456 research outputs found
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or
received signal power across a geographic area of interest using spatially
distributed sensor measurements. Applications of these maps include network
planning, interference coordination, power control, localization, and cognitive
radios to name a few. Since existing spectrum cartography techniques require
accurate estimates of the sensor locations, their performance is drastically
impaired by multipath affecting the positioning pilot signals, as occurs in
indoor or dense urban scenarios. To overcome such a limitation, this paper
introduces a novel paradigm for spectrum cartography, where estimation of
spectral maps relies on features of these positioning signals rather than on
location estimates. Specific learning algorithms are built upon this approach
and offer a markedly improved estimation performance than existing approaches
relying on localization, as demonstrated by simulation studies in indoor
scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on
Signal Processin
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Spectrum cartography (SC), also known as radio map estimation (RME), aims at
crafting multi-domain (e.g., frequency and space) radio power propagation maps
from limited sensor measurements. While early methods often lacked theoretical
support, recent works have demonstrated that radio maps can be provably
recovered using low-dimensional models -- such as the block-term tensor
decomposition (BTD) model and certain deep generative models (DGMs) -- of the
high-dimensional multi-domain radio signals. However, these existing provable
SC approaches assume that sensors send real-valued (full-resolution)
measurements to the fusion center, which is unrealistic. This work puts forth a
quantized SC framework that generalizes the BTD and DGM-based SC to scenarios
where heavily quantized sensor measurements are used. A maximum likelihood
estimation (MLE)-based SC framework under a Gaussian quantizer is proposed.
Recoverability of the radio map using the MLE criterion are characterized under
realistic conditions, e.g., imperfect radio map modeling and noisy
measurements. Simulations and real-data experiments are used to showcase the
effectiveness of the proposed approach.Comment: 16 pages, 9 figure
Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography
Radio maps characterize quantities of interest in radio communication
environments, such as the received signal strength and channel attenuation, at
every point of a geographical region. Radio map estimation typically entails
interpolative inference based on spatially distributed measurements. In this
tutorial article, after presenting some representative applications of radio
maps, the most prominent radio map estimation methods are discussed. Starting
from simple regression, the exposition gradually delves into more sophisticated
algorithms, eventually touching upon state-of-the-art techniques. To gain
insight into this versatile toolkit, illustrative toy examples will also be
presented
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
Spectrum cartography using adaptive radial basis functions: Experimental validation
In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the received signal power at each location is estimated as a linear combination of different RBFs. The weights of the RBFs, their Gaussian decaying parameters and locations are jointly optimized using expectation maximization with a least squares loss function and a quadratic regularizer. The performance of adaptive RBFs based spectrum cartography is shown through measurements using a universal software radio peripheral, a customized node and LabView framework. The obtained results verify the ability of adaptive RBF to construct spectrum maps with an acceptable performance measured by normalized mean square error (NMSE).acceptedVersionnivå
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