Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is considered as a key technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. However, in XL-MIMO systems, the spatial non-stationary effect naturally exists. Such an effect can hardly be recognized by most existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. In order to deal with this problem, we study the spatial non-stationary channel estimation in XL-MIMO systems in this paper. Specifically, the spatial non-stationary channel in an XL-MIMO system is converted to a series of spatial stationary channels by a proposed group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create the time-domain relevance of non-stationary effect, which brings XL-MIMO the ability to recognize such effect in the space domain. Based on the extracted signals, an on-grid GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm and an off-grid GTBC-based polar-domain simultaneous iterative gridless weighted (GP-SIGW) algorithm are proposed to effectively estimate the non-stationary channel. Then, analyses of the time complexity and performances of the above two algorithms are carried out and the Cramér-Rao lower bound is derived. Finally, numerical results reveal that the proposed algorithms can recognize the spatial non-stationary effect and realize a much more accurate channel estimation than existing schemes.

Terahertz (THz) communication has been considered as one of the promising technologies for future 6G wireless systems. In order to cope with the high path loss in THz systems, reconfigurable intelligent surface (RIS) with low-complexity reflecting elements has been proposed to strengthen signals and improve the spectrum and energy efficiency. In order to acquire accurate direction of the user equippment (UE) to send directional beams, beam training is usually utilized. However, existing beam training frameworks have not taken the wideband beam split effect into consideration, so the beam training accuracy decreases a lot in wideband scenario. To solve the problems mentioned above, we propose an analytical beam training framework in RIS-assisted wideband THz communication systems. Specifically, we firstly propose a power distribution pattern (PDP) based direction estimation scheme, where the exact value of the received power is utilized to analytically calculate the direction. Then, we design the analytical codebook for the proposed framework based on the inherent parameters of the wideband THz system. Simulation results show that the proposed framework can achieve the near-optimal achievable rate performance with a lower beam training overhead.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. In XL-MIMO systems, however, the spatial non-stationary effect naturally exists. Such effect can hardly be recognized by existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. To address this problem, in this paper, we study the spatial non-stationary channel estimation for XL-MIMO systems. Specifically, we propose a group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create and exploit the time-domain relevance of non-stationary effect, which allows XL-MIMO to recognize such effect in the space domain. In this way, the spatial non-stationary channel is converted to a series of spatial stationary channels. To effectively estimate these channels, a GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm is proposed as a solution. Simulation results reveal that the proposed GP-SOMP algorithm can recognize the spatial non-stationary effect in XL-MIMO systems and realize a much more accurate channel estimation than existing schemes.

Terahertz (THz) communications have been widely considered as one of the promising technologies for future 6G wireless systems. In order to cope with the high path loss in THz systems, reconfigurable intelligent surface (RIS) composed of low-complexity reflecting elements can be deployed to generate directional beams. In order to acquire the direction of user equipment (UE) to send directional beams, the acquisition of accurate channel state information (CSI) is very important. Beam training is widely utilized to acquire the CSI. However, existing beam training schemes have not taken the wideband beam split effect into consideration, so the beam training accuracy decreases a lot in wideband scenarios. To solve this problem, in this paper, we propose an analytical beam training framework in RIS-assisted wideband THz communication systems. Specifically, we first propose a power distribution pattern (PDP) based direction estimation scheme, where the exact value of the received power is utilized to analytically calculate the UE direction. Then, we design the analytical codebook for the proposed framework based on the inherent parameters of the wideband THz system. Simulation results show that the proposed framework can achieve the near-optimal achievable rate performance with a lower beam training overhead than existing schemes.