# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
###### Abstract  
This work presents the initial experimentation of a real-time 5G mmWave downlink positioning testbed deployed at Airbus premises. This experimentation is part of a first-of-a-kind testbed for hybrid Global Navigation Satellite Systems (GNSS), fifth-generation (5G) new radio (NR) and sensor positioning, called the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed. The mmWave 5G base station (BS) exploits the 5G standard positioning reference signal (PRS) to support positioning capabilities within the 5G NR downlink transmissions. Outdoor field results are used to characterize the received power levels and beam-based angle-of-arrival (AoA) estimation accuracy of this 5G mmWave PRS platform. The goal is to assess the suitability of this platform to enhance the positioning performance thanks to the 5G downlink mmWave transmissions. To the best of the authors' knowledge, this paper presents the first AoA results using OpenAirInterface (OAI) PRS mmWave signal transmissions at 27 GHz for positioning. These initial field results indicate a maximum coverage of 30 m and an AoA accuracy limited by the reduced array size. The limitations and potential enhancements of this platform are provided as future recommendations. 5G positioning; mmWave; FR2; real-time testbed +
Footnote †: Academic Editor: [PERSON]  
standardization since Release 16 in [7], through simulations for frequency range 1 (FR1) below 6 GHz and for FR2 above 24 GHz. However, the real-world demonstration of 5G precise positioning has been very limited, due to major challenges when developing a real-time 5G network able to exploit the high bandwidths required for precise positioning. For instance, an FR1 field trial of 5G positioning based on single- and multi-cell round-trip time (RTT) and uplink angle-of-arrival (AoA) was demonstrated in [8] to achieve meter-level accuracies in FR1 LoS conditions. Another relevant example is the Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed [9; 10; 11; 12], where the world's first airborne 5G network for positioning purposes was shown to enable submeter localization accuracy with 80-MHz downlink PRS in FR1 LoS. In terms of prototype 5G FR2 network deployments, 5G mmWave precise positioning was demonstrated to achieve meter-level accuracies with downlink angle-of-departure (AoD) positioning in [13], or submeter-level accuracies with DL-TDoA in [14], depending on the network synchronization. Nonetheless, there is very limited literature on 5G mmWave network deployments for precise positioning in outdoor environments. There are certainly extensive efforts in the literature to develop high-end mmWave testbeds able to exploit the most advanced 5G FR2 and future 6G communication features. State-of-the-art examples of mmWave front ends and testbeds at 28 GHz and 60 GHz are summarized in [15], including an overview of the example COSMOS testbed able to operate in both mmWave bands. Recently, an end-to-end connection between the OpenAirInterface (OAI) FR2 network and a commercial UE has been demonstrated in [16]. High-end mmWave front ends with a 64-element antenna array for a single 5G transmitter and a 256-element array for a single 5G receiver are deployed in [17], over a controlled indoor LoS environment to achieve centimeter-level ToA accuracy. Field results with a 5G mmWave base station and a prototype UE with 16-element array are shown in [18], to assess experimental RTT, downlink AoD and uplink AoD positioning for vehicular applications. This work is a continuation of the FR1 positioning contributions within the HOP-5G testbed in [9; 10; 11; 12]. In contrast to prototype commercial network demonstrators, the HOP-5G testbed is an experimental and flexible proof-of-concept (PoC) of advanced 5G and hybrid positioning features, based on OAI open-source software, commercial off-the-shelf (COTS) computing and software-defined radio (SDR) equipment, as well as custom-built payload platforms for unmanned aerial vehicles (UAVs) or drones in aerial networks.