Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach

This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between the devices and the satellites is low, and thus alternative localization methods are required for good accuracy. We present a deep learning method for localization, based merely on pathloss, which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on time of arrival or angle of arrival information. In a wireless network, user devices scan the base station beacon slots and identify the few strongest base station signals for handover and user-base station association purposes. In the proposed method, the user to be localized simply reports such received signal strengths to a central processing unit, which may be located in the cloud. For each base station we have good approximation of the pathloss at every location in a dense grid in the map. This approximation is provided by RadioUNet, a deep learning-based simulator of pathloss functions in urban environment, that we have previously proposed and published. Using the estimated pathloss radio maps of all base stations and the corresponding reported signal strengths, the proposed deep learning algorithm can extract a very accurate localization of the user. The proposed method, called LocUNet, enjoys high robustness to inaccuracies in the estimated radio maps. We demonstrate this by numerical experiments, which obtain state-of-the-art results.

Spatially Scalable Lossy Coded Caching

We apply the coded caching scheme proposed by Maddah-Ali and Niesen to a multipoint multicasting video paradigm. Partially caching the video files on the wireless devices provides an opportunity to decrease data traffic load in peak hours via sending multicast coded messages to users. In this paper, we propose a two-hop wireless network for video multicasting, where the common coded multicast message is transmitted through different single antenna Edge Nodes (ENs) to multiple antenna users. Each user can decide to decode any EN by using a zero forcing receiver. Motivated by Scalable Video Coding (SVC), we consider successive refinement source coding in order to provide a ``softer tradeoff between the number of decoded ENs and the source distortion at each user receiver. The resulting coding scheme can be seen as the concatenation of Maddah-Ali and Niesen coded caching for each source-coded layer, and multiple description coding. Using stochastic geometry, we investigate the tradeoff between delivery time and per-user average source distortion. The proposed system is spatially scalable in the sense that, for given users and ENs spatial density, the achieved distortion-delivery time performance is independent of the coverage area (for in the limit of large area).

On D2D Caching with Uncoded Cache Placement

We consider a cache-aided wireless device-to-device (D2D) network under the constraint of one-shot delivery, where the placement phase is orchestrated by a central server. We assume that the devices caches are filled with uncoded data, and the whole file database at the server is made available in the collection of caches. Following this phase, the files requested by the users are serviced by inter-device multicast communication. For such a system setting, we provide the exact characterization of load-memory trade-off, by deriving both the minimum average and the minimum peak sum-loads of links between devices, for a given individual memory size at disposal of each user.