AI Article Synopsis
- The concept of Intelligent Reflecting Surfaces (IRS) is being explored to enhance wireless communication efficiency, particularly in mmWave systems.
- As the number of IRS elements grows, traditional beam training becomes time-consuming because it requires identifying optimal beam pairs for connections between base stations and IRS, as well as IRS and mobile stations.
- This paper introduces a fast beam training method using two types of beam training signals, Zadoff-Chu (ZC-BTS) and m-sequence (m-BTS), which effectively optimize beam selection in complex environments, leading to a significant reduction in training time.
Article Abstract
The concept of an intelligent reflecting surface (IRS) has recently emerged as a promising solution for improving the coverage and energy/spectral efficiency of future wireless communication systems. However, as the number of reflecting elements in an IRS increase, the beam training protocol in IRS-assisted millimeter-wave (mmWave) cellular systems requires a large beam training time because it needs to find the best beam pairs for the link between the base station (BS) and the IRS, as well as the link between the IRS and the mobile station (MS). In this paper, a fast beam training technique for IRS-assisted mmWave cellular systems with a uniform rectangular array is proposed for detecting the best beam pairs of BS-IRS and IRS-MS links simultaneously. Two different types of beam training signals (BTSs) are proposed to distinguish simultaneously transmitted beams from the BSs in multi-cell multi-beam environments: the Zadoff-Chu sequence based BTS (ZC-BTS) and m-sequence based BTS (m-BTS). The correlation properties of ZC-BTSs and m-BTSs are analyzed in multi-cell multi-beam environments. In addition, the effect of symbol time offset on the ZC-BTS and m-BTS is analyzed. Finally, simulation results reveal that the proposed technique can significantly reduce the beam training time for IRS-assisted mmWave cellular systems.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309934 | PMC |
| http://dx.doi.org/10.3390/s21144936 | DOI Listing |
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