Channel Twinning: An Enabler for Next-Generation Ubiquitous Wireless Connectivity

The emerging concept of channel twinning (CT) has great potential to become a key enabler of ubiquitous connectivity in next-generation (xG) wireless systems. By fusing multimodal sensor data, CT advocates a high-fidelity and low-overhead channel acquisition paradigm, which is promising to provide accurate channel prediction in cross-domain and high-mobility scenarios of ubiquitous xG networks. However, the current literature lacks a universal CT architecture to address the challenges of heterogeneous scenarios, data, and resources in xG networks, which hinders the widespread deployment and applications of CT. This article discusses a new modularized CT architecture to bridge the barriers to scene recognition, cooperative sensing, and decentralized training. Based on the modularized design of CT, universal channel modeling, multimodal cooperative sensing, and lightweight twin modeling are described. Moreover, this article provides a concise definition, technical features, and case studies of CT, followed by potential applications of CT-empowered ubiquitous connectivity and some issues requiring future investigations.

Modem Optimization of High-Mobility Scenarios: A Deep-Learning-Inspired Approach

The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal frequency division multiplex (OFDM), cannot deal with the severe Doppler spread brought by high mobility. To address this problem, some new modulation schemes, e.g. orthogonal time frequency space and affine frequency division multiplexing, have been proposed with different design criteria from OFDM, which promote reliability with the cost of extremely high implementation complexity. On the other hand, end-to-end systems achieve excellent gains by exploiting neural networks to replace traditional transmitters and receivers, but have to retrain and update continually with channel varying. In this paper, we propose the Modem Network (ModNet) to design a novel modem scheme. Compared with end-to-end systems, channels are directly fed into the network and we can directly get a modem scheme through ModNet. Then, the Tri-Phase training strategy is proposed, which mainly utilizes the siamese structure to unify the learned modem scheme without retraining frequently faced up with time-varying channels. Simulation results show the proposed modem scheme outperforms OFDM systems under different highmobility channel statistics.

Sparse Estimation for XL-MIMO with Unified LoS/NLoS Representation

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.

Capacity Enhancement for Reconfigurable Intelligent Surface-Aided Wireless Network: from Regular Array to Irregular Array

Reconfigurable intelligent surface (RIS) is promising for future 6G wireless communications. However, the increased number of RIS elements results in the high overhead for channel acquisition and the non-negligible power consumption. Therefore, how to improve the system capacity with limited RIS elements is essential. Unlike the classical regular RIS whose elements are arranged on a regular grid, in this paper, we propose an irregular RIS structure. The key idea is to irregularly configure a given number of RIS elements on an enlarged surface, which provides extra spatial degrees of freedom compared with the regular RIS. In this way, the received signal power can be enhanced, and thus the system capacity can be improved. Then, we formulate a joint topology and precoding optimization problem to maximize the capacity for irregular RIS-aided communication systems. Accordingly, a joint optimization algorithm with low complexity is proposed to alternately optimize the RIS topology and the precoding design. Particularly, a tabu search-based method is used to design the irregular RIS topology, and a neighbor extraction-based cross-entropy method is introduced to optimize the precoding design. Simulation results demonstrate that, subject to the constraint of limited RIS elements, the proposed irregular RIS can significantly enhance the system capacity.

Wideband Channel Estimation for THz Massive MIMO

Terahertz (THz) communication is considered to be a promising technology for future 6G network. To overcome the severe attenuation and relieve the high power consumption, massive MIMO with hybrid precoding has been widely considered for THz communication. However, accurate wideband channel estimation is challenging in THz massive MIMO systems. The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect. In this paper, we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation. Specifically, a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect. Based on the analysis, we define a series of index sets called as beam split patterns, which are proved to have a one-to-one match to different physical channel directions. Inspired by this one-to-one match, we propose to estimate the physical channel direction by exploiting beam split patterns at first. Then, the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window. This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction. The above estimation procedure will be repeated path by path until all path components are estimated. The proposed scheme exploits the wideband channel property implied by the beam split effect, which can significantly improve the channel estimation accuracy. Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.