NTT Docomo

Metasurfaces have emerged as transformative electromagnetic structures for wireless communications, enabling the real-time control over wave propagation, yielding potential for improved data rates, privacy, energy efficiency and even precise environmental sensing. This tutorial offers a perspective on controlling metasurfaces by treating them as components of a larger networked system. Towards this end, we first review the physical principles of metasurfaces and their various applications, followed by an exploration of manufacturing approaches for creating these structures. Then, aligning with standard network layer concepts, we describe the modeling of metasurfaces as wave routers, enabling us to describe systems of metasurfaces using graph theory. This approach enables the development of a performance objective framework for optimizing these systems, while classes of heuristic and path-finding-driven algorithms are discussed as practical solvers. The paper also examines the integration of metasurfaces with communication systems, by presenting their overall workflow, discussing its relation to ongoing standardization efforts, as well as defining a context for their integration to network simulators, using Omnet++ as a driving example. Finally, the paper explores future directions for research in this field, identifying graph-theoretic, standardization and integration challenges, relating to several networking disciplines including AI-driven applications.

Subterahertz (sub-THz) frequencies (100-300 GHz) are expected to play a key role in beyond-5G and sixth-generation (6G) mobile networks. However, their quasi-optical propagation characteristics require new channel models beyond sub-100 GHz extrapolations. This article presents an extensive double-directional (D-D) channel measurement campaign conducted in an outdoor street-canyon environment at 154 and 300 GHz under both line-of-sight (LoS) and non-LoS (NLoS) conditions using an in-house-developed multitone frequency-domain channel sounder. Based on these measurements, clustering with merged datasets across the two frequencies enables comparative analyses that identify both common and distinct multipath clusters, as well as the frequency dependence of cluster-level characteristics. A quasi-deterministic (Q-D) channel model is then proposed, combining deterministic components, such as LoS and single-bounce (SB) reflections from side walls, with random components. Large-scale parameters (LSPs) [path loss (PL), delay spread (DS), angular spread (AS), and Rician $K$ factor (KF)] are also evaluated. These results provide valuable insights into sub-THz propagation in urban street canyons and contribute toward the development of accurate channel models for future 6G systems.

In this paper, we explore a novel joint uplink and downlink framework utilizing a pinching antenna system (PASS). We consider two waveguides, one dedicated to transmission and one to reception, and both of them are connected to a base station (BS). Each type of waveguide consists of several pinching antennas (PAs) in some preconfigured positions. In this framework, we assume the BS can serve downlink and uplink user equipments (UEs) at the same time using the same spectrum resources through the presented PASS. In this aspect, we formulate a sum rate optimization problem that jointly optimizes the antenna activation factor, the BS transmit power, and the UE's transmit power, subject to power budget constraints for the BS and the UEs, as well as minimum rate requirements for the UEs. The formulated problem is highly non-convex and difficult to solve directly. Hence, we divide the main problem into two sub-problems: the antenna activation sub-problem and the power allocation sub-problem. Then, we solve the antenna activation problem utilizing a distance and spatial correlation-based algorithm. Meanwhile, the resource allocation problem is solved using a successive convex approximation (SCA)-based algorithm. Numerical results show that our proposed framework can achieve around 60-90% performance gains over its time division duplex (TDD) where the uplink and downlink transmissions are served in different orthogonal time slots.

6G will provide a new opportunity to integrate Artificial Intelligence (AI) and Machine Learning (ML) into wireless communication. The current 5G Core (5GC) architecture possesses limitations that restrict its ability to effectively deploy AI functions, including the lack of a standardized, unified access layer for network data, limited service exposure models that cannot fully support AI feedback loops, and tight coupling between logic and signaling that inhibits the insertion of AI-based decision-making components. This paper explores approaches to address the 5GC shortcomings in the future 6G Core (6GC) network. Recommendations for architectural evolution and standardization are discussed to facilitate scalable, efficient, and adaptable AI-enabled 6G networks.

Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased consumption. Quantifying the causal effect of these thresholds on customers is crucial for effective marketing strategy design. Although regression discontinuity design is a standard method for such causal inference tasks, its assumptions can be violated when customers, aware of the thresholds, strategically manipulate their spending to qualify for the rewards. To address this issue, we propose a novel framework for estimating the causal effect under threshold manipulation. The main idea is to model the observed spending distribution as a mixture of two distributions: one representing customers strategically affected by the threshold, and the other representing those unaffected. To fit the mixture model, we adopt a two-step Bayesian approach consisting of modeling non-bunching customers and fitting a mixture model to a sample around the threshold. We show posterior contraction of the resulting posterior distribution of the causal effect under large samples. Furthermore, we extend this framework to a hierarchical Bayesian setting to estimate heterogeneous causal effects across customer subgroups, allowing for stable inference even with small subgroup sample sizes. We demonstrate the effectiveness of our proposed methods through simulation studies and illustrate their practical implications using a real-world marketing dataset.