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RIS-enhanced multi-cell downlink transmission using statistical channel state information

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Abstract

This paper considers a multi-cell downlink transmission system, where a reconfigurable intelligent surface (RIS) is deployed to support the connectivity of users in blind areas (i.e., at the cell edges). In order to reduce the instantaneous channel state information (CSI) acquisition overhead at each base station (BS), only statistical CSI is considered at both the BSs and the RIS. Particularly, each BS has only statistical CSI within its own cell. Under these assumptions, we first obtain the approximated ergodic spectral efficiency (SE) for each user. Then, we investigate the BSs beamforming and RIS phase shift joint optimization under different criteria. For the approximated ergodic weighted sum SE maximization criteria, a sub-optimal algorithm is proposed, based on complex circle manifold, to design the RIS phase shift while the statistical maximum ratio transmission beamforming is employed at each BS. To ensure fairness, we adopt the projected subgradient-based algorithm in order to optimize the approximated minimum ergodic user SE. Simulation results demonstrate that the approximated ergodic SE expression matches well with the exact expression, whilst the superiority of the weighted sum SE and fairness performance of the proposed algorithms are verified.

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Acknowledgements

The work of X. LI was supported in part by National Natural Science Foundation of China (Grant Nos. 62231009, 61971126), National Natural Foundation of Jiangsu Province (Grant No. BK20211511), and Jiangsu Province Frontier Leading Technology Basic Research Project (Grant No. BK20212002). The work of S. JIN was supported by National Natural Science Foundation of China (Grant No. 61921004). The work of M. MATTHAIOU was supported by Research Grant from Department for the Economy Northern Ireland under US-Ireland R&D Partnership Programme.

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Li, X., Jiang, L., Luo, C. et al. RIS-enhanced multi-cell downlink transmission using statistical channel state information. Sci. China Inf. Sci. 66, 212301 (2023). https://doi.org/10.1007/s11432-022-3723-5

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  • DOI: https://doi.org/10.1007/s11432-022-3723-5

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