Rate Maximization for Downlink Pinching-Antenna Systems

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 single-antenna user. Our goal is to maximize the downlink transmission rate by optimizing the locations of the pinching antennas. However, these locations affect both the path losses and the phase shifts of the user's effective channel gain, making the problem challenging to solve. To address this challenge and solve the problem in a low complexity manner, a relaxed optimization problem is developed that minimizes the impact of path loss while ensuring that the received signals at the user are constructive. This approach leads to a two-stage algorithm: in the first stage, the locations of the pinching antennas are optimized to minimize the large-scale path loss; in the second stage, the antenna locations are refined to maximize the received signal strength. Simulation results show that pinching-antenna systems significantly outperform conventional fixed-location antenna systems, and the proposed algorithm achieves nearly the same performance as the highly complex exhaustive search-based benchmark.

Beam Training for Pinching-Antenna Systems (PASS)

This article investigates the beam training design problems for pinching-antenna systems (PASS), where single-waveguide-single-user (SWSU), single-waveguide-multi-user (SWMU) and multi-waveguide-multi-user (MWMU) scenarios are considered. For SWSU-PASS, we design a scalable codebook, based on which we propose a three-stage beam training (3SBT) scheme. Specifically, 1) firstly, the 3SBT scheme utilizes one activated pinching antenna to obtain a coarse one-dimensional location at the first stage; 2) secondly, it achieves further phase matching with an increased number of activated antennas at the second stage; 3) finally, it realizes precise beam alignment through an exhaustive search at the third stage. For SWMU-PASS, based on the scalable codebook design, we propose an improved 3SBT scheme to support non-orthogonal multiple access (NOMA) transmission. For MWMU-PASS, we first present a generalized expression of the received signal based on the partially-connected hybrid beamforming structure. Furthermore, we introduce an increased-dimensional scalable codebook design, based on which an increased-dimensional 3SBT scheme is proposed. Numerical results reveal that: i) the proposed beam training scheme can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining reasonable rate performance; ii) compared to fixed-location pinching antennas and conventional array antennas, the proposed dynamic pinching antennas yield better flexibility and improved performance.

NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA

This paper proposes a graph neural network (GNN) enabled power allocation scheme for non-orthogonal multiple access (NOMA) networks. In particular, a downlink scenario with one base station serving multiple users over several subchannels is considered, where the number of subchannels is less than the number of users, and thus, some users have to share a subchannel via NOMA. Our goal is to maximize the system energy efficiency subject to the rate requirement of each user and the overall budget. We propose a deep learning based approach termed NOMA net (NOMANet) to address the considered problem. Particularly, NOMANet is GNN-based, which maps channel state information to the desired power allocation scheme for all subchannels. The multi-head attention and the residual/dense connection are adopted to enhance the feature extraction. The output of NOMANet is guaranteed to be feasible via the customized activation function and the penalty method. Numerical results show that NOMANet trained unsupervised achieves performance close to that of the successive convex approximation method but with a faster inference speed by about $700$ times. Besides, NOMANet is featured by its scalability to both users and subchannels.

Graph Neural Network Enabled Pinching Antennas

The pinching-antenna system is a novel flexible-antenna technology, which has the capabilities not only to combat large-scale path loss, but also to reconfigure the antenna array in a flexible manner. The key idea of pinching antennas is to apply small dielectric particles on a waveguide of arbitrary length, so that they can be positioned close to users to avoid significant large-scale path loss. This paper investigates the graph neural network (GNN) enabled transmit design for the joint optimization of antenna placement and power allocation in pinching-antenna systems. We formulate the downlink communication system equipped with pinching antennas as a bipartite graph, and propose a graph attention network (GAT) based model, termed bipartite GAT (BGAT), to solve an energy efficiency (EE) maximization problem. With the tailored readout processes, the BGAT guarantees a feasible solution, which also facilitates the unsupervised training. Numerical results demonstrate the effectiveness of pinching antennas in enhancing the system EE as well as the proposed BGAT in terms of optimality, scalability and computational efficiency.

ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated.

Downlink Beamforming with Pinching-Antenna Assisted MIMO Systems

Pinching antennas have been recently proposed as a promising flexible-antenna technology, which can be implemented by attaching low-cost pinching elements to dielectric waveguides. This work explores the potential of employing pinching antenna systems (PASs) for downlink transmission in a multiuser MIMO setting. We consider the problem of hybrid beamforming, where the digital precoder at the access point and the activated locations of the pinching elements are jointly optimized to maximize the achievable weighted sum-rate. Invoking fractional programming, a novel low-complexity algorithm is developed to iteratively update the precoding matrix and the locations of the pinching antennas. We validate the proposed scheme through extensive numerical experiments. Our investigations demonstrate that using PAS the system throughput can be significantly boosted as compared with the conventional fixed-location antenna systems, enlightening the potential of PAS as an enabling candidate for next-generation wireless networks.

Pinching Antenna Systems (PASS): Architecture Designs, Opportunities, and Outlook

This article proposes a novel design for the Pinching Antenna Systems (PASS) and advocates simple yet efficient wireless communications over the `last meter'. First, the potential benefits of PASS are discussed by reviewing an existing prototype. Then, the fundamentals of PASS are introduced, including physical principles, signal models, and communication designs. In contrast to existing multi-antenna systems, PASS brings a novel concept termed \emph{Pinching Beamforming}, which is achieved by dynamically adjusting the positions of PAs. Based on this concept, a couple of practical transmission architectures are proposed for employing PASS, namely non-multiplexing and multiplexing architectures. More particularly, 1) The non-multiplexing architecture is featured by simple baseband signal processing and relies only on the pinching beamforming; while 2) the multiplexing architecture provides enhanced signal manipulation capabilities with joint baseband and pinching beamforming, which is further divided into sub-connected, fully-connected, and phase-shifter-based fully-connected schemes. Furthermore, several emerging scenarios are put forward for integrating PASS into future wireless networks. As a further advance, by demonstrating a few numerical case studies, the significant performance gain of PASS is revealed compared to conventional multi-antenna systems. Finally, several research opportunities and open problems of PASS are highlighted.

Pinching Antennas: Principles, Applications and Challenges

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework

In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) specifically for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. By utilizing advanced language models for entity extraction and question generation, rigorous data curation processes are employed to maintain high quality and relevance. Additionally, we introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data with 2.24\% and 1.31\% performance boost for different models compared to baselines, respectively. To demonstrate the effectiveness of the fine-tuned models with the proposed methodologies on practical tasks, we also consider different tasks, including summarizing optimization problems from technical papers and solving the mathematical problems related to non-orthogonal multiple access (NOMA), which are generated by using the proposed multi-agent framework. Simulation results show significant performance gain in summarization tasks with 20.9\% in the ROUGE-L metrics. We also study the scaling laws of fine-tuning LLMs and the challenges LLMs face in the field of wireless communications, offering insights into their adaptation to wireless communication tasks. This dataset and fine-tuning methodology aim to enhance the training and evaluation of LLMs, contributing to advancements in LLMs for wireless communication research and applications.

Array Gain for Pinching-Antenna Systems (PASS)

Pinching antennas is a novel flexible-antenna technology, which can be realized by employing small dielectric particles on a waveguide. The aim of this letter is to characterize the array gain achieved by pinching-antenna systems (PASS). A closed-form upper bound on the array gain is derived by fixing the inter-antenna spacing. Asymptotic analyses of this bound are conducted by considering an infinitely large number of antennas, demonstrating the existence of an optimal number of antennas that maximizes the array gain. The relationship between the array gain and inter-antenna spacing is further explored by incorporating the effect of mutual coupling. It is proven that there also exists an optimal inter-antenna spacing that maximizes the array gain. Numerical results demonstrate that by optimizing the number of antennas and inter-antenna spacing, PASS can achieve a significantly larger array gain than conventional fixed-location antenna systems.