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1. A Spatiotemporal Approach for Secure Crowdsourced Radio Environment Map Construction

   https://doi.org/10.1109/TNET.2020.2992939  

   Hu, Yidan ; Zhang, Rui ( May 2020 , IEEE/ACM Transactions on Networking)

   Database-driven Dynamic Spectrum Sharing (DSS) is the de-facto technical paradigm adopted by Federal Communications Commission for increasing spectrum efficiency, which allows licensed spectrum to be opportunistically used by secondary users. In database-driven DSS, a geo-location database administrator (DBA) maintains spectrum availability information over its service region in the form of a Radio Environment Map (REM), where the received signal strength from the primary user at every location is either directly measured via spectrum sensing or estimated via statistical spatial interpolation. Crowdsourcing-based spectrum sensing is a promising approach for periodically collecting spectrum measurements over a large geographic area but is unfortunately vulnerable to false spectrum measurements. Despite a large body of prior work on secure cooperative spectrum sensing, how to construct an accurate REM in the presence of false measurements remains an open challenge. In this paper, we introduce ST-REM, a novel spatiotemporal approach for securely constructing an REM in the presence of false spectrum measurements. Inspired by the self-label techniques developed for semi-supervised learning, ST-REM iteratively constructs an REM from a small number of spectrum measurements from trusted anchor sensors and many more measurements from mobile users. During each iteration, the DBA evaluates the trustworthiness of each measurement by jointly considering its spatial fitness with other trusted measurements and the mobile user's long-term behavior. By gradually incorporating the most trustworthy spectrum measurements, the DBA is able to construct a REM with high accuracy. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of ST-REM. 

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2. Differentially-Private Incentive Mechanism for Crowdsourced Radio Environment Map Construction

   https://doi.org/10.1109/INFOCOM.2019.8737512  

   Hu, Yidan ; Zhang, Rui ( April 2019 , IEEE INFOCOM 2019 - IEEE Conference on Computer Communications)

   Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism. 

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3. Differentially-Private Incentive Mechanism for Crowdsourced Radio Environment Map Construction

   Hu, Yidan ; Zhang, Rui ( April 2019 , IEEE International Conference on Computer Communications)

   Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism. 

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4. Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks

   https://doi.org/10.3390/jsan9020023  

   Biswas, Rajorshi ; Wu, Jie ( June 2020 , Journal of Sensor and Actuator Networks)

   Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model. 

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5. HyperGal: Hyperspectral scene modeling for supernova typing with the SED Machine integral field spectrograph

   https://doi.org/10.1051/0004-6361/202244740  

   Lezmy, J. ; Copin, Y. ; Rigault, M. ; Smith, M. ; Neill, J. D. ( December 2022 , Astronomy & Astrophysics)

   Context. Recent developments in time domain astronomy, such as Zwicky Transient Facility (ZTF), have made it possible to conduct daily scans of the entire visible sky, leading to the discovery of hundreds of new transients every night. Among these detections, 10 to 15 of these objects are supernovae (SNe), which have to be classified prior to cosmological use. The spectral energy distribution machine (SEDM) is a low-resolution ( ℛ ~ 100) integral field spectrograph designed, built, and operated with the aim of spectroscopically observing and classifying targets detected by the ZTF main camera. Aims. As the current pysedm pipeline can only handle isolated point sources, it is limited by contamination when the transient is too close to its host galaxy core. This can lead to an incorrect typing and ultimately bias the cosmological analyses, affecting the homogeneity of the SN sample in terms of local environment properties. We present a new scene modeler to extract the transient spectrum from its structured background, with the aim of improving the typing efficiency of the SEDM. Methods. H yper G al is a fully chromatic scene modeler that uses archival pre-transient photometric images of the SN environment to generate a hyperspectral model of the host galaxy. It is based on the cigale SED fitter used as a physically-motivated spectral interpolator. The galaxy model, complemented by a point source for the transient and a diffuse background component, is projected onto the SEDM spectro-spatial observation space and adjusted to observations, and the SN spectrum is ultimately extracted from this multi-component model. The full procedure, from scene modeling to transient spectrum extraction and typing, is validated on 5000 simulated cubes built from actual SEDM observations of isolated host galaxies, covering a broad range of observing conditions and scene parameters. Results. We introduce the contrast, c , as the transient-to-total flux ratio at the SN location, integrated over the ZTF r -band. From estimated contrast distribution of real SEDm observations, we show that H yper G al correctly classifies ~95% of SNe Ia, and up to 99% for contrast c ≳ 0.2, representing more than 90% of the observations. Compared to the standard point-source extraction method (without the hyperspectral galaxy modeling step), H yper G al correctly classifies 20% more SNe Ia between 0.1 < c < 0.6 (50% of the observation conditions), with less than 5% of SN Ia misidentifications. The false-positive rate is less than 2% for c > 0.1 (> 99% of the observations), which represents half as much as the standard extraction method. Assuming a similar contrast distribution for core-collapse SNe, H yper G al classifies 14% additional SNe II and 11% additional SNe Ibc. Conclusions. H yper G al has proven to be extremely effective in extracting and classifying SNe in the presence of strong contamination by the host galaxy, providing a significant improvement with respect to the single point-source extraction. 

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