## 2 Background and Related Works  
The radio frequency identification (RFID) is currently the most widely used real-life technology, which utilizes the RF-based WPT principle. The transmitted by the reader radio signal is received, remodulated and backscattered by a generally-passive tag. The traditional RFID tags were sending only the static information, stored in their non-volatile memory: the unique identifiers, serial number or product-related data. Nonetheless, the contemporary tags can integrate one or multiple sensors ([13; 14; 15]), which are powered from the incoming signal and data of which are scattered back. The two principal limitations of the RFID technique are the very limited complexity of the respective applications due to the scarce power budget availability, and the need of having a reader (or the transmitter) device decently close to the tags for the system to run. The ways to overcome these limitations have been proposed in several studies. For example, in Ref. [16], the authors introduced into a tag a designated energy harvester combined with an energy buffer. The collected and accumulated energy was used to feed the power amplifier, thus extending the potential range of communications. Among other notable approaches, enabling to extend the functionality of RFID, are the bistatic and ambient backscatter approaches (refer, e.g., to Ref. [17]). The former implies that the RF signal source and the receiver of the backscattered signal are different devices. This approach enables for simplifying and optimizing the structure, thus enabling for performance improvements and cost optimizations. The latter approach refers to tags using the signals from ambient RF sources (e.g., television or radio, or WiFi) rather than that originating from the dedicated RF sources. Albeit these modifications improve the utility of RFID dramatically, their functionality remains limited and cannot match that of the conventional IoT devices. The aspects related to engineering and employing RF WPT to power the IoT devices were investigated in a series of studies. The thoughtful survey of the device-level aspects is provided in Ref. [11]. To get an insight into the potential performance of the WPT solutions, in Table 1, we overview the efficiencies of some prototype WPTimplementations reported in the literature. Note that Table 1 illustrates the efficiencies for RF-DC conversion and thus does not account for the power losses in the radio channel. Aside from the research prototypes, the RF-based WPT commercial solutions are also available. For example, the P2110B RF energy harvesting chipset [18] from Powercast(r) (Pittsburgh, PA, USA) can collect energy from the signals of above \(-14\) dBm and features RF-DC conversion efficiency of about 62% in 900 MHz band for three dBm input signal. The major downside of this solution is the cost--which exceeds 30 EUR (34 USD) per a single receiver for the small-scale orders. A significant percentage of the electromagnetic signal energy is lost when the signal travels through the radio channel--this is the law of nature. The losses can be somewhat mitigated by operating in the line-of-sight and directing the power transfer towards the receiver. [PERSON] et al. [39] proposed an adaptive multi-antenna system with channel estimation, duty-cycle control, and beamforming algorithms, which dynamically steer the beams towards the sensor nodes. Experimental results indicate that the beamforming allows for increasing the amount of received power up to six times compared to the random beamforming scheme. Another promising observation is that the RF energy transfer efficiency linearly increases with the number of transmit antennas when the beamforming algorithms are applied. In Ref. [40], [PERSON] et al. extended the previous research by combining beamforming and energy neutral control algorithm.