# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 1 Introduction  
Current research on different sources of illumination for passive radars has been very intense. Besides well-known illuminators such as digital video broadcasting--terrestrial (DVB-T) [1, 2], digital video broadcasting--satellite (DVB-S) [3], satellites in geosynchronous orbits [4], the Global Navigation Satellite System (GNSS) [5], frequency-modulated (FM) analogue radio [6, 7], or digital audio broadcasting (DAB) [2, 8] radios, one may note several novel ideas involving 5G [9], long-term evolution (LTE) [10] and Starlink [11, 12, 13] transmission for target detection. Theoretical studies and simulations show that these sources of illumination are suitable for passive radar purposes. Recent experiments have proved the ability to detect large targets, such as cars, using newly deployed telecommunications standards, e.g., the 5G network [9, 14]. However, the continuous dynamic growth of the number of unmanned aerial vehicles (UAVs) has created a demand for drone detection capability (flight control and security). Passive radar based on a 5G network is a promising technology regarding the satisfaction of this demand. It is characterized by low cost and easy deployment thanks to there being no need for it to have its own transmitter and transmission license. 5G networks provide excellent coverage of base stations and a relatively wide band, therefore, one can detect small targets such as UAVs. The latter parameter (signal bandwidth) is of particular interest, since it allows for the obtaining of high-range resolution. For example, in Poland, the 5G network will cover up to 80 MHz from a single telecommunications provider, which gives up to 400 MHz for five expected providers [9]. This excellent value gives a maximum range resolution of 75 cm and, in the future, it certainly could be used for radar imaging and micro-Doppler signature analysis. Some experiments covering the utilization of a 5G network for passive radar UAV detection have already been conducted. In [15], the authors describe simulations of LTE and 5G New Radio (5G NR)-based radar and experiments with successful drone (holding a multi-faceted reflector) detection using synthetically generated waveforms. In [16], a concept of using 5G base stations as a millimeter wave active radar system capable ofdetecting and identifying UAVs was presented. However, the authors did not consider the specific 5G waveform, and the simulation was carried out without assumptions on both signal and network properties (content-dependent transmission). Moreover, the simulations in [16] comprised only the parameter estimation of the signal reflected from a drone (micro-Doppler analysis), neglecting the problem of target detection in a practical scenario. Successful UAV detection utilizing a software-defined radio (SDR) transmitter was also reported in [17]. In [9], a passive detection of a car was obtained employing the same cooperative 5G network as in the present article. In [9], many challenges and difficulties connected with 5G passive radar systems were addressed, but none of them were solved. One problem in 5G-based passive radar is a content-dependent transmission that disables target detection when the base station does not emit any downlink signal. Furthermore, there have still not been any experiments reported with a real-life and operational 5G network used as a source of illumination for passive drone detection. Thus, this topic is the main novelty presented in this paper. Content-dependent transmission is the main problem limiting the possibility of using passive radar based on illumination from the 5G network. As a result, the illumination has a variable filling both in time and frequency. It may happen that in the absence of data to be transmitted via downlink, the amount of signal is reduced to a minimum. Furthermore, in the case of a partial resources allocation, there may be situations where the full downlink allocation will be temporary, and the rest of the time it will use only small portions of frequency.