Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks

AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems.

Towards Realistic Statistical Channel Models For Positioning: Evaluating the Impact of Early Clusters

Physical effects such as reflection, refraction, and diffraction cause a radio signal to arrive from a transmitter to a receiver in multiple replicas that have different amplitude and rotation. Bandwidth-limited signals, such as positioning reference signals, have a limited time resolution. In reality, the signal is often reflected in the close vicinity of a transmitter and receiver, which causes the displacement of the observed peak from the true peak expected according to the line of sight (LOS) geometry between the transmitter and receiver. In this paper, we show that the existing channel model specified for performance evaluation within 3GPP fails to model the above phenomena. As a result, the simulation results deviate significantly from the measured values. Based on our measurement and simulation results, we propose a model for incorporating the signal reflection by obstacles in the vicinity of transmitter or receiver, so that the outcome of the model corresponds to the measurement made in such scenario.

Complementary Semi-Deterministic Clusters for Realistic Statistical Channel Models for Positioning

Positioning benefits from channel models that capture geometric effects and, in particular, from the signal properties of the first arriving path and the spatial consistency of the propagation condition of multiple links. The models that capture the physical effects observed in a realistic deployment scenario are essential for assessing the potential benefits of enhancements in positioning methods. Channel models based on ray-tracing simulations and statistical channel models, which are current state-of-the-art methods employed to evaluate performance of positioning in 3GPP systems, do not fully capture important aspects applicable to positioning. Hence, we propose an extension of existing statistical channel models with semi-deterministic clusters (SDCs). SDCs allow channels to be simulated using three types of clusters: fixed-, specular-, and random-clusters. Our results show that the proposed model aligns with measurements obtained in a real deployment scenario. Thus, our channel models can be used to develop advanced positioning solutions based on machine learning, which enable positioning with centimeter level accuracy in NLOS and multipath scenarios.