Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to a direct application of existing language models that were designed for natural language processing (NLP) applications. To address this challenge and create wireless-centric foundation models, this paper presents a comprehensive vision on how to design universal foundation models that are tailored towards the deployment of artificial intelligence (AI)-native networks. Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI. In essence, these properties enable the proposed LMM framework to build universal capabilities that cater to various cross-layer networking tasks and alignment of intents across different domains. Preliminary results from experimental evaluation demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, the enhanced rationale exhibited in the responses to mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs. We then conclude with a set of recommendations that ignite the path towards LMM-empowered AI-native systems.

STAN: Synthetic Network Traffic Generation using Autoregressive Neural Models

Deep learning models have achieved great success in recent years. However, large amounts of data are typically required to train such models. While some types of data, such as images, videos, and text, are easier to find, data in certain domains is difficult to obtain. For instance, cybersecurity applications routinely use network traffic data which organizations are reluctant to share, even internally, due to privacy reasons. An alternative is to use synthetically generated data; however, most existing data generating methods lack the ability to capture complex dependency structures that are usually prevalent in real data by assuming independence either temporally or between attributes. This paper presents our approach called STAN, Synthetic Network Traffic Generation using Autoregressive Neural models, to generate realistic synthetic network traffic data. Our novel autoregressive neural architecture captures both temporal dependence and dependence between attributes at any given time. It integrates convolutional neural layers (CNN) with mixture density layers (MDN) and softmax layers to model both continuous and discrete variables. We evaluate performance of STAN by training it on both a simulated dataset and a real network traffic data set. Multiple metrics are used to compare the generated data with real data and with data generated via several baseline methods. Finally, to answer the question -- can real network traffic data be substituted with synthetic data to train models of comparable accuracy -- we consider two commonly used models for anomaly detection in such data, and compare F1/MSE measures of models trained on real data and those on increasing proportions of generated data. The results show only a small decline in accuracy of models trained solely on synthetic data.