches in recent years. One of the critical issues is to solve the rendezvous problem by using one radio. Especially, Unmanned Aerial Vehicles (UAVs), embedded devices or smart devices usually can only equip one radio for various reasons, including energy-saving, cost-saving and size-constraint. In this paper, we present a multi-channel cognitive MAC protocol, namely CogMOR-MAC, which is based on Multi-channel Opportunistic Reservation (MOR) mechanism. The MOR mechanism focuses on solving the rendezvous problem and improving the negotiation efficiency by using only one radio. And the presented MOR mechanism is also used to reduce the impact on frequency hopping and avoid the primary user activities. In the performance evaluation, typical metrics are considered on the performance, such as packet success rate, link latency, link throughput, channel utilization, etc. CogMOR-MAC is also compared with other multi-channel cognitive MAC protocol on performance. The result shows that CogMOR-MAC can adapt to Primary User (PU) activities environment more effectively and provide a reliable communication. Of course, it is not only suitable for the multi-UAVs ad hoc networks, but also can be applied to mobile ad hoc network or wireless sensor network, especially in the severely interfered environment.

Optimal Power Allocation of GFDM Secondary Links With Power Amplifier Nonlinearity and ACI

We investigate the problem of the rate maximization of a generalized frequency division multiplexing (GFDM) based secondary user (SU) link operating over a spectrum hole where adjacent channel interference (ACI) on two active primary users (PUs) in the right and left adjacent channels must be below a threshold. The SU transmitter has nonlinear power amplifier (PA) distortions. We consider a zero forcing (ZF) receiver which removes self-generated interference of GFDM. For a third-order nonlinear PA, we derive the signal-to-interference-plus noise ratio (SINR) and ACI. By using successive convex approximations, we develop a power allocation algorithm to maximize the SU rate subject to the ACI limits on adjacent PUs. Finally, we show that the proposed algorithm improves the SU data rate and that GFDM achieves a higher SU data rate than orthogonal frequency division multiplexing (OFDM).

A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

A large scale of spectrum sensing techniques are proposed to improve the use of spectrum resources. However, the EE (energy efficiency) should be guaranteed for both primary and secondary users, especially under various detection performance constraints. In this regard, the linking between activities of PU (primary user) and dynamic access behaviors of SUs (secondary users) should be considered in an integrated way. This survey has compared different existing scenarios and frameworks on EE optimizations. The principal objective is to enhance the system throughput and to coordinate on the physical layer of both PU and SUs, in order to enable a high-quality spectrum detection and a more efficient spectrum access. In the technical part, several optimization methods are introduced under PU’s constraints, and different methods based on game theory are applied to suitable cooperative sensing scenarios for SUs’ optimal access. Finally, the complexity of algorithms is compared, to further reduce the execution time and deploy real-time adaptation for users with lower delay.

Primary User-Aware Optimal Discovery Routing for Cognitive Radio Networks

Routing protocols in multi-hop cognitive radio networks (CRNs) can be classified into two main categories: local and global routing. Local routing protocols aim at decreasing the overhead of the routing process while exploring the route by choosing, in a greedy manner, one of the direct neighbors. On the contrary, global routing protocols choose the optimal route by exploring the whole network to the destination paying the flooding overhead cost. In this paper, we propose a primary user-aware $k$ -hop routing scheme where $k$ is the discovery radius. This scheme can be plugged into any CRN routing protocol to adapt, in real time, to network dynamics like the number and activity of primary users. The aim of this scheme is to cover the gap between local and global routing protocols for CRNs. It is based on balancing the routing overhead and the route optimality, in terms of primary users avoidance, according to a user-defined utility function. We analytically derive the optimal discovery radius ( $k$ ) that achieves this target. Evaluations on NS2 with a side-by-side comparison with traditional CRNs protocols show that our scheme can achieve the user-defined balance between the route optimality, which in turn reflected on throughput and packet delivery ratio, and the routing overhead in real time.