**1. Introduction**

Recently, with the rapid growth of wireless communication systems, researchers have studied various challenges about improvement of these systems from many aspects such as performance, error optimization, hardware design and implementation and etc. with introducing the wireless sensor networks, Internet of Things (IoT) and robotics, one of the main challenges appeared is the energy consumption of these systems and how to provide reliable and low cost power supply to feed these systems as long as possible with high durability. That is the main reason for all of the researches conducted on energy harvesting. Various methods and approaches are presented to tackle the issue e.g. improvement of batteries structure and their capacity, piezoelectric materials and movement of human body part to produce the required power mostly for wearable devices, or thermal and magnetic energy harvesting approaches.

Mainly, there are two energy sources: mechanical and magnetic waves [1]. For energy harvesting purposes, as in [2–5], natural sources like solar energy wind, vibrations and movement of human body parts or magnetic waves can be exploited. Here in this chapter, our focus is on a specific kind of electromagnetic source, Radio Frequency (RF) signal, which is produced by the oscillation of photons in a certain frequency and used for transmitting data and information in communication systems.

Examples of these transceivers in today's world are Frequency Modulation (FM) radio, Analog TV (ATV), Digital TV (DTV), mobile and cellular networks and Wi-Fi. To have a more clear understanding of the issue and seeing RF signals as a energy source, in **Figure 1**, DTV and cellular signal spectrums for Tokyo City and Yokohama City are indicated [6]. As it can be seen in this figure, for some certain frequencies, the measured power is about 0 and −20 dB.

By saying RF energy harvesting, we mean that we capture the energy from the RF signal existing in the ambient and transform this power into DC power and using it for supplying battery. Passive ambient RF energy harvesting is exactly defined as this procedure [7]. In this case, sources can be FM radio, Wi-Fi, DTV or military communication transmitters [8] and the amount of energy harvested from these sources is in the order of 1 to 10\_\_\_\_ *W cm*<sup>2</sup> [9]. Also there is another type of RF energy harvesting, i.e. RF energy harvesting from a dedicated source. In this scenario, the amount of harvested energy is higher comparing to passive ambient case and is in the order of 50\_\_\_\_ *W cm*<sup>2</sup> [9]. One example for this category is RFID chips [10].

#### **1.1 Preprocessing in energy harvesting system**

Spectrum is a scarce source. In wireless communication systems, efforts have been made to use frequency spectrum with policies and priorities in order to maximize the spectrum efficiency. The main idea here is to allocate empty spectrum holes over time, frequency and space to secondary users while the interference with primary user is minimum. Several approaches are proposed for spectrum sensing, such as energy detection [11–13], matched filter [11, 12, 14], cyclostationary detection [15, 16], spectrum sensing based on covariance matrix [17, 18] and wavelet based spectrum sensing [19]. By studying energy detection, it can be understood that this approach is based on detecting the signal power such a way that secondary users detect the signal power received from primary users. Then they compare it to some predefined threshold level and then decide whether they can use the primary frequency band or not. Well, here is the novel preprocessing idea which we are exploited in this chapter:

"In RF energy harvesting, we use RF signal power and convert it to DC power for charging batteries. On the other hand, energy detection algorithms give us the

#### **Figure 1.**

*DTV signal spectrum measured in Tokyo City (left side graph) and Cellular signal spectrum measured in Yokohama City (right side graph) [6].*

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**Figure 3.** *Battery model [34].*

**Figure 2.**

*Schematic of proposed system.*

*RF Energy Harvesting System and Circuits for Charging of Wireless Devices Using Spectrum…*

ability to detect the ambient signal power and compare it to a predefined threshold level. So, we do the same here as well. But with the goal of detecting a strong RF signal. In other words, by exploiting this preprocessing, we compare our received RF signal power and if it is greater than a threshold, then we will decide that frequency contains our required power for energy harvesting purposes and switch our circuit to that frequency. In the simulation process, this threshold is set to 0 dBm which is a

Our proposed system is indicated in **Figure 2**. As it is indicated in this figure, by preprocessing stage, the frequency containing the high amount of energy is selected. After that, this signal is selected as the input of matching circuit and rectified. Then a DC-DC converter circuit is used to level up the DC signal and finally it

For simulation stage and performance evaluation of our proposed system, we must be able to model the battery that we intend to charge. There are various battery models with different structures and complexities. Electrochemical models [20–22] are usually used for battery physical design, performance and power generation optimization. Mathematical models [23–26], are much more effective. Random events for predicting battery systematic behaviors like battery life time and efficiency are discussed using mathematical equations. Electrical models [27–33] are placed somewhere between mathematical and chemical models in terms of accuracy and utilize the combination of voltage sources, resistors and capacitors.

*DOI: http://dx.doi.org/10.5772/intechopen.84526*

**2. Proposed method**

**2.1 Battery model**

is fed to the battery for charging.

reasonable and practical assumption based on **Figure 1**."

*RF Energy Harvesting System and Circuits for Charging of Wireless Devices Using Spectrum… DOI: http://dx.doi.org/10.5772/intechopen.84526*

ability to detect the ambient signal power and compare it to a predefined threshold level. So, we do the same here as well. But with the goal of detecting a strong RF signal. In other words, by exploiting this preprocessing, we compare our received RF signal power and if it is greater than a threshold, then we will decide that frequency contains our required power for energy harvesting purposes and switch our circuit to that frequency. In the simulation process, this threshold is set to 0 dBm which is a reasonable and practical assumption based on **Figure 1**."
