This paper proposes a double phase shifter–based hybrid beamforming (DPS-HBF) framework for millimeter-wave NOMA systems, enabling the simultaneous realization of beam steering and beam focusing within a unified analog architecture. By superposing two independent phase-shifter vectors per beam, DPS-HBF flexibly supports heterogeneous near-field and far-field users without requiring full channel state information. To exploit this capability, a hierarchical scheduling framework combining Bitmask Dynamic Programming, Maximum Weight Matching, andk-best Semi-Greedy User Scheduling is developed to balance optimality, scalability, and computational complexity. The proposed design relies solely on low-overhead SINR feedback, making it suitable for practical large-scale deployments. Simulation results show that DPS-HBF consistently outperforms existing hybrid beamforming and orthogonal multiple access baselines in terms of sum-rate and fairness, achieving up to 30–35% throughput gains over the strongest benchmark under moderate-to-high SNR conditions.

The navigation system of autonomous mobile robots has appeared challenging when using exteroceptive sensors such as cameras, LiDARs, and radars in textureless and structureless environments. This paper presents a robust state estimation system for holonomic mobile robots using intrinsic sensors based on adaptive factor graph optimization in the degradation scenarios. In particular, the neural networks are employed to learn the observation and noise model using only IMU sensor and wheel encoder data. Investigating the learning model for the holonomic mobile robot is discussed with various neural network architectures. We also explore the neural networks that are far more powerful and have cheaper computing power when using the inertial-wheel encoder sensors. Furthermore, we employ an industrial holonomic robot platform equipped with multiple LiDARs, cameras, IMU, and wheel encoders to conduct the experiments and create the ground truth without a bulky motion capture system. The collected datasets are then implemented to train the neural networks. Finally, the experimental evaluation presents that our solution provides better accuracy and real-time performance than other solutions.Note to Practitioners—Autonomous mobile robots need to serve in challenging environments robustly that deny extrinsic sensors such as cameras, LiDARs, and radars. In order to operate in this situation, the navigation system shall rely on the intrinsic sensor as inertial sensor and wheel encoders. Existing conventional methods have combined the intrinsic sensors in the form of the recursive Bayesian filtering technique without adapting these models. Besides, deep learning-based solutions have adopted extensive networks like LSTM or CNN to handle the estimation problem. This work aims to develop a state estimation subsystem of the navigation system for the holonomic mobile robots that utilize the intrinsic sensors in adaptive factor graph optimization. In particular, we present how to join the factor graph efficiently with the learning observation model of IMU and wheel encoder factor. Moreover, the neural networks are introduced to learn the observation model with an IMU and wheel encoder data inputs. We recognize that lightweight neural networks can achieve more power than deep learning techniques using the IMU sensor and wheel encoders. Finally, the neural networks are embedded in a factor graph to handle the smoothing state estimation. The proposed system could operate with high accuracy in real time.

Although a wide variety of high-performance state estimation techniques have been introduced recently, the robustness and extension to actual conditions of the estimation systems have been challenging. This paper presents a robust adaptive state estimation framework based on the Type-2 fuzzy inference system and factor graph optimization for autonomous mobile robots. We use the hybrid solution to connect the advantages of the tightly and loosely coupled technique by providing an inertial sensor and other extrinsic sensors such as LiDARs and cameras. In order to tackle the uncertainty input covariance and sensor failures problems, a learnable observation model is introduced by joining the Type-2 FIS and factor graph optimization. In particular, the use of Type-2 Takagi-Sugeno FIS can learn the uncertainty by using particle swarm optimization before adding the observation model to the factor graph. The proposed design consists of four parts: sensor odometry, up-sampling, FIS based-learning observation model, and factor graph-based smoothing. We evaluate our system by using a mobile robot platform equipped with a sensor setup of multiple stereo cameras, an IMU, and a LiDAR sensor. We imitate the LiDAR odometry in structure environments without needing other bulky motion capture systems to learn the observation model of the visual-inertial estimators. The experimental results are deployed in real-world environments to present the accuracy and robustness of the algorithm.