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.

Reasoning with the Theory of Mind for Pragmatic Semantic Communication

In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed. In particular, semantics is defined as the causal state that encapsulates the fundamental causal relationships and dependencies among different features extracted from data. The proposed framework leverages the emerging concept in machine learning (ML) called theory of mind (ToM). It employs a dynamic two-level (wireless and semantic) feedback mechanism to continuously fine-tune neural network components at the transmitter. Thanks to the ToM, the transmitter mimics the actual mental state of the receiver's reasoning neural network operating semantic interpretation. Then, the estimated mental state at the receiver is dynamically updated thanks to the proposed dynamic two-level feedback mechanism. At the lower level, conventional channel quality metrics are used to optimize the channel encoding process based on the wireless communication channel's quality, ensuring an efficient mapping of semantic representations to a finite constellation. Additionally, a semantic feedback level is introduced, providing information on the receiver's perceived semantic effectiveness with minimal overhead. Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits while maintaining the same semantics, outperforming conventional systems that do not exploit the ToM-based reasoning.

Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing ``AI for wireless'' paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models, their curve-fitting nature, which can limit their ability to reason and adapt, their reliance on large amounts of training data, and the energy inefficiency of large neural networks. In response to these limitations, this article presents a comprehensive, forward-looking vision that addresses these shortcomings by introducing a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. Causal reasoning, founded on causal discovery, causal representation learning, and causal inference, can help build explainable, reasoning-aware, and sustainable wireless networks. Towards fulfilling this vision, we first highlight several wireless networking challenges that can be addressed by causal discovery and representation, including ultra-reliable beamforming for terahertz (THz) systems, near-accurate physical twin modeling for digital twins, training data augmentation, and semantic communication. We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges. Furthermore, we outline potential frameworks that leverage causal inference to achieve the overarching objectives of future-generation networks, including intent management, dynamic adaptability, human-level cognition, reasoning, and the critical element of time sensitivity.

Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach

A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints by utilizing AI techniques such as causal reasoning. In this paper, a novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems. The CSC system is posed as an imitation learning (IL) problem, where the transmitter, with access to optimal network control policies using a DT, teaches the receiver using SC over a bandwidth limited wireless channel how to improve its knowledge to perform optimal control actions. The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios. The CSC decoder at the receiver is designed to extract and estimate semantic information while ensuring high semantic reliability. The receiver control policies, semantic decoder, and causal inference are formulated as a bi-level optimization problem within a variational inference framework. This problem is solved using a novel concept called network state models, inspired from world models in generative AI, that faithfully represents the environment dynamics leading to data generation. Simulation results demonstrate that the proposed CSC system outperforms state-of-the-art SC systems by achieving better semantic reliability and reduced semantic representation.

Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?

Future wireless services, such as the metaverse require high information rate, reliability, and low latency. Multi-user wireless systems can meet such requirements by utilizing the abundant terahertz bandwidth with a massive number of antennas, creating narrow beamforming solutions. However, existing solutions lack proper modeling of channel dynamics, resulting in inaccurate beamforming solutions in high-mobility scenarios. Herein, a dynamic, semantically aware beamforming solution is proposed for the first time, utilizing novel artificial intelligence algorithms in variational causal inference to compute the time-varying dynamics of the causal representation of multi-modal data and the beamforming. Simulations show that the proposed causality-guided approach for Terahertz (THz) beamforming outperforms classical MIMO beamforming techniques.

Sensing aided Channel Estimation in Wideband Millimeter-Wave MIMO Systems

In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases with the number of antennas and the bandwidth. To overcome this, the proposed approach allows the channel estimation at the base station to be aided by the sensing information. The sensing information contains an estimate of scatterers locations in an environment. A simultaneous weighting orthogonal matching pursuit (SWOMP) - sparse Bayesian learning (SBL) algorithm is proposed that efficiently incorporates this sensing information in the communication channel estimation procedure. The proposed framework can cope with scenarios where a) scatterers present in the sensing information are not associated with the communication channel and b) imperfections in the scatterers' location. Simulation results show that the proposed sensing aided channel estimation algorithm can obtain good wideband performance only at the cost of fractional pilot overhead. Finally, the Cramer-Rao Bound (CRB) for the angle estimation and multipath channel gains in the SBL is derived, providing valuable insights into the local identifiability of the proposed algorithms.

Mitigating Intra-Cell Pilot Contamination in Massive MIMO: A Rate Splitting Approach

Massive MIMO (MaMIMO) has become an integral part of the 5G standard, and is envisioned to be further developed in beyond 5G networks. With a massive number of antennas at the base station (BS), MaMIMO is best equipped to cater prominent use cases of B5G networks such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) or combinations thereof. However, one of the critical challenges to this pursuit is the sporadic access behaviour of the massive number of devices in practical networks that inevitably leads to the conspicuous pilot contamination problem. Conventional linearly precoded physical layer strategies employed for downlink transmission in time division duplex (TDD) MaMIMO would incur a noticeable spectral efficiency (SE) loss in the presence of this pilot contamination. In this paper, we aim to integrate a robust multiple access and interference management strategy named rate-splitting multiple access (RSMA) with TDD MaMIMO for downlink transmission and investigate its SE performance. We propose a novel downlink transmission framework of RSMA in TDD MaMIMO, devise a precoder design strategy and power allocation schemes to maximize different network utility functions. Numerical results reveal that RSMA is significantly more robust to pilot contamination and always achieves a SE performance that is equal to or better than the conventional linearly precoded MaMIMO transmission strategy.

Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication

Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the network to consider the semantics (meanings) and effectiveness (at end-user) of the data transmission. This is essential if 6G systems are to communicate reliably with fewer bits while simultaneously providing connectivity to heterogeneous users. In this paper, contrary to state of the art, which lacks explainability of data, the framework of neuro-symbolic artificial intelligence (NeSy AI) is proposed as a pillar for learning causal structure behind the observed data. In particular, the emerging concept of generative flow networks (GFlowNet) is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data. Further, a novel optimization problem for learning the optimal encoding and decoding functions is rigorously formulated with the intent of achieving higher semantic reliability. Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability. These semantic measure functions rely on the proposed definition of semantic content of the knowledge base and this information measure is reflective of the nodes' reasoning capabilities. Simulation results validate the ability to communicate efficiently (with less bits but same semantics) and significantly better compared to a conventional system which does not exploit the reasoning capabilities.