29 research outputs found
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
Estimation of Spatial Fields of Nlos/Los Conditions for Improved Localization in Indoor Environments
A major challenge in indoor localization is the presence or absence of line-of-sight (LOS). The absence of LOS, denoted as non-line-of-sight (NLOS), directly affects the accuracy of any localization algorithm because of the induced bias in ranging. The estimation of the spatial distribution of NLOS-induced ranging bias in indoor environments remains a major challenge. In this paper, we propose a novel crowd-based Bayesian learning approach to the estimation of bias fields caused by LOS/NLOS conditions. The proposed method is based on the concept of Gaussian processes and exploits numerous measurements. The performance of the method is demonstrated with extensive experiments
LAPRA: Location-aware Proactive Resource Allocation
Today’s indoor wireless networks employ reactive
resource allocation methods to provide fair and efficient usage of the communication system. However, their reactive nature limits the quality of service (QoS) that can be offered to the user locations within the environment. In large crowded areas (airports, conferences), networks can get congested and users may suffer from poor QoS. To mitigate this, we propose and evaluate a location-aware user-centric proactive resource allocation approach (LAPRA), in which the users are proactive and seek good channel quality by moving to locations where the signal quality is good. As a result, the users and their locations are optimized to improve the overall QoS. We demonstrate that the proposed proactive approach enhances the user QoS and improves network throughput of the system
Channel Gain Cartography via Mixture of Experts
Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio
Channel Gain Cartography via Mixture of Experts
In order to estimate the channel gain (CG) between the locations of an
arbitrary transceiver pair across a geographic area of interest, CG maps can be
constructed from spatially distributed sensor measurements. Most approaches to
build such spectrum maps are location-based, meaning that the input variable to
the estimating function is a pair of spatial locations. The performance of such
maps depends critically on the ability of the sensors to determine their
positions, which may be drastically impaired if the positioning pilot signals
are affected by multi-path channels. An alternative location-free approach was
recently proposed for spectrum power maps, where the input variable to the maps
consists of features extracted from the positioning signals, instead of
location estimates. The location-based and the location-free approaches have
complementary merits. In this work, apart from adapting the location-free
features for the CG maps, a method that can combine both approaches is proposed
in a mixture-of-experts framework.Comment: 5 pages, 2 figures, accepted in Globecom 202
Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels
In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio