Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI$^{2}$ in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.

Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.

Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement

Task-oriented object grasping and rearrangement are critical skills for robots to accomplish different real-world manipulation tasks. However, they remain challenging due to partial observations of the objects and shape variations in categorical objects. In this paper, we propose the Multi-feature Implicit Model (MIMO), a novel object representation that encodes multiple spatial features between a point and an object in an implicit neural field. Training such a model on multiple features ensures that it embeds the object shapes consistently in different aspects, thus improving its performance in object shape reconstruction from partial observation, shape similarity measure, and modeling spatial relations between objects. Based on MIMO, we propose a framework to learn task-oriented object grasping and rearrangement from single or multiple human demonstration videos. The evaluations in simulation show that our approach outperforms the state-of-the-art methods for multi- and single-view observations. Real-world experiments demonstrate the efficacy of our approach in one- and few-shot imitation learning of manipulation tasks.