Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades.

Multiple Active STAR-RIS-Assisted Secure Integrated Sensing and Communication via Cooperative Beamforming

This paper explores an integrated sensing and communication (ISAC) network empowered by multiple active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). A base station (BS) furnishes downlink communication to multiple users while concurrently interrogating a sensing target. We jointly optimize the BS transmit beamformer and the reflection/transmission coefficients of every active STAR-RIS in order to maximize the aggregate communication sum-rate, subject to (i) a stringent sensing signal-to-interference-plus-noise ratio (SINR) requirement, (ii) an upper bound on the leakage of confidential information, and (iii) individual hardware and total power constraints at both the BS and the STAR-RISs. The resulting highly non-convex program is tackled with an efficient alternating optimization (AO) framework. First, the original formulation is reformulated into an equivalent yet more tractable representation and partitioned into subproblems. The BS beamformer is updated in closed form via the Karush-Kuhn-Tucker (KKT) conditions, whereas the STAR-RIS reflection and transmission vectors are refined through successive convex approximation (SCA), yielding a semidefinite program that is then solved via semidefinite relaxation. Comprehensive simulations demonstrate that the proposed algorithm delivers substantial sum-rate gains over passive-RIS and single STAR-RIS baselines, all the while rigorously meeting the prescribed sensing and security constraints.

Secure Multi-Hop Relaying in Large-Scale Space-Air-Ground-Sea Integrated Networks

As a key enabler of borderless and ubiquitous connectivity, space-air-ground-sea integrated networks (SAGSINs) are expected to be a cornerstone of 6G wireless communications. However, the multi-tiered and global-scale nature of SAGSINs also amplifies the security vulnerabilities, particularly due to the hidden, passive eavesdroppers distributed throughout the network. In this paper, we introduce a joint optimization framework for multi-hop relaying in SAGSINs that maximizes the minimum user throughput while ensuring a minimum strictly positive secure connection (SPSC) probability. We first derive a closed-form expression for the SPSC probability and incorporate this into a cross-layer optimization framework that jointly optimizes radio resources and relay routes. Specifically, we propose an $\mathcal{O}(1)$ optimal frequency allocation and power splitting strategy-dividing power levels of data transmission and cooperative jamming. We then introduce a Monte-Carlo relay routing algorithm that closely approaches the performance of the numerical upper-bound method. We validate our framework on testbeds built with real-world dataset. All source code and data for reproducing the numerical experiments will be open-sourced.

Robust Transmission of Punctured Text with Large Language Model-based Recovery

With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust performance regardless of dataset or tasks. In this correspondence, we propose a novel text transmission model that selects and transmits only a few characters and recovers the missing characters at the receiver using a large language model (LLM). Additionally, we propose a novel importance character extractor (ICE), which selects transmitted characters to enhance LLM recovery performance. Simulations demonstrate that the proposed filter selection by ICE outperforms random filter selection, which selects transmitted characters randomly. Moreover, the proposed model exhibits robust performance across different datasets and tasks and outperforms traditional bit-based communication in low signal-to-noise ratio conditions.

Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

Deep learning (DL) has made notable progress in addressing complex radio access network control challenges that conventional analytic methods have struggled to solve. However, DL has shown limitations in solving constrained NP-hard problems often encountered in network optimization, such as those involving quality of service (QoS) or discrete variables like user indices. Current solutions rely on domain-specific architectures or heuristic techniques, and a general DL approach for constrained optimization remains undeveloped. Moreover, even minor changes in communication objectives demand time-consuming retraining, limiting their adaptability to dynamic environments where task objectives, constraints, environmental factors, and communication scenarios frequently change. To address these challenges, we propose a large language model for resource allocation optimizer (LLM-RAO), a novel approach that harnesses the capabilities of LLMs to address the complex resource allocation problem while adhering to QoS constraints. By employing a prompt-based tuning strategy to flexibly convey ever-changing task descriptions and requirements to the LLM, LLM-RAO demonstrates robust performance and seamless adaptability in dynamic environments without requiring extensive retraining. Simulation results reveal that LLM-RAO achieves up to a 40% performance enhancement compared to conventional DL methods and up to an $80$\% improvement over analytical approaches. Moreover, in scenarios with fluctuating communication objectives, LLM-RAO attains up to 2.9 times the performance of traditional DL-based networks.