This paper addresses the challenges of extending cellular network coverage into low-altitude airspace by deploying reconfigurable intelligent surfaces (RISs) near base stations (BSs) to serve unmanned aerial vehicles (UAVs). Existing approaches typically assume the channel is temporally and spatially correlated and the UAV location is known. Without requiring the linearity or continuity of the channel state information (CSI) transition process. we propose to recover the CSI evolution by leveraging a channel covariance-embeded radio map. At each time slot, one pilot is used to observe the high dimensional RISUAV channel. By exploiting both the radio map and the mobility model, we develop a strategy to use sequential one-pilot CSI measurements to jointly estimate the UAV location and CSI. Additionally, the phase shift vector of RIS is adaptively designed with an entropy-minimizing approach, enhancing observation quality under stochastic channel conditions. To initialize phase shifts effectively, a Random-and-Tracking strategy is introduced, combining early-stage random sensing with refined CSI tracking via eigenvector-based sensing. Numerical results, using ray-traced channel data over real city maps, demonstrate that our proposed framework achieves over 94% of the capacity attained with perfect channel knowledge at 15 dB signal-to-noise ratio (SNR), with a UAV speed of$50 ~\text{km} / \mathrm{h}$.

This paper investigates the problem of discovering the channel state information (CSI) evolution using radio maps and the mobility property of the users. The goal is to reduce the pilots for channel estimation to below the sparsity level of the multiple-input multiple-output (MIMO) channel, and this is to be achieved by exploiting both the sparsity and the temporal correlation of the channel. The methodology is motivated by the observation that the mobility of the user is constrained in a two-dimensional area, and therefore, the high -dimensional CSI is a process in a low-dimensional manifold. To discover the CSI evolution based on limited observations, a new radio map structure is proposed which associates a physical location and a sparse access token with a full CSI. A hidden Markov model (HMM) based problem is formulated to estimate the physical trajectory of the user, and a fine channel training with reduced pilots based on the estimated CSI evolution is performed. The experiments are conducted on a MIMO communication environment consisting of 7 base station (BS)s and above 20000 user locations. It is found that the channel estimated by the proposed scheme with only 10 pilots achieves above 96 % of the channel capacity of the baseline where perfect CSI is available.

Many emerging technologies for the next generation wireless network prefer line-of-sight (LOS) propagation conditions to fully release their performance advantages. This paper studies 3D unmanned aerial vehicle (UAV) placement to establish LOS links for two ground terminals in deep shadow in a dense urban environment. The challenge is that the LOS region for the feasible UAV positions can be arbitrary due to the complicated structure of the environment. While most existing works rely on simplified stochastic LOS models and problem relaxations, this paper focuses on establishing theoretical guarantees for the optimal UAV placement to ensure LOS conditions for two ground users in an actual propagation environment. It is found that it suffices to search a bounded 2D area for the globally optimal 3D UAV position. Thus, this paper develops an exploration-exploitation algorithm with a linear trajectory length and achieves above 99% global optimality over several real city environments being tested in our experiments. To further enhance the search capability in an ultra-dense environment, a dynamic multi-stage algorithm is developed and theoretically shown to find an$\delta $-optimal UAV position with a search length$O(1/\delta)$. Significant performance advantages are demonstrated in several numerical experiments for wireless communication relaying and wireless power transfer.

This paper studies optimal unmanned aerial vehicle (UAV) placement to establish line-of-sight (LOS) relaying channels for multiple ground nodes in dense urban areas with arbitrary obstacle topology. The key challenge originates from the fact that the terrain obstacles may have arbitrary locations and shapes, and therefore, the placement problems are non-convex with possibly arbitrarily many local optima. Prior works employ over-simplified probabilistic models for the 3D environment, and thus, the LOS condition cannot be guaranteed. Exploiting the properties of the equipotential surface, it is proved that the globally optimal position in 3D lies on the equipotential surface under certain conditions. With this insight, an adaptive search trajectory on the equipotential surface is developed and the closed-form expressions of search directions are derived using perturbation methods. Numerical experiments using real city map data show that the proposed algorithm achieves over 96.7% of the performance of the exhaustive 3D search scheme in all tested scenarios. Additionally, the proposed algorithm only needs a 172-meter search length, while other baseline schemes have dozens of times longer search trajectories.

This paper studies optimal unmanned aerial vehicle (UAV) placement to establish line-of-sight (LOS) relay channels for ground users, where the signal of the users is likely to be blocked by propagation obstacles surrounding the users. The key challenge is that the LOS region can be arbitrary and non-convex due to the complicated urban environment. Prior works either employ over-simplified probabilistic models for the 3D environment or use relaxation methods to solve the UAV placement problem, and therefore, the LOS condition cannot be guaranteed. By contrast, this paper aims at establishing theoretical guarantees for the optimal UAV relay position to ensure LOS condition for two ground users. By investigating two essential properties of the LOS regions, it is found that it suffices to search a bounded 2D area for the globally optimal 3D UAV position. Based on these properties, this paper develops efficient and dynamical search trajectory on a 2D perpendicular plane. It is theoretically shown to find an$\epsilon-\mathbf{optimal}$UAV position with a search length$O(1/\epsilon)$. In our experiments, the proposed algorithms are shown to achieve above 97% global optimality within a 1-kilometer search distance over a real city environment.