Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

LEO satellites are key to global coverage in 6G wireless communications. However, efficiently selecting service satellites in multi-orbit systems remains a challenge due to dynamic topologies and limited resources. To address this challenge, this paper proposes a joint optimization framework for satellite selection, user association, and resource allocation in Space-AirGround Integrated Networks (SAGINs). We design a computationally efficient algorithm that leverages Markov approximation for satellite selection and employs matching game theory for user association and resource allocation. Our simulation results show that the proposed algorithms outperform benchmark methods.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-out (MIMO) communication systems is proposed. The model includes a more general path loss model and a small-scale fading channel model that considers the RIS electromagnetic (EM) response and RIS polarization adjustment. In the path loss model, a method for generating large-scale parameters with height-dependent is proposed. In the small-scale fading model, four different propagation conditions are given to reflect the diverse link states among base station, RIS, and user equipment, depending on whether the sub-channel is line-of-sight (LOS) or non-line-of-sight (NLOS). Different from the conventional phase shift (PS) model, the RIS response matrix in the small-scale fading channel model can distinguish EM waves scattered from different directions. The accuracy of the proposed channel model is further verified by real channel measurements. The results can well guide the practical deployment of RIS in future sixth generation (6G) wireless communication systems.

Nowadays, Reconfigurable intelligent surface (RIS) is regarded as one of the key technology of the sixth generation (6G) wireless communication systems. However, most of the current researches are based on single RIS. In this paper, we propose a three-dimensional (3D) double-RIS-assisted geometry-based stochastic model (GBSM) for massive multiple-input multiple-output (MIMO) communication systems. The channel model also supports the movements of transmitter, receiver, and clusters. For RIS, a new method is proposed for the joint design of reflection coefficients in MIMO channels based on cascaded RISs. In addition, different channel properties in the spatial domain, time domain, and frequency domain are studied to verify the validity and non-stationary properties of the model and explore the improvement of channel performance by double RISs.

Reconfigurable intelligent surface (RIS) is a potential solution to cost-effectively and energy-efficiently improve the performance of the sixth generation (6G) wireless communication system. In this paper, a method to realize the function of RIS in three dimensional (3D) ray tracing simulator is proposed and applied to two indoor scenarios where there is no line-of-sight (LoS) path between transmitter (Tx) and receiver (Rx). To verify the effectiveness of the proposed method, channel characteristics including received power, channel capacity, and angular power spectral density (PSD) are studied. To analyze the influence of different RIS deployment locations on coverage enhancement, simulations with different setups are carried out at 5.4 GHz. Besides, applicability of RIS in millimeter wave (mm Wave) is also demonstrated in ray tracing simulator. Numerical results show a significant coverage improvement by adding RIS into current wireless communication system and reveal useful insights for optimal RIS deployment in indoor scenarios.

Reconfigurable intelligent surfaces (RISs) are 2-D metasurfaces, which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth-generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this article, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. First, the concept, principle, and potential applications of RIS are given. An overview of RIS-based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS-based channel characteristics are studied, including reflection and transmission, the Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field, near field, and so on. RIS-based channel modeling works are investigated, including large-scale path loss models and small-scale multipath fading models. Finally, future research directions related to RIS-assisted channels are also discussed.