14,643 research outputs found
Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential enabling functionality for cognitive radio
networks to detect spectrum holes and opportunistically use the under-utilized
frequency bands without causing harmful interference to legacy networks. This
paper introduces a novel wideband spectrum sensing technique, called multiband
joint detection, which jointly detects the signal energy levels over multiple
frequency bands rather than consider one band at a time. The proposed strategy
is efficient in improving the dynamic spectrum utilization and reducing
interference to the primary users. The spectrum sensing problem is formulated
as a class of optimization problems in interference limited cognitive radio
networks. By exploiting the hidden convexity in the seemingly non-convex
problem formulations, optimal solutions for multiband joint detection are
obtained under practical conditions. Simulation results show that the proposed
spectrum sensing schemes can considerably improve the system performance. This
paper establishes important principles for the design of wideband spectrum
sensing algorithms in cognitive radio networks
Energy efficient cooperative spectrum sensing techniques in cognitive radio networks.
Master of Science in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.The demand for spectrum is increasing particularly due to the accelerating growth in
wireless data traffic generated by smart phones, tablets and other internet access devices.
Most of prime spectrum is already licensed. The licensed spectrum is underutilized or
used inefficiently, i.e. spectrum sits idle at any given time and location. Opportunistic
Spectrum Access (OSA) is proposed as a solution to provide access to the temporarily
unused spectrum commonly known as white spaces to improve spectrum utilization,
increase spectrum efficiency and reduce spectrum scarcity. The aim of this research is to
investigate potential impact of cooperative spectrum sensing techniques technologies on
spectrum management. To fulfill this we focused on two spectrum sensing techniques
namely; Firstly energy efficient statistical cooperative spectrum sensing in cognitive radio
networks, this work exploits the higher order statistical (HOS) tests to detect the
status of PU signal by a group of SUs. Secondly, an optimal energy based cooperative
spectrum sensing in cognitive radio networks was investigated. In this work the performance
of optimal hard fusion rules are employed in SUâs selection criteria and fusion
of the decisions under Gaussian channel and Rayleigh channels. To optimize on the
energy a two stage fusion and selection strategy is adopted to minimize the number of
collaborating SUs
Collaborative spectrum sensing in cognitive radio networks
The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government
agencies. With the explosive emergence of wireless applications, the demands for the
RF spectrum are constantly increasing. On the other hand, it has been reported that localised
temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an
innovative technology designed to improve spectrum utilisation by exploiting those spectrum
opportunities. This ability is dependent upon spectrum sensing, which is one of most critical
components in a cognitive radio system. A significant challenge is to sense the whole RF
spectrum at a particular physical location in a short observation time. Otherwise, performance
degrades with longer observation times since the lagging response to spectrum holes implies
low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing
technique is prime important.
In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs
multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband
spectrum sensing. The key features of the MASS system are, 1) low implementation complexity,
2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack
of time synchronisation. The conditions under which recovery of the full spectrum is unique
are presented using compressive sensing (CS) analysis. The MASS system is applied to both
centralised and distributed cognitive radio networks. When the spectra of the cognitive radio
nodes have a common spectral support, using one low-rate ADC in each cognitive radio node
can successfully recover the full spectrum. This is obtained by applying a hybrid matching
pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal
matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP).
Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary
users from a small number of measurements without ever reconstructing the full spectrum.
To achieve a better detection performance, a data fusion strategy is developed for combining
sensing data from all cognitive radio nodes. Theoretical bounds on detection performance
are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise
(AWGN), Rayleigh fading, and log-normal fading channels.
In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency,
good data compression capability, and are applicable to distributed cognitive radio
networks
MULTI USER COOPERATION SPECTRUM SENSING IN WIRELESS COGNITIVE RADIO NETWORKS
With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation.
This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency.
To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher.
Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance.
Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads.
Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighboursâ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes
Energy-Efficient Cooperative Spectrum Sensing based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks Normal
Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.This work was supported in part by the DICYT Project, Direction of Research, Development and Innovation, Universidad de Santiago de Chile, USACH, under Grant 061813KC, in part by the CONICYT-PFCHA/Doctorado Nacional/2016-21160292, and in part by the Spanish National Project TERESA-ADA (MINECO/AEI/FEDER, UE) under Grant TEC2017-90093-C3-2-R
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