Related papers: Adaptive Pinching Antenna Optimization via Meta-Le…
There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these…
In this paper, we investigate the resource allocation for multi-dielectric waveguide-assisted broadcast systems, where each waveguide employs multiple pinching antennas (PAs), aiming to maximize the minimum achievable rate among multiple…
Pinching Antennas (PAs) represent a revolutionary flexible antenna technology that leverages dielectric waveguides and electromagnetic coupling to mitigate large-scale path loss. This letter is the first to explore channel estimation for…
Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic…
This paper investigates a novel downlink symbiotic radio framework enabled by the pinching antenna system (PASS), designed to enhance both primary and secondary transmissions through reconfigurable antenna positioning. This…
Next-generation networks require intelligent and robust channel conditions to support ultra-high data rates, seamless connectivity, and large-scale device deployments in dynamic environments. While flexible antenna technologies such as…
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and…
Pinching antenna systems (PASS) have emerged as a technology that enables the large-scale movement of antenna elements, offering significant potential for performance gains in next-generation wireless networks. This paper investigates the…
Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas…
There has been a growing interest in developing data-driven and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these…
A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be…
In this paper,we investigate a novel wireless powered mobile edge computing (MEC) system assisted by pinching antennas (PAs), where devices first harvest energy from a base station and then offload computation-intensive tasks to an MEC…
A critical goal of adaptive control is enabling robots to rapidly adapt in dynamic environments. Recent studies have developed a meta-learning-based adaptive control scheme, which uses meta-learning to extract nonlinear features…
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the…
This paper proposes a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction (NTP) models. The primary objective is to accumulate and leverage the acquired hyper-parameter optimization…
Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks. The model initialized by provided weight can be fine-tuned to…
Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to new tasks, as well as (ii) having a shared representation…
In this letter, we consider a new type of flexible-antenna system, termed pinching-antenna, where multiple low-cost pinching antennas, realized by activating small dielectric particles on a dielectric waveguide, are jointly used to serve a…
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, many systems have similar and well-understood dynamics, which…
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of…