In this paper, we propose a precoding method to maximize energy efficiency (EE) in a downlink multiuser massive multiple-input multiple-output system with low-resolution quantizers. To this end, we formulate an EE maximization problem with respect to precoders by incorporating the quantization errors caused by the low-resolution quantizers. The main challenges exist: i) the quantization errors are entangled with the precoders, ii) a objective function is non-convex, and iii) unlike a spectral efficiency (SE) maximization problem, a precoding power needs to be jointly optimized. To address these challenges, we first adopt a Dinkenbach method and reformulate the EE problem to a more tractable form. We further decompose the problem into an optimal precoding direction and transmit power problems. To find the optimal direction, we derive a first-order Karush-Kuhn-Tucker (KKT) condition and interpret the condition as a generalized eigenvalue problem. Accordingly, adopting a generalized power iteration-based precoding method, we find the principal eigenvector which is the best sub-optimal precoder. Regarding the transmit power optimization, the objective function becomes concave for given other variables. Hence, the transmit power level is optimized by using a gradient descent method. Via simulations, we demonstrate that the proposed algorithm provides the highest EE performance compared to baseline methods.
|Title of host publication||2022 IEEE Wireless Communications and Networking Conference, WCNC 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2022|
|Event||2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 - Austin, United States|
Duration: 2022 Apr 10 → 2022 Apr 13
|Name||IEEE Wireless Communications and Networking Conference, WCNC|
|Conference||2022 IEEE Wireless Communications and Networking Conference, WCNC 2022|
|Period||22/4/10 → 22/4/13|
Bibliographical noteFunding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2019R1G1A1094703) and (No. 2021R1C1C1004438), and in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2017-0-01635) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
© 2022 IEEE.
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