Statistical analysis on ambient RF energy harvesting for low‐power wireless applications

Abstract

Renewable energy sources from the earth constitute another option apart from the available ones for wellspring of energy for economizing on cost of power supply. The energy obtained from ambient sources is called energy harvesting. Energy-harvesting low-power systems have acquired a remarkable consideration as a viable hotspot for expanding both energy efficiency and spectral efficiency. Radio frequency (RF) energy harvesting from ambient source is a promising technique for fulfilling the irreplaceable power prerequisites for powering the low-power devices. Hence, it requires the development of an antenna for harvesting RF energy. In this paper, a coplanar waveguide (CPW) antenna has been designed and fabricated using FR4 lossy substrate. This CPW antenna covers frequency bands from the most important RF patrons (GSM 900, GSM 1800, 3G, and Wi-Fi) within the frequency range from (0.58 to 3 GHz) with a percentage fractional bandwidth of 116% with the center frequency of 1.65 GHz. The fabricated antenna then has been experimentally validated at SSN College of Engineering campus. The effects on the RF power density level for CPW antenna are examined by statistical approach known as Taguchi method. The L9 and L8 orthogonal arrays and analysis of variance are implemented to analyze the execution qualities. The CPW antenna control factors are distance, time, and number of receivers. Then, statistical test (P) are used to determine the significant factors on RF power density. KEYWORDSANOVA, CPW antenna, RF energy harvesting, spectral survey, Taguchi orthogonal array | INTRODUCTIONThe enormous growth in communication networks has triggered persistent expansion of the need for energy supply, leading to a battery exhaustion issue for wireless communication devices. 1 Research work has been conducted recently to increase energy from ambient sources, which is an alternate option for maintaining a sustainable energy supply instead of rechargeable or replaceable batteries. 2 Energy harvesting has been proposed for accomplishing this and ensuring unlimited energy supply to electronic systems and saving energy consumption. 3,4 Hence, energy harvesting grasps energy from the ambient environment and stores them for future use. There are different types of energy harvesting such as thermal, solar, and radio frequency (RF). Energy harvesting from electromagnetic (EM) radiation has huge consideration because of wireless communications networks. 5 Electromagnetic radiations from the far-field applications have emerged in the form of radio frequency and can be collected by aerial, which converts the radio waves into electric power using rectifier circuits. 6 This paper envisages the usage of harvesting arising from the availability of frequency bands such as Wi-Fi routers, cell phones, and laptops. Cognitive radio can be powered from RF ambient sources, which improve the spectrum efficiency and energy efficiency. 6 Use of the RF signal and the distance between an RF energy source can help harvesting RF energy. First, the purpose of the ...

Statistical analysis of radiofrequency energy harvester with bandpass filter for ultra‐low power applications

“…Usually, the harvested energy from ambient RF is expected to meet the energy needs of low power electronic devices. 15 The block diagram of the proposed RF energy harvester is shown in Figure 2, which includes a BF to reduce the ripples, harmonics, and also to improve the efficiency of the rectification circuit. 14 The design of all the elements in the proposed RF energy harvesting system is explained below.…”

Joint energy allocation and multiuser scheduling in SWIPT systems with energy harvesting

“…Therefore, we consider the linear EH model in the simulations. Finally, although we adopt the ideal linear model in the simulation results, the proposed algorithms in Sections 3 and 4 are still applicable if the non-linear EH model in (5) is adopted in the problem (10). The average harvested energy per TS in the finite horizon is denoted as…”

“…taken during K TSs should satisfy constraints in (10). Let A(s k ) denote the set of feasible actions in state s k .…”

“…and D sum λ, β [31]. In (10), since the action space is discrete, the problem is non-convex. That means the set concluding all costconstraint pairs…”

“…Intel has demonstrated the wireless charging of a temperature and humidity meter as well as a liquid-crystal display using the signals of a television station 4 km away [9]. With the development of wireless energy transfer (WET) technology, the EH circuits can harvest energy over frequency bands from the most important RF patrons (GSM 900, GSM 1800, 3G and Wi-Fi) within the frequency ranging from 0.58 to 3 GHz [10]. Pulkit and Anant [11] investigated the SWIPT in a frequency selective channel by assuming information decoding (ID) and EH can be performed at the same time.…”