No abstract

No abstract

No abstract

Indoor localization techniques based on supervised learning deliver great performance accuracy while maintaining low online complexity. However, such systems require massive amounts of data for offline training, which necessitates costly measurements. The essence of this paper is twofold with the purpose of providing solutions to missing data of different nature: available unlabeled data and missing unlabeled data. In both cases, we rely on a few labeled available data, which is costly yet insufficient to achieve a high localization accuracy. To address the problem of available unlabeled data, a weighted semisupervised DNN-based indoor localization approach leveraging pseudo-labeling methods in combination with real labeled samples and inexpensive pseudo-labeled samples is proposed in order to boost localization accuracy, while overcoming the high cost of collecting additional labeled data. As for the extreme case of unavailable unlabeled data, we propose an alternative localization system generating fake fingerprints based on generative adversarial networks (GANs) named 'Weighted GAN based indoor localization'. Furthermore, a deep neural network is trained on a mixed dataset containing both real collected and fake produced data samples using a similar weighting technique in order to improve location prediction performance and avoids overfitting. In terms of localization accuracy, our proposed localization approaches outperform conventional supervised localization schemes utilizing the same collection of real labeled samples. For experimental evaluations, we have tested our proposed methods on both simulated data and experimental data from the publicly available UJIndoorLoc database, which is built to test indoor positioning systems relying on Wi-Fi fingerprints. Results based on experimental data provide the localization accuracy increase compared to the classical supervised learning method using the same set of labeled collected data when using the weighted semi-supervised and the weighted-GAN approaches by 10.11 % and 8.53 %, respectively.

In this letter, we propose novel dual-mode chirp spread spectrum (DM-CSS) modulation for lowpower wide-area networks for Internet-of-Things applications. DM-CSS is capable of transmitting higher number of bits relative to other counterparts, such as Long Range (LoRa) modulation. Considering the same symbol period, the number of transmitted bits in DM-CSS are augmented by: (i) simultaneously multiplexing even and odd chirp signals; (ii) using phase shifts of 0 and π radians for both even and odd chirp signals; and (iii) using either up-chirp or down-chirp signal. Relative to LoRa, the transmitted bit rate increases by up to 116.66% for the same bandwidth and spreading factor. Moreover, we present a non-coherent receiver architecture for DM-CSS. Simulation results reveal that DM-CSS is not only more spectral efficient but also more energy efficient than most classical counterparts. It is also demonstrated that DM-CSS is robust to phase and frequency offsets.

No abstract

No abstract

In applications such as seismic acquisition, the position information of sensor nodes, that are deployed in a linear topology, is desired with sub-meter accuracy in the presence of a limited number of anchor nodes. This can be achieved with antenna arrays via mm-wave cooperative localization, whose performance limits are derived in this letter. The number of anchor nodes is seen to have a stronger impact than the number of antenna elements in the anchor nodes. Succinct closed-form expressions for the position error bound are also obtained for 1-hop and 2-hop cooperative localization, where sub-meter accuracy is perceived over several hundred nodes.