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H2020

MAPS Report Summary

Project ID: 659067
Funded under: H2020-EU.1.3.2.

Periodic Reporting for period 1 - MAPS (Millimeter Wave Massive Arrays enabling RFID/Radar Applications on 5G Smartphones)

Reporting period: 2015-07-20 to 2017-01-19

Summary of the context and overall objectives of the project

A rising interest is moving towards the next 5G wireless mobile communication, as numerous devices and different heterogeneous networks will be interconnected, with an increased data traffic demand at an unprecedented scale. In this context, MAPS aims to exploit the joint use of mm-wave and massive array technologies for packing a large number of antenna elements into a small area, i.e., a smartphone, thus enabling the opportunity for a user to automatically reconstruct the surrounding environment as well as to identify and localize surrounding tagged objects (see Fig-Scenario).
MAPS objectives can be summarized as follows:
• Investigation of the real capabilities of mm-wave massive arrays;
• Models assessment for propagation and backscattering from objects in indoor environments at mm-wave;
• Development of personal radar using mm-wave massive arrays, able to detect, self-localize and localize objects in the environment;
• Development of mm-wave passive radio-frequency identification (RFID) using massive arrays;
• Development of environment-learning radar algorithms exploiting massive arrays capabilities;
• Merging of mm-wave, massive arrays, personal radar and RFID technologies in a unique network.
Within ten years, thanks to the available technologies, the personal radar could be embedded in portable devices enabling automatic indoor environment mapping with a potential impact similar to that of outdoor mapping technologies.

● Conclusions of the Action
A new mm-wave backscattering channel model was proposed through an ad-hoc measurement campaign, which was successively exploited to validate the proposed localization and mapping algorithms. Then, fundamental localization limits were derived by accounting for the presence of a single-anchor equipped with a massive array performing beamsteeering. Furthermore, the possibility to exploit mm-wave RFID capabilities was investigated by considering also real measured data. Finally, the main achievements from the all work packages (WPs) have been put together into a unique network enabling object and tags detection and localization.
Main MAPS results were published in highly recognized journals and conferences.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

According to the different WPs, the following work was performed in MAPS.
• WP1: The state of the art concerning arrays architectures was revised. In order to choose a possible candidate to accomplish the goal, we analyzed the impact of massive arrays side-lobes into detection performance for mapping and personal radars applications. In fact, the side-lobes of realistic radiation patterns might cause false target detection, or errors in the ranging procedure. Such output of WP1 was accounted for as an input for WP2.
• WP2: MM-wave backscattering channel measurement campaigns using massive arrays were performed and main multipaths components were retrieved and grouped in clusters through ad hoc algorithms. Then, we proposed a new channel model which put in relation the found parameters with the environment geometry.
Then, to counteract the side-lobe effect, the joint conception of a massive array mask and of a thresholding strategy robust to the presence of interferers in the side-lobes direction was investigated, accounting for the considerations in WP1. An example of map reconstruction using the proposed approach is reported in Fig-Loc. We also proposed an ad-hoc mapping algorithm accounting for massive arrays and capable to exploit all the raw measured data, and we investigated the impact of different design parameters into the environment reconstruction performance. An example of reconstructed scenario is shown in Fig-Map. Then, the theoretical position error bound (PEB) was derived for different beamforming strategies. We finally proposed a novel radar approach where the threshold is adaptive with the signals measured from the environment.
• WP3: We investigated the possibility to exploit mm-wave passive RFID by comparing its performance with that of UWB RFID and, successively, by showing the possibility to localize tags with a single reader operating beamsteering. To this purpose, we performed measurements by emulating the presence of a user who moves in an unknown environment and operates beamsteering to detect and localize the surrounding tags.
• WP4: Results and technologies previously found were then merged into a unique network in order to assess the possibility to have a user moving in an unknown scenario, capable to reconstruct the environment and localize tagged objects within it. A simulator performing off-line post-processing showed the feasibility of the proposed architecture.

● Dissemination
In the project proposal, a target of at least 3 journal papers submitted was indicated. At the end of the project, the target was achieved as 4 journal papers were published, 2 were under review and other 2 in preparation for submission. 8 conference papers with MAPS acknowledgment were accepted.
A contribution concerning the MAPS activity was also presented at the 1st technical IRACON COST meeting (http://www.iracon.org/meeting/iracon-2nd-mc-meeting-and-1st-technical-meeting/).

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Below, we reported the main progresses beyond the state of the art according to the MAPS objectives.
• By proposing a new mm-wave backscattering channel model related to the environment perimeter, we gave a new contribution with respect to current literature available in such a topic.
• Differently from the state of the art, we considered a low-complexity non-coherent detection scheme, where the massive arrays characteristics, e.g. side-lobes, were taken into account for the threshold evaluation.
• We showed a new PEB-based investigation on how a proper choice of the beamforming strategy can lead to different levels of localization accuracy and of array complexity. In fact, beamforming strategies were usually neglected when the PEB was exploited to investigate the ultimate localization performance.
• Environment Mapping: we proposed an ad-hoc environment mapping algorithm which exploits all the raw measured data from the radar, which was different from classical camera-based approaches or algorithms which discard part of the collected measured information.
• Passive mm-wave RFID: we considered an ad-hoc architecture which allowed to achieve accurate tags localization with a single reader, whereas usually at least three readers were required to perform such operation with current available RFID systems.

● Impact on the society
The technologies and applications investigated in MAPS could be exploited in our society, especially for what next 5G is concerned.
In fact, MAPS could be a preliminary step for users to automatically create indoor maps which can be shared in a common space. i.e., a cloud. Such procedure could help supporting a safer navigation in indoor environments for visually impaired people, as well as rescue situations in critical visual conditions. These scenarios could be facilitated by the spread of mm-wave and massive arrays technologies in next smartphones generation, thus without requiring dedicated hardware to be installed onto portable devices.
Applications could be summarized (but not limited to) to the following ones:
• Healthcare: assisted guidance for visually impaired persons;
• Industry: automatic navigation of robots and drones in indoor environments by merging the personal radar information to that of the camera;
• Safety: assistance in navigation in case of need.

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