Cellular technology is constantly evolving, and 5G is set to take over the world as the primary network soon. The RIS-assisted mm-wave and sub-THz communication is a promising technology for its development. Now, researchers from South Korea and USA have reviewed the channel estimation frameworks and strategies for the RIS systems. They further discuss the recent developments in training signal design and highlight open challenges in the field, providing the direction for future research.
Fifth-generation (5G) is the latest cellular network technology in telecommunications. It will replace the 4G networks in the coming years. Millimeter-wave (mm-wave) and sub-terahertz (sub-THz) communication is a key technology for supporting the required network capabilities of 5G and beyond. However, it suffers from large propagation loss and blockage of network signal.
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The emerging reconfigurable intelligent surface (RIS) technology is promising. It consists of low-cost passive elements which reflect the signals like a scatterer. The wireless technology can enhance communication performance in the mm-wave and sub-THz frequency bands by compensating for the inherent shortcomings. In addition, RIS reduces the number of antennas required for a transceiver.
A RIS-assisted communication system works best when the channel state information is acquired accurately. The term “channel” refers to a physical medium between a base station (BS) and a user equipment (UE), say, a mobile phone. In the presence of RISs, there are potentially two sub-channels: direct (between BS and UE) and indirect (from BS to RIS, then RIS to UE) channels. They are characterized by parameters such as angle of arrival, angle of departure, path gain, and the number of paths.
Recently, a team of researchers led by Dr. Song Noh of the Incheon National University reviewed channel estimation technology for RIS-assisted systems in mm-wave and sub-THz bands. Their work provides new insights into the technology.
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While existing studies have focused on the separate estimation of the direct and indirect channels, a joint estimation framework is introduced. “This approach is practically essential when there exist direct and indirect channels. It enables the transparent deployment of RISs to wireless users by eliminating the need for switching between direct and indirect channel estimation,” explains Dr. Noh. The paper also discusses representative estimation methods: beam-based, sparse recovery, array signal processing, and data-driven techniques. Researchers indicate further room for improvement relative to the theoretical Cramer-Rao lower bound (CRB).
In addition, the review explores the recent developments in designing training signals essential to enhance channel estimation performance. There are two major approaches: algorithmic-specific design based on discrete Fourier transform (DFT) and systematic design based on CRB. Under limited training overhead, researchers point out that the latter approach outperforms the widely used DFT-based design in terms of minimizing the CRB.
Finally, the paper highlights the potential challenges in developing a practically feasible RIS technology. These include inventing a CRB-achieving estimator, ensuring effective wideband transmission, mitigating interuser interference, exploring the line-of-sight transmission, and comprehending the channel estimation algorithm. Overcoming the above obstacles will result in tremendous benefits.
Dr. Noh elaborates: “Programmable RISs can help radio signals penetrate deeper indoors and extend network coverage. They can also reduce the energy consumption of radio access networks and alleviate the impact of mobile networks on the environment. Moreover, RIS has lucrative economic benefits as well.”
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