Wireless Energy Harvesting and Power Transfer for Communications and Networks

Topic Information

Dear Colleagues,

Advancements in wireless communication systems have motivated both academics and industries to work toward sustainable and long-lasting networks with low energy cost. Energy harvesting is a potential solution to overcome the issues of high data traffic demand, high energy consumption, complex infrastructures, and device battery limitations. Energy harvesting along with wireless information and power transfer are futuristic options to tailor diversified networks and devices in 6G communication systems.

This Topic primarily focuses on wireless energy harvesting solutions for incorporating complex scenarios and varied ranges of communication networks. This can include energy harvesting in 5G/6G communication systems with a focus on machine-type networks, machine-learning-based techniques, signal processing, and distributed complex systems. Original research in pertinent fields, which has not yet been published or is not presently being considered by another publication, is encouraged such as but not limited to the following.

• Energy harvesting wireless communications and networks.
• Wirelessly powered communications and networks.
• Centralized and distributed power transfer in wireless communications.
• Simultaneous wireless information and power transfer (SWIPT).
• RF, millimeter wave, and THz energy harvesting, power transfer, and SWIPT.
• Massive MIMO- and RIS-aided energy harvesting, power transfer, and SWIPT.
• Waveform design for energy harvesting, power transfer, and SWIPT.
• Signal processing for energy harvesting, power transfer, and SWIPT.
• Machine learning for energy harvesting, power transfer, and SWIPT.
• Circuits and systems for energy harvesting, power transfer, and SWIPT.
• Wireless energy harvesting and power transfer for Internet of Things.
• Wireless energy harvesting and power transfer for sensor networks.
• Wireless energy harvesting and power transfer for machine-type networks.  

Prof. Dr. Yichuang Sun
Dr. Arooj Mubashara Siddiqui
Dr. Xiaojing Chen
Dr. Oluyomi Simpson
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
IoT IoT	-	8.5	2020	15.9 Days	CHF 1200	Submit
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	22.6 Days	CHF 2000	Submit
Network network	-	-	2021	26.5 Days	CHF 1000	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit
Telecom telecom	2.1	4.8	2020	22.7 Days	CHF 1200	Submit
Technologies technologies	4.2	6.7	2013	24.6 Days	CHF 1600	Submit

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Published Papers (3 papers)

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10 pages, 470 KiB  

Open AccessArticle

Transmit Power Optimization in Multihop Amplify-and-Forward Relay Systems with Simultaneous Wireless Information and Power Transfer

by Kunju Kim, Derek Kwaku Pobi Asiedu, Prince Anokye, Eunkyung Kim and Kyoung-Jae Lee

Electronics 2024, 13(21), 4232; https://doi.org/10.3390/electronics13214232 - 29 Oct 2024

Viewed by 724

Abstract

In this paper, we study a multi-hop amplify-and-forward (AF) simultaneous wireless information and power transmission (SWIPT) relay system. Each relay node harvests power using a power split (PS) method from a portion of the received signal, amplifies the remaining received signal, and passes [...] Read more.

In this paper, we study a multi-hop amplify-and-forward (AF) simultaneous wireless information and power transmission (SWIPT) relay system. Each relay node harvests power using a power split (PS) method from a portion of the received signal, amplifies the remaining received signal, and passes it to the next relay. Based on this system model and signal flow, we derived and solved the convex power minimization problem with the optimal PS ratio. In this case, it was found that using the optimal PS ratio consumed a lower amount of power than when using a fixed PS ratio (0.5). We then investigated the impact of processing cost on the AF-SWIPT system using decoding and forwarding SWIPT as benchmarks, and found that AF-SWIPT was superior. Full article

(This article belongs to the Topic Wireless Energy Harvesting and Power Transfer for Communications and Networks)

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Figure 1

Figure 1
<p>Multi-hop AF relay systems with SWIPT architecture.</p> Full article ">Figure 2
<p>Multihop AF relay node SWIPT architecture.</p> Full article ">Figure 3
<p>The influence of increasing each relay <math display="inline"><semantics> <msub> <mi>P</mi> <mi>c</mi> </msub> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mover accent="true"> <mi>γ</mi> <mo>¯</mo> </mover> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> dB).</p> Full article ">Figure 4
<p>Average <math display="inline"><semantics> <msub> <mi>E</mi> <mn>0</mn> </msub> </semantics></math> against the number of relay nodes at <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>γ</mi> <mo>¯</mo> </mover> <mo>=</mo> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> dB, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>c</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>50</mn> </mrow> </semantics></math> dBm.</p> Full article ">Figure 5
<p>Average <math display="inline"><semantics> <msub> <mi>E</mi> <mn>0</mn> </msub> </semantics></math> relative to <math display="inline"><semantics> <mover accent="true"> <mi>γ</mi> <mo>¯</mo> </mover> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>c</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>40</mn> </mrow> </semantics></math> dBm.</p> Full article ">Figure 6
<p>Average <math display="inline"><semantics> <msub> <mi>E</mi> <mn>0</mn> </msub> </semantics></math> relative to <math display="inline"><semantics> <msub> <mi>d</mi> <mi>k</mi> </msub> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>c</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>50</mn> </mrow> </semantics></math> dBm.</p> Full article ">

20 pages, 4003 KiB  

Open AccessArticle

A Hybrid Anti-Collision Protocol Based on Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) for Radio Frequency Identification (RFID) Readers

by Mourad Ouadou, Rachid Mafamane and Khalid Minaoui

Network 2024, 4(2), 217-236; https://doi.org/10.3390/network4020011 - 13 Jun 2024

Viewed by 1043

Abstract

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system [...] Read more.

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity. Full article

(This article belongs to the Topic Wireless Energy Harvesting and Power Transfer for Communications and Networks)

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Figure 1

Figure 1
<p>RFID collisions. (<b>a</b>) Reader-to-Reader Interference; (<b>b</b>) Reader-to-Tag Interference.</p> Full article ">Figure 2
<p>Proposed background environment for RFID readers.</p> Full article ">Figure 3
<p>Proposed algorithm scheme.</p> Full article ">Figure 4
<p>Possible cases for the Time Slot distribution process.</p> Full article ">Figure 5
<p>Possible cases for the frequency distribution process.</p> Full article ">Figure 6
<p>Average success reading vs. number of readers.</p> Full article ">Figure 7
<p>Number of active readers vs. number of readers.</p> Full article ">Figure 8
<p>Number of active readers vs. number of readers for dense network.</p> Full article ">Figure 9
<p>Average success reading vs. number of TSs.</p> Full article ">Figure 10
<p>Total interrogation time vs. number of readers.</p> Full article ">Figure 11
<p>The complexity of the DTS-AC protocol. 1—number of readers, 2—number of readers, 3—number of frequency, 4—number of time slots, 5—read range, 6—control range.</p> Full article ">

22 pages, 4655 KiB  

Open AccessArticle

Optimal Frequency and Wireless Power Budget for Miniature Receivers in Obese People

by Tom Van de Steene, Emmeric Tanghe, Luc Martens, Carmine Garripoli, Stefano Stanzione and Wout Joseph

Sensors 2023, 23(19), 8084; https://doi.org/10.3390/s23198084 - 26 Sep 2023

Cited by 1 | Viewed by 1589

Abstract

This study investigates wireless power transfer for deep in-body receivers, determining the optimal frequency, power budget, and design for the transmitter and receiver. In particular, the focus is on small, in-body receivers at large depths up to 20 cm for obese patients. This [...] Read more.

This study investigates wireless power transfer for deep in-body receivers, determining the optimal frequency, power budget, and design for the transmitter and receiver. In particular, the focus is on small, in-body receivers at large depths up to 20 cm for obese patients. This enables long-term monitoring of the gastrointestinal tract for all body types. Numerical simulations are used to investigate power transfer and losses as a function of frequency and to find the optimal design at the selected frequency for an obese body model. From all ISM-frequencies in the investigated range (1 kHz–10 GHz), the value of 13.56 MHz yields the best performance. This optimum corresponds to the transition from dominant copper losses in conductors to dominant losses in conductive tissue. At this frequency, a transmitting and receiving coil are designed consisting of 12 and 23 windings, respectively. With a power transfer efficiency of $2.70\times {10}^{-5}$, 18 µW can be received for an input power of 0.68 W while still satisfying exposure guidelines. The power transfer is validated by measurements. For the first time, efficiency values and the power budget are reported for WPT through 20 cm of tissue to mm sized receivers. Compared to WPT at higher frequencies, as commonly used for small receivers, the proposed system is more suitable for WPT to large depths in-body and comes with the advantage that no focusing is required, which can accommodate multiple receivers and uncertainty about receiver location more easily. The received power allows long-term sensing in the gastrointestinal tract by, e.g., temperature, pressure, and pH sensors, motility sensing, or even gastric stimulation. Full article

(This article belongs to the Topic Wireless Energy Harvesting and Power Transfer for Communications and Networks)

►▼ Show Figures

Figure 1

Figure 1
<p>Application goal: Wireless power transfer to in-body receivers for obese people. Adapted from [<a href="#B53-sensors-23-08084" class="html-bibr">53</a>].</p> Full article ">Figure 2
<p>The tissue model, consisting of symmetrically stacked cylindrical layers of tissue. (<b>a</b>) shows the model in 3D. The transmitter, consisting of a single loop coil is shown at the left base of the cylinder. The receiver is located inside the model, although very small. (<b>b</b>) shows a cross-section of the model. the left side shows (half of) the original model, the right side shows (half of) the model with modified tissue thickness. Both the coaxial and coplanar configurations are shown for Tx. Rx is very small, and is parallel to Tx.</p> Full article ">Figure 3
<p>A general two-port network. The relation between voltages and currents at each of the ports is given by the matrix of Z-parameters in Equation (<a href="#FD1-sensors-23-08084" class="html-disp-formula">1</a>).</p> Full article ">Figure 4
<p>Cross-sectional view of the Tx coil, illustrating the definition of each of the investigated parameters: axial and radial number of windings (<math display="inline"><semantics> <msub> <mi>N</mi> <mi>a</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>N</mi> <mi>r</mi> </msub> </semantics></math>), outer and inner radii (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math>), winding diameter (<span class="html-italic">d</span>) and winding spacing (<math display="inline"><semantics> <msub> <mi>i</mi> <mi>a</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>i</mi> <mi>r</mi> </msub> </semantics></math>).</p> Full article ">Figure 5
<p>Contributions to <math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math>, showing the influence of copper losses (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>C</mi> <mi>o</mi> <mi>p</mi> <mi>p</mi> <mi>e</mi> <mi>r</mi> </mrow> </msub> </semantics></math>), tissue losses (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>T</mi> <mi>i</mi> <mi>s</mi> <mi>s</mi> <mi>u</mi> <mi>e</mi> </mrow> </msub> </semantics></math>), radiation losses (<math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>R</mi> <mi>a</mi> <mi>d</mi> <mi>i</mi> <mi>a</mi> <mi>t</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </semantics></math>) and the total sum (<math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math>) for a fixed input current <math display="inline"><semantics> <msub> <mi>I</mi> <mn>1</mn> </msub> </semantics></math> = 1 A.</p> Full article ">Figure 6
<p>Efficiency of the single-loop model at multiple frequency points. The difference between the original model (<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>T</mi> <mi>E</mi> </mrow> </semantics></math>) and the model with modified tissue layers (<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>T</mi> <msub> <mi>E</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>i</mi> <mi>f</mi> <mo>.</mo> <mi>t</mi> <mi>i</mi> <mi>s</mi> <mi>s</mi> <mi>u</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math>) is small and the graphs overlap at frequencies up to <math display="inline"><semantics> <mrow> <mn>100</mn> <mspace width="3.33333pt"/> <mi>M</mi> <mi>H</mi> <mi>z</mi> </mrow> </semantics></math>. The coplanar model (<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>T</mi> <msub> <mi>E</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>p</mi> <mi>l</mi> <mi>a</mi> <mi>n</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>) mainly differs at lower frequencies, where the latter yields lower efficiency. The influence of all factors contributing to the PTE (Equation (<a href="#FD2-sensors-23-08084" class="html-disp-formula">2</a>)) is shown as well (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>Z</mi> <mn>21</mn> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math>).</p> Full article ">Figure 7
<p>Simulated PTE for multiple Tx designs as a function of (<b>a</b>) winding diameter and (<b>b</b>) input resistance <math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math>. The color shows the total number of windings, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>a</mi> </msub> <mo>·</mo> <msub> <mi>n</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. A supplementary blue line is drawn, showing the trend for all points with the final number of 12 windings.</p> Full article ">Figure 8
<p>Efficiency of Rx (PTE, vertical axis) for multiple designs, showing the relation with (<b>a</b>) winding diameter (d, horizontal axis), radial number of windings (color) and (<b>b</b>) the resistance <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> (horizontal axis) and winding diameter (d, color) for a fixed radial number of windings of 1. The light blue line shows all points with a winding diameter of 0.079 mm.</p> Full article ">Figure 9
<p>Simulation model to investigate exposure during WPT. The Tx coil (represented by the white circles) is placed at 10 different, randomly distributed locations around the abdomen. During every simulation, only one Tx coil is present.</p> Full article ">Figure 10
<p>Prototypes of Transmitting and Receiving coils. (<b>a</b>) Prototype of the Transmitting coil with 12 windings of 1.3 mm in a 3 × 4 configuration. (<b>b</b>) Prototype of the Receiving coil with 23 windings of 0.08 mm in the axial direction.</p> Full article ">

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