2,246 research outputs found
Spectrum cartography techniques, challenges, opportunities, and applications: A survey
The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Sixth-generation (6G) mobile communication networks are expected to have
dense infrastructures, large-dimensional channels, cost-effective hardware,
diversified positioning methods, and enhanced intelligence. Such trends bring
both new challenges and opportunities for the practical design of 6G. On one
hand, acquiring channel state information (CSI) in real time for all wireless
links becomes quite challenging in 6G. On the other hand, there would be
numerous data sources in 6G containing high-quality location-tagged channel
data, making it possible to better learn the local wireless environment. By
exploiting such new opportunities and for tackling the CSI acquisition
challenge, there is a promising paradigm shift from the conventional
environment-unaware communications to the new environment-aware communications
based on the novel approach of channel knowledge map (CKM). This article aims
to provide a comprehensive tutorial overview on environment-aware
communications enabled by CKM to fully harness its benefits for 6G. First, the
basic concept of CKM is presented, and a comparison of CKM with various
existing channel inference techniques is discussed. Next, the main techniques
for CKM construction are discussed, including both the model-free and
model-assisted approaches. Furthermore, a general framework is presented for
the utilization of CKM to achieve environment-aware communications, followed by
some typical CKM-aided communication scenarios. Finally, important open
problems in CKM research are highlighted and potential solutions are discussed
to inspire future work
SPECTRUM SENSING AND COOPERATION IN COGNITIVE-OFDM BASED WIRELESS COMMUNICATIONS NETWORKS
The world has witnessed the development of many wireless systems and
applications. In addition to the large number of existing devices, such development of
new and advanced wireless systems increases rapidly the demand for more radio
spectrum. The radio spectrum is a limited natural resource; however, it has been
observed that it is not efficiently utilized. Consequently, different dynamic spectrum
access techniques have been proposed as solutions for such an inefficient use of the
spectrum. Cognitive Radio (CR) is a promising intelligent technology that can identify
the unoccupied portions of spectrum and opportunistically uses those portions with
satisfyingly high capacity and low interference to the primary users (i.e., licensed users).
The CR can be distinguished from the classical radio systems mainly by its awareness
about its surrounding radio frequency environment. The spectrum sensing task is the
main key for such awareness. Due to many advantages, Orthogonal Frequency Division
Multiplexing system (OFDM) has been proposed as a potential candidate for the CR‟s
physical layer. Additionally, the Fast Fourier Transform (FFT) in an OFDM receiver
supports the performance of a wide band spectrum analysis. Multitaper spectrum
estimation method (MTM) is a non-coherent promising spectrum sensing technique. It
tolerates problems related to bad biasing and large variance of power estimates.
This thesis focuses, generally, on the local, multi antenna based, and global
cooperative spectrum sensing techniques at physical layer in OFDM-based CR systems.
It starts with an investigation on the performance of using MTM and MTM with
singular value decomposition in CR networks using simulation. The Optimal MTM
parameters are then found. The optimal MTM based detector theoretical formulae are
derived. Different optimal and suboptimal multi antenna based spectrum sensing
techniques are proposed to improve the local spectrum sensing performance. Finally, a
new concept of cooperative spectrum sensing is introduced, and new strategies are
proposed to optimize the hard cooperative spectrum sensing in CR networks.
The MTM performance is controlled by the half time bandwidth product and
number of tapers. In this thesis, such parameters have been optimized using Monte
Carlo simulation. The binary hypothesis test, here, is developed to ensure that the effect
of choosing optimum MTM parameters is based upon performance evaluation. The
results show how these optimal parameters give the highest performance with minimum
complexity when MTM is used locally at CR.
The optimal MTM based detector has been derived using Neyman-Pearson
criterion. That includes probabilities of detection, false alarm and misses detection
approximate derivations in different wireless environments. The threshold and number
of sensed samples controlling is based on this theoretical work.
In order to improve the local spectrum sensing performance at each CR, in the CR
network, multi antenna spectrum sensing techniques are proposed using MTM and
MTM with singular value decomposition in this thesis. The statistical theoretical
formulae of the proposed techniques are derived including the different probabilities.
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The proposed techniques include optimal, that requires prior information about the
primary user signal, and two suboptimal multi antenna spectrum sensing techniques
having similar performances with different computation complexity; these do not need
prior information about the primary user signalling. The work here includes derivations
for the periodogram multi antenna case.
Finally, in hard cooperative spectrum sensing, the cooperation optimization is
necessary to improve the overall performance, and/or minimize the number of data to be
sent to the main CR-base station. In this thesis, a new optimization method based on
optimizing the number of locally sensed samples at each CR is proposed with two
different strategies. Furthermore, the different factors that affect the hard cooperative
spectrum sensing optimization are investigated and analysed and a new cooperation
scheme in spectrum sensing, the master node, is proposed.Ministry of Interior-Kingdom of Saudi Arabi
Algorithms for 5G physical layer
There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical
layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses
he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use
cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G.
All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and
he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming
istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User
activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to
spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA)
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Compressive techniques for sub-Nyquist data acquisition & processing in vibration-based structural health monitoring of engineering structures
Vibration-based structural health monitoring (VSHM) is an automated method for assessing the integrity and performance of dynamically excited structures through processing of structural vibration response signals acquired by arrays of sensors. From a technological viewpoint, wireless sensor networks (WSNs) offer less obtrusive, more economical, and rapid VSHM deployments in civil structures compared to their tethered counterparts, especially in monitoring large-scale and geometrically complex structures. However, WSNs are constrained by certain practical issues related to local power supply at sensors and restrictions to the amount of wirelessly transmitted data due to increased power consumptions and bandwidth limitations in wireless communications.
The primary objective of this thesis is to resolve the above issues by considering sub-Nyquist data acquisition and processing techniques that involve simultaneous signal acquisition and compression before transmission. This drastically reduces the sampling and transmission requirements leading to reduced power consumptions up to 85-90% compared to conventional approaches at Nyquist rate. Within this context, the current state-of-the-art VSHM approaches exploits the theory of compressive sensing (CS) to acquire structural responses at non-uniform random sub-Nyquist sampling schemes. By exploiting the sparse structure of the analysed signals in a known vector basis (i.e., non-zero signal coefficients), the original time-domain signals are reconstructed at the uniform Nyquist grid by solving an underdetermined optimisation problem subject to signal sparsity constraints. However, the CS sparse recovery is a computationally intensive problem that strongly depends on and is limited by the sparsity attributes of the measured signals on a pre-defined expansion basis. This sparsity information, though, is unknown in real-time VSHM deployments while it is adversely affected by noisy environments encountered in practice.
To efficiently address the above limitations encountered in CS-based VSHM methods, this research study proposes three alternative approaches for energy-efficient VSHM using compressed structural response signals under ambient vibrations. The first approach aims to enhance the sparsity information of vibrating structural responses by considering their representation on the wavelet transform domain using various oscillatory functions with different frequency domain attributes. In this respect, a novel data-driven damage detection algorithm is developed herein, emerged as a fusion of the CS framework with the Relative Wavelet Entropy (RWE) damage index. By processing sparse signal coefficients on the harmonic wavelet transform for two comparative structural states (i.e., damage versus healthy state), CS-based RWE damage indices are retrieved from a significantly reduced number of wavelet coefficients without reconstructing structural responses in time-domain.
The second approach involves a novel signal-agnostic sub-Nyquist spectral estimation method free from sparsity constraints, which is proposed herein as a viable alternative for power-efficient WSNs in VSHM applications. The developed method relies on Power Spectrum Blind Sampling (PSBS) techniques together with a deterministic multi-coset sampling pattern, capable to acquire stationary structural responses at sub-Nyquist rates without imposing sparsity conditions. Based on a network of wireless sensors operating on the same sampling pattern, auto/cross power-spectral density estimates are computed directly from compressed data by solving an overdetermined optimisation problem; thus, by-passing the computationally intensive signal reconstruction operations in time-domain. This innovative approach can be fused with standard operational modal analysis algorithms to estimate the inherent resonant frequencies and modal deflected shapes of structures under low-amplitude ambient vibrations with the minimum power, computational and memory requirements at the sensor, while outperforming pertinent CS-based approaches. Based on the extracted modal in formation, numerous data-driven damage detection strategies can be further employed to evaluate the condition of the monitored structures.
The third approach of this thesis proposes a noise-immune damage detection method capable to capture small shifts in structural natural frequencies before and after a seismic event of low intensity using compressed acceleration data contaminated with broadband noise. This novel approach relies on a recently established sub-Nyquist pseudo-spectral estimation method which combines the deterministic co-prime sub-Nyquist sampling technique with the multiple signal classification (MUSIC) pseudo-spectrum estimator. This is also a signal-agnostic and signal reconstruction-free method that treats structural response signals as wide-sense stationary stochastic processes to retrieve, with very high resolution, auto-power spectral densities and structural natural frequency estimates directly from compressed data while filtering out additive broadband noise
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A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications
A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closely-spaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as field-recorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available
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