Extremely large-scale multiple input multiple output (XL-MIMO) plays an important role in providing ultra-high data rates in high-frequency millimeter-wave (mmWave) and Terahertz (THz) spectrum. Efficient beam training is essential as an enabler to ultra-high data rate transmission. However, the challenges brought by large array apertures and broad bandwidth, such as near-field propagation modeling and wideband near-field beam squint effect, make XL-MIMO systems suffer from high training overhead when carrying out beam training. To address this problem, based on the true-time-delay (TTD) deployment, we propose a frequency-scanning-based beam training scheme by exploiting the near-field channel characteristic for XL-MIMO systems. Firstly, we elaborate on the surrogate distance-angular two-dimensional representation for near-field beams and the corresponding squint effect. Then, a TTD-based 2-phase frequency-scanning scheme is proposed for overhead reduction. Numerical simulations verify that the proposed scheme can realize a comparable sum rate with low training overhead.

Orthogonal Time Frequency Space (OTFS) modulation has exhibited significant potential to further promote the performance of future wireless communication networks especially in high-mobility scenarios. In practical OTFS systems, the subcarrier-dependent Doppler shift which is referred to as the Doppler Squint Effect (DSE) plays an important role due to the assistance of time-frequency modulation. Unfortunately, most existing works on OTFS channel estimation ignore DSE, which leads to severe performance degradation. In this letter, OTFS systems taking DSE into consideration are investigated. Inspired by the input-output analysis with DSE and the embedded pilot pattern, the sparse Bayesian learning based parameter estimation scheme is adopted to recover the delay-Doppler channel. Simulation results verify the excellent performance of the proposed off-grid estimation approach considering DSE.

Intelligent lighting has attracted lots of research interests to investigate all the possible schemes to support this need as human has spent more and more time indoor. Semiconductor-based illumination network is an ideal bearer to carry on this mission. In this paper, we propose the concept of Internet of Light (IoL) and define its key functionalities by introducing the information and communication technologies to the illumination networks. Our latest research progress on high-speed transmission, resource optimization, and light stroboscopic irradiation experiment based on IoL platform show that IoL can not only provide value-added services such as positioning and information transmission but also act like a sensor network as part of Internet of Things infrastructure. It confirms that with sensors for different purposes integrated into the lamp, IoL helps people be aware of the environmental changes and make the adjustment accordingly, can provide cost-effective information service for Internet of Things applications, and supports the non-intrusive optical therapy in the future.