Beam tracking based on trajectory prediction can effectively mitigate beam misalignment for highly maneuverable unmanned aerial vehicles (UAVs), thereby enhancing communication link quality and stability. To improve trajectory prediction accuracy and enable real-time error correction, this paper proposes an Adaptive Unscented Kalman Filter with sampling correction (AUKF-SC). The algorithm dynamically adjusts the covariance matrix to balance the weights between state estimation and observation update. Furthermore, a sampling correction strategy is introduced to ensure that sampling points better capture the true state distribution in high-dimensional systems. Simulation results demonstrate that the AUKF-SC algorithm achieves approximately 17.4% higher prediction accuracy than the conventional Unscented Kalman Filter (UKF), while maintaining low computational complexity. Particularly, the proposed method exhibits robust performance during high-speed UAV maneuvers.

Future autonomous vehicles will incorporate high date rate communications and high-accuracy radar sensing capabilities operating in the millimeter-wave (mmWave) and higher frequencies, which results in Integrated sensing and communication (ISAC). Hybrid beamforming (HBF) is an attractive technology for practical vehicular ISAC systems. The HBF with the partially-connected structure (PCS) can effectively reduce the hardware cost and power consumption compared to fully-connected structure (FCS). But the constant-modulus constraint caused by PCS makes the HBF design problem non-convex, which poses a greater challenge. In this paper, we consider the HBF design with PCS as a weighted minimization problem of the communication and radar beamforming errors under the constant-modulus constraints and power constraints. Dual functions of communication and radar are expressed as a tradeoff in this question. Despite the optimization problem being non-convex and hard to obtain the global minimizer, we reduce the problem into a two-step subproblem including the analog precoder design and digital precoder design. Then, a variable projection-based alternating minimization algorithm is proposed to solve these problems. Unlike previous works, which focused on the relationship between variables to iteratively solve, our method exploits the intrinsic geometric features of the mmWave channel and the variable projection to simplify the solution of the beamformers. Simulation results demonstrate that the proposed algorithm achieves significantly improved performance in terms of the system spectral efficiency over the existing solutions and greatly reduces the computational complexity.

Millimeter Wave (mmW) communication has received more and more attention since it can use the massive available bandwidths to improve the rate of wireless networks. In this paper, we will analyze and optimize the performance of mmW communication system with directional cell discovery based on beam scanning. We first model the mmW communication system with beam scanning and then analyze the effect of the number of the scanning beams and the beam gains on its performance. Since changing the number of scanning beams, K, can not only adjust the beam gains but also control the cell searching delay equivalently, K can be optimized to maximum the system capacity and reduce pilot signal overhead. The simulation and analysis results show that there exists an optimal K which not only makes the system capacity maximum but also trade off the cell searching delay and the beam gain.

Recently, amorphous cells emerge as a promising paradigm in cellular networks to support bursty traffic caused by the user mobility, where dense small cells can cooperate on data delivery to mobile users thus implementing coverage without definite cell shape. In this paper, we propose a novel small cell deployment scheme, aiming at optimizing the network throughput over varying distributions of users' locations. Unlike the traditional research simply assuming one specific distribution of users' locations, our work could deal with the more realistic scenario, where the statistical information of users' locations might change across different time periods. In order to tackle this issue, we formulate the throughput maximization problem over multiple user-location distributions. Then, by applying the simulated annealing algorithm, we develop an iterative location deployment updating algorithm to solve for the efficient deployment as well as cooperation and resource allocation among small cells. Simulation results show that our proposed strategy can effectively improve the system average throughput, cell-edge user throughput, and user fairness as compared to the traditional approach.

In this paper, we investigate the joint impact of both the number of small BSs and the transmit powers on the energy efficiency (EE) of Heterogeneous cellular networks (HetNets). Based on a typical HetNet structure, we propose a feasible focusing searching algorithm to find the optimal number of small BSs and the corresponding transmit power under coverage and spectral efficiency constraints that maximize the network EE. Simulation results show that there exists an optimal number of small BSs and the transmit power which can not only maximize network EE but also satisfy the constraints.