Unleashing the Potential of Beamspace Modulation in Near-Field MIMO: Opportunities and Challenges

The principal distinction in transitioning from far-field multiple-input multiple-output (MIMO) systems to near-field MIMO configurations lies in the notable augmentation of spatial degrees of freedom (DoF). This increase is not static;rather, it dynamically fluctuates in response to user mobility. A critical challenge emerges in effectively leveraging this significantly enhanced and continuously evolving spatial DoF, particularly when constrained by a limited and energy-intensive array of radio frequency (RF) chains. This article presents an exhaustive review of the current methodologies for exploring spatial DoF in MIMO systems, with a particular emphasis on the near-field context. Central to this review is the exploration of beamspace modulation, a technique that ingeniously capitalizes on the increased and dynamic spatial DoFs inherent in near-field MIMO systems. This strategic exploitation is demonstrated to yield significant enhancements in both spectral efficiency and system reliability. Furthermore, the article delves into a detailed analysis of the multifaceted challenges associated with implementing this technology. Through this comprehensive evaluation, the work provides crucial insights into ongoing efforts to navigate these challenges and suggests potential pathways for future research in this rapidly evolving field.

Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach

Graph data, essential in fields like knowledge representation and social networks, often involves large networks with many nodes and edges. Transmitting these graphs can be highly inefficient due to their size and redundancy for specific tasks. This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission. The challenge lies in the irregular structure of graph data, making GIB optimization complex. We address this by deriving a tractable variational upper bound for the objective function. Additionally, we propose the VQ-GIB mechanism, integrating vector quantization (VQ) to convert subgraph representations into a discrete codebook sequence, compatible with existing digital communication systems. Our experiments show that this GIB-based method significantly lowers communication costs while preserving essential task-related information. The approach demonstrates robust performance across various communication channels, suitable for both continuous and discrete systems.

The Application of Machine Learning in Tidal Evolution Simulation of Star-Planet Systems

With the release of a large amount of astronomical data, an increasing number of close-in hot Jupiters have been discovered. Calculating their evolutionary curves using star-planet interaction models presents a challenge. To expedite the generation of evolutionary curves for these close-in hot Jupiter systems, we utilized tidal interaction models established on MESA to create 15,745 samples of star-planet systems and 7,500 samples of stars. Additionally, we employed a neural network (Multi-Layer Perceptron - MLP) to predict the evolutionary curves of the systems, including stellar effective temperature, radius, stellar rotation period, and planetary orbital period. The median relative errors of the predicted evolutionary curves were found to be 0.15%, 0.43%, 2.61%, and 0.57%, respectively. Furthermore, the speed at which we generate evolutionary curves exceeds that of model-generated curves by more than four orders of magnitude. We also extracted features of planetary migration states and utilized lightGBM to classify the samples into 6 categories for prediction. We found that by combining three types that undergo long-term double synchronization into one label, the classifier effectively recognized these features. Apart from systems experiencing long-term double synchronization, the median relative errors of the predicted evolutionary curves were all below 4%. Our work provides an efficient method to save significant computational resources and time with minimal loss in accuracy. This research also lays the foundation for analyzing the evolutionary characteristics of systems under different migration states, aiding in the understanding of the underlying physical mechanisms of such systems. Finally, to a large extent, our approach could replace the calculations of theoretical models.

Learning Multi-Rate Task-Oriented Communications Over Symmetric Discrete Memoryless Channels

This letter introduces a multi-rate task-oriented communication (MR-ToC) framework. This framework dynamically adapts to variations in affordable data rate within the communication pipeline. It conceptualizes communication pipelines as symmetric, discrete, memoryless channels. We employ a progressive learning strategy to train the system, comprising a nested codebook for encoding and task inference. This configuration allows for the adjustment of multiple rate levels in response to evolving channel conditions. The results from our experiments show that this system not only supports edge inference across various coding levels but also excels in adapting to variable communication environments.

Trustworthy Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective

Trustworthy task-oriented semantic communication (ToSC) emerges as an innovative approach in the 6G landscape, characterized by the transmission of only vital information that is directly pertinent to a specific task. While ToSC offers an efficient mode of communication, it concurrently raises concerns regarding privacy, as sophisticated adversaries might possess the capability to reconstruct the original data from the transmitted features. This article provides an in-depth analysis of privacy-preserving strategies specifically designed for ToSC relying on deep neural network-based joint source and channel coding (DeepJSCC). The study encompasses a detailed comparative assessment of trustworthy feature perturbation methods such as differential privacy and encryption, alongside intrinsic security incorporation approaches like adversarial learning to train the JSCC and learning-based vector quantization (LBVQ). This comparative analysis underscores the integration of advanced explainable learning algorithms into communication systems, positing a new benchmark for privacy standards in the forthcoming 6G era.

Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent

Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering, network optimization will witness a revolutionary approach. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs, and the deployment of autonomous agents as a communication bridge to seamlessly connect the machine language based LLMs with human users using natural language. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease. As a preliminary exploration, this research first develops a novel LLM-empowered BSS optimization framework, and heuristically proposes four different potential implementations: the strategies based on Prompt-optimized LLM (PoL), human-in-the-Loop LLM (HiLL), LLM-empowered autonomous BSS agent (LaBa), and Cooperative multiple LLM-based autonomous BSS agents (CLaBa). Through evaluation on real-world data, the experiments demonstrate that prompt-assisted LLMs and LLM-based agents can generate more efficient, cost-effective, and reliable network deployments, noticeably enhancing the efficiency of BSS optimization and reducing trivial manual participation.

Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models

Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to derive semantic representations from visual data, expressed as natural language descriptions. These are further refined using a pre-trained large language model (LLM) for importance quantification and semantic error correction. The subsequent semantic importance-aware communications (SIAC) aim to minimize semantic loss while respecting transmission delay constraints, exemplified through adaptive modulation and coding strategies. At the receiving end, LLM-based semantic error correction is utilized. If visual data recreation is desired, a pre-trained generative artificial intelligence (AI) model can regenerate it using the corrected descriptions. We assess semantic similarities between transmitted and recovered content, demonstrating ULSC's superior ability to convey semantic understanding compared to feature-level semantic communications (FLSC). ULSC's conversion of visual data to natural language facilitates various cognitive tasks, leveraging human knowledge bases. Additionally, this method enhances privacy, as neither original data nor features are directly transmitted.

Unified Integrated Sensing and Communication Signal Design: A Sphere Packing Perspective

The design of communication signal sets is fundamentally a sphere packing problem. It aims to identify a set of M points in an N -dimensional space, with the objective of maximizing the separability of points that represent different bits.In contrast, signals used for sensing targets should ideally be asdeterministic as possible. This paper explores the inherent conflict and trade-off between communication and sensing when these functions are combined within the same signal set. We present a unified approach to signal design in the time, frequency, and space domains for integrated sensing and communication (ISAC), framing it as a modified sphere packing problem. Through adept formula manipulation, this problem is transformed into a large-scale quadratic constrained quadratic programming (QCQP) challenge. We propose an augmented Lagrangian and dual ascent (ALDA) algorithm for iterative problem-solving. The computational complexity of this approach is analyzed and found to be daunting for large, high-dimensional signal set designs. To address this, we introduce a bit-dimension-power splitting (BDPS) method. This method decomposes the large-scale QCQP into a series of smaller-scale problems that can be solved more efficiently and in parallel, significantly reducing the overall computational load. Extensive simulations have been conducted to validate the effectiveness of our proposed signal design methods in the context of ISAC.

Green Holographic MIMO Communications With A Few Transmit Radio Frequency Chains

Holographic multiple-input multiple-output (MIMO) communications are widely recognized as a promising candidate for the next-generation air interface. With holographic MIMO surface, the number of the spatial degrees-of-freedom (DoFs) considerably increases and also significantly varies as the user moves. To fully employ the large and varying number of spatial DoFs, the number of equipped RF chains has to be larger than or equal to the largest number of spatial DoFs. However, this causes much waste as radio frequency (RF) chains (especially the transmit RF chains) are costly and power-hungry. To avoid the heavy burden, this paper investigates green holographic MIMO communications with a few transmit RF chains under an electromagnetic-based communication model. We not only look at the fundamental capacity limits but also propose an effective transmission, namely non-uniform holographic pattern modulation (NUHPM), to achieve the capacity limit in the high signal-to-noise (SNR) regime. The analytical result sheds light on the green evaluation of MIMO communications, which can be realized by increasing the size of the antenna aperture without increasing the number of transmit RF chains. Numerical results are provided to verify our analysis and to show the great performance gain by employing the additional spatial DoFs as modulation resources.

Near-Field Integrated Sensing and Communication: Performance Analysis and Beamforming Design

This paper explores the potential of near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of electromagnetic (EM) near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. We formulate the NFBF design problem as minimizing the weighted summation of radar and the communication beamforming errors under a total power constraint and solve this quadratically constrained quadratic programming (QCQP) problem using the least squares (LS) method. Moreover, the Cram\'er-Rao bound (CRB) for target parameter estimation is derived to verify the performance of NFBF. Furthermore, we also perform power minimization using convex optimization while ensuring the required communication and sensing quality-of-service (QoS). The simulation results show the influence of model mismatch on near-field ISAC and the performance gain of transmit beamforming from the additional distance dimension of near-field.