570 research outputs found
Wideband cyclostationary spectrum sensing and characterization for cognitive radios
Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques
Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio
The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing
Compressive Sampling as an Enabling Solution for Energy-Efficient and Rapid Wideband RF Spectrum Sensing in Emerging Cognitive Radio Systems
Wireless systems have become an essential part of every sector of the national and global economy. In addition to existing commercial systems including GPS, mobile cellular, and WiFi communications, emerging systems like video over wireless, the Internet of Things, and machine-to-machine communications are expected to increase mobile wireless data traffic by several orders of magnitude over the coming decades, while natural resources like energy and radio spectrum remain scarce. The projected growth of the number of connected nodes into the trillions in the near term and increasing user demand for instantaneous, over-the-air access to large volumes of content will require a 1000-fold increase in network wireless data capacity by 2020. Spectrum is the lifeblood of these future wireless networks and the âdata stormâ driven by emerging technologies will lead to a pressing âartificialâ spectrum scarcity.
Cognitive radio is a paradigm proposed to overcome the existing challenge of underutilized spectrum. Emerging cognitive radio systems employing multi-tiered, shared-spectrum access are expected to deliver superior spectrum efficiency over existing scheduled-access systems; they have several device categories (3 or more tiers) with different access privileges. We focus on lower tiered âsmartâ devices that evaluate the spectrum dynamically and opportunistically use the underutilized spectrum. These âsmartâ devices require spectrum sensing for incumbent detection and interferer avoidance. Incumbent detection will rely on database lookup or narrowband high-sensitivity sensing. Integrated interferer detectors, on the other hand, need to be fast, wideband, and energy efficient, while requiring only moderate sensitivity.
These future 'smart' devices operating in small cell environments will need to rapidly (in 10s of ÎŒs) detect a few (e.g. 3 to 6) strong interferers within roughly a 1GHz span and accordingly reconfigure their hardware resources or request adjustments to their wireless connection consisting of primary and secondary links in licensed and unlicensed spectrum.
Compressive sampling (CS), an evolutionary sensing/sampling paradigm that changes the perception of sampling, has been extensively used for image reconstruction. It has been shown that a single pixel camera that exploits CS has the ability to obtain an image with a single detection element, while measuring the image fewer times than the number of pixels with the prior assumption of sparsity. We exploited CS in the presented works to take a âsnapshotâ of the spectrum with low energy consumption and high frequency resolutions.
Compressive sampling is applied to break the fixed trade-off between scan time, resolution bandwidth, hardware complexity, and energy consumption. This contrasts with traditional spectrum scanning solutions, which have constant energy consumption in all architectures to first order and a fixed trade-off between scan time and resolution bandwidth. Compressive sampling enables energy-efficient, rapid, and wideband spectrum sensing with high frequency resolutions at the expense of degraded instantaneous dynamic range due to the noise folding.
We have developed a quadrature analog-to-information converter (QAIC), a novel CS rapid spectrum sensing technique for band-pass signals. Our first wideband, energy-efficient, and rapid interferer detector end-to-end system with a QAIC senses a wideband 1GHz span with a 20MHz resolution bandwidth and successfully detects up to 3 interferers in 4.4ÎŒs. The QAIC offers 50x faster scan time compared to traditional sweeping spectrum scanners and 6.3x the compressed aggregate sampling rate of traditional concurrent Nyquist-rate approaches. The QAIC is estimated to be two orders of magnitude more energy efficient than traditional spectrum scanners/sensors and one order of magnitude more energy efficient than existing low-pass CS spectrum sensors.
We implemented a CS time-segmented quadrature analog-to-information converter (TS-QAIC) that extends the physical hardware through time segmentation (e.g. 8 physical I/Q branches to 16 I/Q through time segmentation) and employs adaptive thresholding to react to the signal conditions without additional silicon cost and complexity. The TS-QAIC rapidly detects up to 6 interferers in the PCAST spectrum between 2.7 and 3.7GHz with a 10.4ÎŒs sensing time for a 20MHz RBW with only 8 physical I/Q branches while consuming 81.2mW from a 1.2V supply.
The presented rapid sensing approaches enable system scaling in multiple dimensions such as ADC bits, the number of samples, and the number of branches to meet user performance goals (e.g. the number of detectable interferers, energy consumption, sensitivity and scan time).
We envision that compressive sampling opens promising avenues towards energy-efficient and rapid sensing architectures for future cognitive radio systems utilizing multi-tiered, shared spectrum access
AnalogâtoâDigital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing
This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process
PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach
PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with
cognition which can be developed by implementing Artificial Intelligence (AI) techniques.
Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum
abnormalities, can be effectively implemented as shown by the proposed research. One important
application is PHY-layer security since it is essential to establish secure wireless communications
against external jamming attacks.
In this framework, signals are non-stationary and features from such kind of dynamic
spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell
Transform (ST) with dual-resolution which has been proposed and validated in this work as
part of spectrum sensing techniques.
Afterwards, analysis of the state-of-the-art about learning dynamic models from observed
features describes theoretical aspects of Machine Learning (ML). In particular, following the
recent advances of ML, learning deep generative models with several layers of non-linear
processing has been selected as AI method for the proposed spectrum abnormality detection
in CR for a brain-inspired, data-driven SA.
In the proposed approach, the features extracted from the ST representation of the wideband
spectrum are organized in a high-dimensional generalized state vector and, then, a generative
model is learned and employed to detect any deviation from normal situations in the analysed
spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN),
auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative
models.
A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using
the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency)
with 800 MHz frequency range. Training of the deep generative model is performed
on the generalized state vector representing the mmWave spectrum with normality pattern
without any malicious activity. Testing is based on new and independent data samples corresponding
to abnormality pattern where the moving signal follows a different behaviour which
has not been observed during training.
An abnormality indicator is measured and used for the binary classification (normality hypothesis
otherwise abnormality hypothesis), while the performance of the generative models
is evaluated and compared through ROC curves and accuracy metrics
From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
Strategies to acquire white space information is the single most significant
functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution
to enhance information accuracy. The evolution trends are spectrum sensing, prediction
algorithm and recently, geo-location database technique. Previously, spectrum sensing was
the main technique for detecting the presence/absence of a primary user (PU) signal in a
given radio frequency (RF) spectrum. However, this expectation could not materialized as
a result of numerous technical challenges ranging from hardware imperfections to RF
signal impairments. To convey the evolutionary trends in the development of white space
information, we present a survey of the contemporary advancements in PU detection with
emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks.
It is found that geo-location database is the most reliable technique to acquire
TVWS information although, it is financially driven. Finally, using financially driven
database model, this study compared the data-rate and spectral efficiency of FCC and
Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms
FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the
adoption of an all-inclusive TVWS information acquisition model as the future research
direction for TVWS information acquisition techniques
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