## 3 Fundamentals of RF Energy Harvesting
### RF Energy Sources and Availability  
The efficiency of RFEH systems is intricately linked to the availability and strength of ambient RF signals, which serve as the primary energy source. These RF energy sources are broadly categorized into intentional and unintentional emitters. Intentional sources involve dedicated RF power transmitters specifically deployed for wireless power transfer applications, providing a consistent and controllable energy supply. Unintentional sources encompass ambient RF emissions from existing communication infrastructures, including cellular networks, television broadcasts, and Wi-Fi routers, which, while ubiquitous, offer variable energy levels depending on the environment [46; 47]. The ambient RF energy landscape varies significantly across different environments. In densely populated urban areas, the proliferation of communication devices and broadcasting systems results in higher RF power densities. Conversely, rural or remote locations, with fewer such devices, exhibit lower RF energy availability. Studies have indicated that RF power density can \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Supercapacitor** & **Battery** \\ \hline Recharge cycle lifetime & \(>10^{6}\) cycles & \(<10^{3}\) cycles \\ Fastest charging time & sec\(\sim\)min & Hours \\ Fastest discharging time & \(<\)a few min & 0.3–3 h \\ Self-discharge rate & 30\% & 5\% \\ Voltage & 0–2.7 V & 3.7–4.2 V \\ Energy density (Wh/kg) & low (0.8–10) & high (20–150) \\ Power density (W/kg) & high (500–400) & low (50–300) \\ Charging circuit & simple & Complex \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative evaluation of supercapacitors and batteries [45]. range from approximately 0.1 \(\upmu\)W/cm\({}^{2}\) in rural settings to over 10 \(\upmu\)W/cm\({}^{2}\) in urban centres [8]. To effectively harness ambient RF energy, especially given its fluctuating nature, researchers have proposed adaptive techniques such as dynamic spectrum sensing and intelligent frequency selection. These methods enable energy-harvesting devices to identify and utilize the most energy-rich frequencies available in real time, thereby optimizing the energy capture process. Such adaptive strategies are crucial for enhancing the viability of RF energy harvesting in environments where RF energy availability is inconsistent [48; 49]. Visual representations of RF energy availability, such as heatmaps, offer valuable insights into the distribution of ambient RF energy across various environments. Several studies have conducted measurements and developed these visualizations to assess RF power density. A study conducted in Montreal, for instance, measured ambient RF power levels along major streets and highways, providing critical data for understanding RF energy distribution in urban settings. The findings shown in Figure 7 reinforce the feasibility of RF energy harvesting in densely populated commercial districts, where multiple cellular networks operate at higher power levels. However, the study also highlights significant variations in RF power levels across different frequency bands. Notably, GSM and LTE bands exhibit stronger RF signals compared to wireless fidelity (Wi-Fi) and Digital Television (DTV), indicating that RF energy-harvesting systems should prioritize cellular bands for optimal efficiency rather than relying on weaker signals [50; 51]. In contrast, RF power levels in rural areas tend to be significantly lower due to fewer base stations and lower communication activity. A study conducted in Geidam Town, Yobe State, Nigeria, measured RF power densities around mobile telephony base stations, reporting values ranging from 0.3017 mW/m\({}^{2}\) to 1.265 mW/m\({}^{2}\) at distances between 10 m and 100 m from the stations. These values are substantially lower than those recorded in urban environments, reinforcing the challenge of RF energy harvesting in rural areas.