A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2102.00767v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future wireless communication systems for close-distance communication network. This paper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication designed to improve energy collection performance while satisfying wireless information and Power Transfer (WIPT). The designed system consists of an IRS-assisted system consists of a multi-antenna assisted base station (BS) and two opposite<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.00767v1">arXiv:2102.00767v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x5ywrm5wyzgllj2kpqy7oioyay">fatcat:x5ywrm5wyzgllj2kpqy7oioyay</a> </span>
more »... nna assisted information receiver cooperated (RIS) as energy receiver (ERs) that meets energy collection requirements. Based on the electromagnetic property of Reconfigurable Intelligent Surface (RIS), like two mirrors that are opposite each other, setting two Reconfigurable Intelligent Surface (RIS) attached to the city buildings to reflect the sending signals. The transmitting precoding of the Multi-antenna Auxiliary Base Station (BS) and the angular phase transfer matrix of the multi-antenna Auxiliary Information Receiver (IRs) need to be optimized together to maximize the energy harvesting of IoT devices for energy efficiency (EE) of the IRs system and to provide users with the efficiency of the received signal. In order to solve the joint optimization problem effectively, we turn the non-convex maximize problem into the equivalent formal error method based on the mean square, and finally use the iterative algorithm for optimization. As for algorithm, we respectively use MSE method, semidefinite relaxation techniques to simplify transmitting beamforming matrix and the matrix phase shift. Through the observation of simulation data, it can be concluded that the performance optimization method of SDR based on RIS is effective.
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