Cognitive radio

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Multimedia Content Delivery for Remote Patient Monitoring using Cognitive Radio Networks

Multimedia Content Delivery for Remote Patient Monitoring using Cognitive Radio Networks

Fig. 1. Cognitive radio medium access method III. Cognitive radio delay analysis A. Theoretical analysis In CRN, we have two types of users: Primary Users (PU) who have the license to use bands and Secondary Users (SUs) also called cognitive radio users that explore opportunistically the free frequency bands. The sensing module of cognitive radio allows the detection of free bands and the sharing module allows to control their access. Figure 1 depicts the medium access process in cognitive radio networks. Sensing phase with duration t1, that occurs before any transmission seems very impor- tant. More this phase (t1 ) is longer, more it has better detection results of free spectrum and better control of interferences. Signalling phase corresponds to the period of information exchange, necessary for the communication session initiation. It allows to reach an agreement on the criteria of communication (e.g. frequency band), taking into account applications constraints. Also, a significant lapse of time should be reserved for data transmission after signalling and a time for acknowledgement as shown in figure 1. A "Time-slot" (t1+t2 ) is the total time given to a cognitive radio user for transmitting a data packet. This time-slot is obviously longer than in standards networks where medium access process corresponds to the time t2 of figure 1. As shown in Figure 1, there is a significant latency and delay linked to the sensing that could affect the quality of real-time medical video transmission in cognitive radio networks.
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Statistical Admission Control in Multi-Hop Cognitive Radio Networks

Statistical Admission Control in Multi-Hop Cognitive Radio Networks

Cognitive Radio Networks A main goal of any cognitive radio architecture is to protect the primary users from interference [9]. At the routing layer, a node is required to adapt its path computations according to the primary user activity. To this end, it can either route around the primary user, thus potentially increasing the path length, or, switch its transmission channel on the affected links [15]. Obviously, both strategies will increase the end-to- end delay. An optimal routing metric for multi-hop cognitive radio networks is proposed in [16]. The authors analytically demonstrate its optimality and accuracy for the cases of mobile and static networks. While the works presented so far are shown to handle well the primary users, none of them addresses the problem of admission control for quality of service. Works closer to the problem considered in our work can be found in [17] and [18] where algorithms for joint routing, link scheduling and spectrum assignment have been studied. Nevertheless, the problem of computing the end-to-end bandwidth of a multih-hop path is not addressed in any of these works.
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Medium Access Control for Dynamic Spectrum Sharing in Cognitive Radio Networks.

Medium Access Control for Dynamic Spectrum Sharing in Cognitive Radio Networks.

Extended Summary The proliferation of wireless services and applications over the past decade has led to the rapidly increasing demand in wireless spectrum. Hence, we have been facing a critical spec- trum shortage problem. Several recent measurements have, however, reported that a large portion of the licensed radio spectrum is very underutilized in spatial and temporal domains [ 7 ], [ 1 ]. These facts have motivated the development of cognitive radio (CR) techniques for dynamic spectrum access to enhance the spectrum utilization [ 2 ]. To achieve this goal in the popular hierarchical spectrum access scenario, secondary users (SUs) can opportunis- tically exploit spectral holes for data transmissions while not interfering the transmissions of primary users (PUs). Toward this end, SUs can perform spectrum sensing to explore spectrum holes and adopt suitable spectrum access mechanisms to share the discovered available spectrum with each other [ 8 ]. Although the spectrum sensing and access functions are tightly coupled, they have not thoroughly been treated in the existing cognitive radio literature. Moreover, it is desirable to deploy a distributed cognitive MAC protocol for spec- trum sharing in many wireless applications, which is usually more cost-efficient compared to the centralized cognitive MAC counterpart. This dissertation aims to perform distributed cognitive MAC protocol engineering with extensive performance analysis and optimization for several practically relevant cognitive network settings.
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Optimization of spectrum utilization parameters in cognitive radio using genetic algorithm

Optimization of spectrum utilization parameters in cognitive radio using genetic algorithm

Abstract The dramatically development of wireless technologies in the last few decades, leads to the growth of channel resources demand in a limited spectrum with inextensible character. Cognitive radio network (CR) is a promising technology that provides solutions for the spectrum management and optimization problems via dynamic spectrum management. The spectrum resources management and optimization are an important part of the future network performances. In this paper, we propose an efficient algorithm to examine the design specification issues regarding the choice of optimal power, optimal speed, and optimal amount of information in a wireless network along with studying the effect of different parameters on the obtained results. Our objectives are to guarantee the protection on licensed users (Primary users ‘PU’) from harmful interference caused by the unlicensed users (Secondary users ‘SU’), more especially, to optimize the quality of communication link, Transmission levels, and battery life of the wireless devices. Results show that our proposed work leads to an efficient utilization of radio spectrum and strongly contributes to alleviating the spectrum scarcity problem.
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Modeling, Design and Analysis of Multi-Channel
Cognitive Radio Networks.

Modeling, Design and Analysis of Multi-Channel Cognitive Radio Networks.

(a) Wearable Technologies (b) Video on-demand (c) Learning (d) Smart cities (e) Conferencing (f) Virtual group activities Figure 1.1: Some 5G applications. urban locations in Europe. This has motivated the research community to reconsider a different paradigm in utilizing the valuable spectrum. This paradigm that is known as dy- namic spectrum allocation (DSA), loosely speaking, cognitive radio (CR), is empowered by a new architectural framework called software defined radio (SDR). DSA is believed to be the remedy for BW insufficiency and inefficiency in a more foreseeable future com- pared to the mmW rival technology. Despite the common misunderstanding, CR and DSA are not precisely the same. More accurately, DSA is a subsidiary of CR, a big one, where radio adaptation to the environment is only restricted to the operating frequency. On the other hand, the realm of adaptation in CR has no bound, including the operating frequency, transmit power, transmit mode and beyond. Having this narrow, yet impactful, distinction in mind, we will use the two terms interchangeably in this thesis.
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Priority queueing models for cognitive radio networks with traffic differentiation

Priority queueing models for cognitive radio networks with traffic differentiation

Abstract In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and used to illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation.
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Enabling the Implementation of Spatial Interweave LTE Cognitive Radio

Enabling the Implementation of Spatial Interweave LTE Cognitive Radio

widely adopted around the world as near 4G standard. Be- sides, cognitive radio (CR) which enables smart use of ra- dio resources, is a key ingredient to achieve high spectral ef- ficiency [2]. Consequently, it is a natural choice to exploit the LTE specifications in CR (CR-LTE) in order to enable the wide adoption of cognitive radio and eventually to optimized the spectrum management in wireless systems. In addition, the LTE standard has been designed with a high flexibility which allows the integration of innovative technologies like CR.

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On the Performance of Calibration Techniques for Cognitive Radio Systems

On the Performance of Calibration Techniques for Cognitive Radio Systems

3 EURECOM, Sophia Antipolis, France Email: kouassi@i3s.unice.fr, irfan.ghauri@intel.com, bassem.zayen@eurecom.fr, deneire@i3s.unice.fr Abstract—In Cognitive Radio (CR) systems, primary licensed and secondary unlicensed users share the same spectrum. To minimize the interference caused by secondary users to primary users, we use Beamforming (BF). To perform BF in time division duplex (TDD), we acquire Channel State Information (CSI) with the help of channel reciprocity. This reciprocity is in practice not perfect due to non reciprocal Radio Frequency (RF) front- ends, this non reciprocity can be compensated by calibration algorithms, using only CSI, pilots and signalling. This paper 1
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Advanced metering infrastructure backhaul reliability improvement with cognitive radio

Advanced metering infrastructure backhaul reliability improvement with cognitive radio

Fig. 2. Simplified cognitive cycle A cognitive radio has sensors which are used to acquire knowledge of its environment. The result of the sensing is used to make decisions and to reconfigure the radio frequency equipment. Cognitive radio is usually used for dynamic spec- trum access (DSA) [12]. In a DSA scenario, secondary users want to access to licensed channels unused by primary users. Before accessing a channel, a secondary user must sense if a primary user uses this channel. With the sensing result, the secondary user decides if it access to this channel or chooses another channel for data transmission.
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Coalition formation game for cooperative cognitive radio using Gibbs Sampling

Coalition formation game for cooperative cognitive radio using Gibbs Sampling

I. I NTRODUCTION Towards a more efficient utilization of the wireless spec- trum, there has been recent interest in employing cooperation among secondary nodes in cognitive radio networks, and modeling it using game theoretic tools [ 1 ]-[ 5 ]. Cooperation in cognitive radio was initially considered from a collaborative sensing and access perspective in [ 1 ]-[ 3 ], where secondary users were considered transparent to the primary users. The secondary users actively listen to the primary users’ channels and opportunistically transmit when the primary users’ chan- nels are idle. More specifically in [ 1 ], the secondary users collaborate to improve their sensing information. In [ 2 ], the tradeoff between channel sensing and channel access time is captured, and the secondary users form coalitions within which they share their channel sensing information in order to improve their channel sensing and access time. Both problems in [ 1 ] and [ 2 ] are formulated as coalition formation games, and a distributed algorithm for coalition formation that adapts based on topology and environment changes is presented. In [ 3 ], the problem of collaborative spectrum sensing is formulated as an evolutionary game, where each secondary user can choose whether to disclose its sensing information to others or not, and the objective is to study the behaviour of selfish secondary users who make use of other secondary users sensing information in order to maximize their channel access time.
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Tunable diplexer for cognitive radio RF front-end modules

Tunable diplexer for cognitive radio RF front-end modules

discriminator–locked tunable oscillator. Moreover, tunable RF front-end components that have largely been studied and implemented for the future software defined cognitive radio systems (SDCR) are RF filters [2], [3], couplers [4], [5] ], and demodulators [6]. In this letter, a varactor-tuned planar dual-mode bandpass filter is used to design a tunable diplexer. Simulations and measurements results of S-parameters and 1-dB compression point for different biased voltage are also presented.

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Opportunistic Spectrum Access with Channel Switching Cost for Cognitive Radio Networks

Opportunistic Spectrum Access with Channel Switching Cost for Cognitive Radio Networks

Despite of the similarity to the MAB problem, the spec- trum access problem in cognitive radio networks has several specificities that make it especially challenging to tackle. One major specialty lies in the fact of multiple SUs that can cause collisions if they simultaneously access the same channel. Some recent work has investigated this issue, among which Anandkumar et al. proposed two algorithms with logarithmic regret, where the number of SUs is known [9] and unknown and estimated by each SU [10], Liu and Zhao developed a time-division fare share (TDFS) algorithm with convergence and logarithmic regret [11].
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Optimal power allocation policies in multi-hop cognitive radio networks

Optimal power allocation policies in multi-hop cognitive radio networks

This work has been partly supported by IRCICA USR 3380. between the two devices by considering an overall power budget [9]. Furthermore, a cognitive radio context is assumed in which the opportunistic user (e.g., a device-to-device link) is allowed to communicate over the primary spectrum provided the primary link (e.g., a cellular user link to the base station) is not disturbed. We study a minimum Quality of Service (QoS) constraint to protect the primary user [10], different than the more common maximum interference constraints [11], allowing the secondary user to transmit as long as the primary user achieves its desired target Shannon rate. Throughout this paper, we focus on CF and DF; AF is not considered here due to its poor performance in multi-user interference settings (the relay amplifies not only the useful signal but also the noise plus interference).
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Mitigating the Hospital Area Communication's Interference using Cognitive Radio Networks

Mitigating the Hospital Area Communication's Interference using Cognitive Radio Networks

4) Simulation results: Figure 4 (a) shows results for a scenario where over a single channel network we modify the overlap size and study its impact on medical device receivers. We consider Uniform and Poisson distributions to deploy hospital receiver nodes around the PT radio emitter. The ratio of impacted medical receivers by the CMs transmissions grows linearly from 30% to more than 90% when the overlap size increases as observed in Figure 4 (a). Figure 4 (b) corresponds to the scenario where two channels are available. These result shows that the multichannel exploration with cognitive radio could help reducing interference. In fact, unlike to the single channel scenario, the ratio of impacted medical devices varies from almost 34% to almost 40% on channel1 and from 10% to almost 15% on channel2. Also, we observe that the ratio of affected medical devices is almost independent of the number of medical receivers and is not related to the distribution type. These important observations reinforce our principle that consists in dynamic transmission power adaptation according to the interference limit and CM radio position.
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Mitigating the Hospital Area Communication's Interference using Cognitive Radio Networks

Mitigating the Hospital Area Communication's Interference using Cognitive Radio Networks

4) Simulation results: Figure 4 (a) shows results for a scenario where over a single channel network we modify the overlap size and study its impact on medical device receivers. We consider Uniform and Poisson distributions to deploy hospital receiver nodes around the PT radio emitter. The ratio of impacted medical receivers by the CMs transmissions grows linearly from 30% to more than 90% when the overlap size increases as observed in Figure 4 (a). Figure 4 (b) corresponds to the scenario where two channels are available. These result shows that the multichannel exploration with cognitive radio could help reducing interference. In fact, unlike to the single channel scenario, the ratio of impacted medical devices varies from almost 34% to almost 40% on channel1 and from 10% to almost 15% on channel2. Also, we observe that the ratio of affected medical devices is almost independent of the number of medical receivers and is not related to the distribution type. These important observations reinforce our principle that consists in dynamic transmission power adaptation according to the interference limit and CM radio position.
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Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

In the literature, several papers survey the usage of SS for CR. In [ 22 ], the authors presented many aspects of spectrum sensing from a cognitive radio perspective. However, this paper is published more than 12 years ago and did not address the recent applications and paradigms. [ 23 ] surveys FDCR technique by focusing on the concurrent transmit– sense mode while other techniques, such as transmit–receive, were not covered. Ref. [ 24 ] details the challenges of applying CR in IoT networks by focusing on the issues related to SS. [ 25 ] surveys the techniques of SS with a focus on wideband and compressive sensing. In [ 26 ], the authors survey the recent techniques of SS by highlighting the mathematical models deriving the SS metrics (detection and false alarm probabilities). However, recent paradigms, such as Full-Duplex, and recent applications, such as the Internet of Things, are not addressed. The work in [ 27 ] is limited to technical issues related to the application of CR in IoT. Finally, the authors of [ 18 ] investigate the use of CR for 5G communication without further explanation of recent development in SS.
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Wideband signal detection for cognitive radio applications with limited resources

Wideband signal detection for cognitive radio applications with limited resources

Keywords: Wideband signal detection, Cognitive radio, Small sample size, Dempster-Shafer theory of evidence 1 Introduction With the evolution and development of various wireless technologies, spectrum resources are becoming scarce due to the increasing need for spectral bandwidth and number of users. Cognitive radio (CR) technology has attracted a lot of interest, especially for the next gener- ation of wireless communications, many types of radar systems and wireless sensor network (WSN) [ 1 – 4 ]. In all those systems, wideband signals are expected to be used to achieve the required quality of service (QoS). There- fore, wideband signal detection plays an important role in a wide range of wireless communication systems and has been identified as one of the most challenging problems in the CR technology applications [ 5 – 7 ].
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Adaptive non-uniform sampling of sparse signals for Green Cognitive Radio

Adaptive non-uniform sampling of sparse signals for Green Cognitive Radio

Abstract Based on previous results on periodic non-uniform sampling (Multi-Coset) and using the well known Non-Uniform Fourier Transform through Bartlett's method for Power Spectral Density estimation, we propose a new smart sampling scheme named the Dynamic Single Branch Non-uniform Sampler. The idea of our scheme is to reduce the average sampling frequency, the number of samples collected, and consequently the power consumption of the Analog to Digital Converter. In addition to that our proposed method detects the location of the bands in order to adapt the sampling rate. In this paper, through we show simulation results that compared to classical uniform sampler or existing multi- coset based samplers, our proposed sampler, in certain conditions, provides superior performance, in terms of sampling rate or energy consumption. It is not constrained by the inexibility of hardware circuitry and is easily recongurable. We also show the eect of the false detection of active bands on the average sampling rate of our new adaptive non-uniform sub-Nyquist sampler scheme. Keywords: Non-Uniform sub-Nyquist sampling, Software Radio, Cognitive radio, Non-Uniform spectrum sensing.
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Strategic Surveillance Against Primary User Emulation Attacks in Cognitive Radio Networks

Strategic Surveillance Against Primary User Emulation Attacks in Cognitive Radio Networks

VII. C ONCLUSION We have discussed the surveillance process to mitigate multi-channel selfish PUE in cognitive radio networks. Two scenarios are considered: the selfish PUE attack with and without the fallow set. By monitoring the occupied channels, the network manager can detect the selfish attacker. The relationship between the selfish attacks and the surveillance process is analyzed by game-theoretic approaches. Through appropriate modeling of the strategic interaction between a de- fender and an attacker, we investigated the commitment model. In this model, the defender takes the lead by committing to a surveillance strategy. To maximize the expected payoff, the rational attacker is forced to become a follower responding to the strategy used by the defender. The relevant strategies of the surveillance process are invested through the SSE. Analytical and numerical results show that the defender’s expected payoff is significantly improved when the defender commits to a surveillance strategy. Moreover, the computation time required to find the equilibrium point is lower in the commitment case than in the non-commitment case. We conclude that the defender should exploit the leader position in the game by committing to a defense strategy. This method can be generalized to address other types of PUEs such as malicious or unknown-attacking-type attacks.
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CorteXlab: A Cognitive Radio Testbed for Reproducible Experiments

CorteXlab: A Cognitive Radio Testbed for Reproducible Experiments

Universit´e de Lyon, INRIA, INSA-Lyon, CITI-INRIA, F-69621, Villeurbanne, France Email: leonardo.cardoso@insa-lyon.fr Abstract—The efficiency and potential gain of cognitive radio and more generally opportunistic cooperative communications have been already demonstrated from a theoretical point of view and supported by various simulation results. Beyond these promising results, several questions remain open from a practical point of view. Addressing these issues is not straightforward because deploying complex heterogeneous systems for cooperative scenarios is tedious, time consuming and hardly reproducible. We propose to make a step in this direction by offering a new ex- perimental facility, called CorteXlab, that allows complex multi- node cognitive radio scenarios deployment from anywhere in the world. Our objective is neither to design new software defined radio (SDR) nodes nor to propose a new software framework, but rather to provide a comprehensive access to a large set of high performance SDR nodes. The CorteXlab facility offers a 167 m 2 electromagnetically (EM) shielded room and integrates a set of 24 universal software radio peripherals (USRPs) from National Instruments, 18 PicoSDR nodes from Nutaq and 42 IoT- Lab wireless sensor nodes from Hikob. CorteXlab is built upon the foundations of the SensLAB testbed and also exploits the free and open-source toolkit GNU Radio. Automation in scenario deployment, experiment start, stop and results collection is performed by an experiment controller, called Minus. CorteXlab is in its final stages of development and is already capable of running specific test scenarios. In this contribution, we show that CorteXlab is able to easily cope with the usual issues faced by other testbeds providing a reproducible experiment environment for CR experimentation.
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