Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 38, Issue 8, Pages 1719-1734Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3000802
Keywords
Optimization; Erbium; MIMO communication; Wireless communication; Energy harvesting; Iterative methods; Array signal processing; Intelligent reflecting surface (IRS); large intelligent surface (LIS); SWIPT; energy harvesting; MIMO
Funding
- U.K. Engineering and the Physical Sciences Research Council [EP/N029666/1]
- Engineering and Physical Sciences Research Council [EP/Noo4558/1, EP/PO34284/1]
- Royal Society's Global Challenges Research Fund Grant
- European Research Council
- EPSRC [EP/N029666/1] Funding Source: UKRI
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An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.
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