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Machine learning-inspired hybrid precoding with low-resolution phase shifters for intelligent reflecting surface (IRS) massive MIMO systems with limited RF chains

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Abstract

The number of bits required in phase shifters (PS) in hybrid precoding (HP) has a significant impact on sum-rate, spectral efficiency (SE), and energy efficiency (EE). The space and cost constraints of a realistic massive multiple-input multiple-output (MIMO) system limit the number of antennas at the base station (BS), limiting the throughput gain promised by theoretical analysis. This paper demonstrates the effectiveness of employing an intelligent reflecting surface (IRS) to enhance efficiency, reduce costs, and conserve energy. Particularly, an IRS consists of an extensive number of reflecting elements, wherein every individual element has a distinct phase shift. Adjusting each phase shift and then jointly optimizing the source precoder at BS and selecting the optimal phase-shift values at IRS will allow us to modify the direction of signal propagation. Additionally, we can improve sum-rate, EE, and SE performance. Furthermore, we proposed an energy-efficient HP at BS in which the analog component is implemented using a low-resolution PS rather than a high-resolution PS. Our analysis reveals that the performance gets better as the number of bits increases. We formulate the problem of jointly optimizing the source precoder at BS and the reflection coefficient at IRS to improve the system performance. However, because of the non-convexity and high complexity of the formulated problem. Inspired by the cross-entropy (CE) optimization technique used in machine learning, we proposed an adaptive cross-entropy (ACE) 1-3-bit PS-based optimization HP approach for this new architecture. Moreover, our analysis of energy consumption revealed that increasing the low-resolution bits can significantly reduce power consumption while also improving performance parameters such as SE, EE, and sum-rate. The simulation results are presented to validate the proposed algorithm, which highlights the IRS efficiency gains to boost sum-rate, SE, and EE compared to previously reported methods.

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When required, data will be made accessible.

Code availability

Simulation codes will be given after paper acceptance at the following link https://github.com/Hassanbangash12.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China under Projects 62071206

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Authors and Affiliations

  1. Department of Electronic Engineering Information Science, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Shabih ul Hassan & Zhongfu Ye

  2. Department of Electronic Engineering, Baluchistan University of IT, Engineering and Management Sciences Pakistan (BUITEMS), Quetta, 87300, Pakistan

    Talha Mir

  3. School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, ON, N9B 3P4, Canada

    Usama Mir

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  1. Shabih ul Hassan
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Contributions

SuH: preparation, data collection, analysis, drafting of the paper and study conception; ZY: Design, conception, critical review of intellectual content, and supervision; TM: collection, evaluation, and proofreading of data; and UM supervision and critical revision.

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Correspondence to Zhongfu Ye.

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The work described in this paper does not appear to have been influenced by any known competing financial interests or personal relationships, according to the authors.

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Hassan, S.u., Ye, Z., Mir, T. et al. Machine learning-inspired hybrid precoding with low-resolution phase shifters for intelligent reflecting surface (IRS) massive MIMO systems with limited RF chains. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03748-8

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