On the Feasibility of Wireless Energy Transfer Using Intelligent Reflecting Surface in Next Generation IoT Networks.

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Wireless energy transfer (WET) is a promising energy harvesting technology in which the sensor node harvests energy from electromagnetic radiations instead of traditional wired energy sources. However, there are several design challenges that must be addressed in order to implement WET efficiently. Firstly, only a very small fraction of the energy radiated by the source can actually be harvested by the sensor node due to severe path loss. Secondly, the receive power levels that are suitable for reliable data transfer and decoding in conventional wireless communication may not be sufficient for activating the harvester in these sensors. Thirdly, design of WET systems must ensure that the sensors harvest more than what they consume in tasks related to uplink channel estimation, sensing, computation and communication. In order to tackle these challenges mentioned above, intelligent reflecting surface (IRS), which comprises of an array of low cost reflecting elements that are passive in nature and do not require dedicated radio-frequency (RF) chains can be used. Each of these elements in an IRS is capable of inducing changes in amplitude and phase of the incident electromagnetic signal. And by appropriately programming these reflecting elements, constructive interference-aided boost in the strength of the received signal can be obtained. This talk will elucidate feasibility of WET using IRS. Specifically, we will focus on a wireless scenario where the source is equipped with multiple antennas but a single RF chain (to reduce cost, power consumption and hardware complexity) and is assisted by an IRS to transfer energy wirelessly to a sensor node. For this model, we will first discuss a near-optimal antenna selection and passive beamforming strategy that requires fewer pilot transmissions to obtain channel state information (CSI), thus increasing the time available for WET in a coherence interval. We will then discuss the outage analysis with both perfect and imperfect CSI to obtain insights into WET system design. Extensions to performance of WET under subset AS, discrete phase shifts at IRS, multi-user scenario and spatial correlation will also be presented. We will present results to illustrate that we can trade-off active RF chains at the source with passive elements at IRS to obtain improved performance both in terms of outage probability and power transfer efficiency.

Salil Kashyap, IIT Guwahati

Salil Kashyap is an Assistant Professor in the Dept. of Electronics and Electrical Eng. at IIT Guwahati. Before joining IIT Guwahati, he was a senior DSP Engineer at Marvell where he designed physical layer algorithms for next generation WLANs (IEEE 802.11ax). Prior to that, he was a post-doctoral researcher at Linköping University, Sweden. He received his PhD from IISc Bangalore, M.Tech from IIT Guwahati and B.Tech from NERIST. His research spans areas of wireless communications, networks and signal processing with emphasis on mathematical modeling, performance analysis and algorithm design for 5G and beyond 5G cellular communication systems.