# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 1 Introduction  
Mobile networks are one of the most important innovations humans have developed. Beginning with voice and text transmission to now being one of the most advanced and popular technologies for providing wireless Internet access and communication services, it will continue to play a key role in our future. The current fifth generation of mobile networks [1, 2] is constantly growing in terms of market share. Its goal is to provide the newest functionalities [3] that were not available before, one of them being integrated sensing and communications (ISAC) [4, 5, 6, 7]. The core task of ISAC is to perform these actions at the same time using a single transmitted signal without a significant negative impact on each other. This can help save a considerable amount of energy and build large, efficient sensor networks of objects such as moving vehicles (cars, drones) in smart cities and mobile intelligent robots in modern factory automation systems. Radar technology has a very long history [8]. In monostatic active radars, a specially designed signal is sent, and its copies coming back to the transmitter, reflected from moving targets, are then analyzed. The time delay of any reflection gives information about thedistance of some target, while its frequency shift, which results from the Doppler effect, gives information about the target velocity. In order to find values of both shifts, radar typically uses the cross-ambiguity function (CAF). In CAF, samples of the received signal (RX) are multiplied by samples of the shifted-in-time and frequency transmitted signal (TX). The multiplication results are then accumulated. Co-ordinates of CAF magnitude maxima, if such peaks are present, inform us about the range and velocity of moving objects. In other words, the radar checks for which time and frequency shift values the TX signal will best correlate with the RX signal. The 2D CAF obtained in this way is called the delay-Doppler map (DDM) or range-velocity map (RVM) of the radar. In bistatic active radar systems, the first station generates and sends a radar waveform, while the second station analyzes received reflections of it. In passive radar technology [9], the publicly available signals are used for vehicle illumination. They are generated and sent by commercial digital communication transmitters, e.g., Digital Audio Broadcasting (DAB), Digital Video Broadcasting-Terrestrial (DVB-T), 4G, and 5G services. The CAF computation, based on its definition, is very time-consuming. There are two main options: (1) first modulate the transmitted signal in frequency and then apply the fast correlation algorithm, (2) first shift the transmitted signal in time and multiply it by the received signal, then perform a spectral analysis of the result using the fast Chirp-Z transform, but only for low-frequency values. However, the computational speed-up offered by both approaches is still significantly lower than required. As a remedy to this situation, in [10], a fast proximate CAF computation method (proximate matched filter) was introduced. Here, the TX and RX signals, being compared, are divided into fragments (batches) and correlated part by part. The theoretical derivation of the method and its efficiency evaluation are presented in [11], while its comparison with other alternative approaches is given in [12]. In [13], a low-cost alternative to the CAF batch algorithm is discussed, i.e., the so-called reciprocal filtering approach. This method is, in fact, equivalent to an OFDM-based radar technique, which is characterized in the following two paragraphs. However, looking at the fundamental problem of ISAC from the communication perspective, signal blocking is necessary in Orthogonal Frequency Division Multiplexing (OFDM)--the now widely used technique in many telecommunication services. It is a consequence of the fact that the data bits are coded in Discrete Fourier Transform (DFT) coefficients of OFDM symbols, and transmitted signal samples result from the inverse DFT of them.