WiFo: Wireless Foundation Model for Channel Prediction

Channel prediction permits to acquire channel state information (CSI) without signaling overhead. However, almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency (STF) wireless foundation model (WiFo) to address time-frequency channel prediction tasks in a one-for-all manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder (MAE)-based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160K CSI samples for pre-training. Simulations validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art (SOTA) zero-shot generalization performance via comparisons with other full-shot baselines.

Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

Integrated sensing and communication (ISAC) technology plays a crucial role in vehicular networks. However, the communication channel within this context exhibits time-varying characteristics, and potential targets may move rapidly, resulting in double dynamics. These presents significant challenges for real-time ISAC precoding design that have not been thoroughly explored. While optimization-based precoding methods have been extensively studied, they are computationally complex and heavily rely on perfect prior information that is rarely available in situations with double dynamics. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm, where the base station leverages various modalities such as positioning and channel information to adapt to double dynamics, and effectively utilizes environmental information to stretch ISAC performance boundaries through a deep reinforcement learning framework. Additionally, a parameter-shared actor-critic architecture is tailored to expedite training in complex state and action spaces. Extensive experimental validation has demonstrated the multifaceted superiority of our method over existing approaches.

Synesthesia of Machines (SoM)-Enhanced Wideband Multi-User CSI Learning

Light detection and ranging (LiDAR) has been utilized for optimizing wireless communications due to its ability to detect the environment. This paper explores the use of LiDAR in channel estimation for wideband multi-user multiple-input-multiple-output orthogonal frequency division multiplexing systems and introduces a LiDAR-enhanced Channel State Information (CSI) learning network (LE-CLN). By utilizing user positioning information, LE-CLN first calculates user-localized over-complete angular measurements. It then investigates the correlation between LiDAR and CSI, transforming raw LiDAR data into a low-complexity format embedded with signal propagation characteristics. LE-CLN also adapts the use of LiDAR based on channel conditions through attention mechanisms. Thanks to the unique wireless features offered by LiDAR, LE-CLN achieves higher estimation accuracy and spectrum efficiency compared to benchmarks, particularly in latency-sensitive applications where pilot transmissions are expected to be reduced.

Multi-Modal Fusion-Based Multi-Task Semantic Communication System

In recent years, there has been significant progress in semantic communication systems empowered by deep learning techniques. It has greatly improved the efficiency of information transmission. Nevertheless, traditional semantic communication models still face challenges, particularly due to their single-task and single-modal orientation. Many of these models are designed for specific tasks, which may result in limitations when applied to multi-task communication systems. Moreover, these models often overlook the correlations among different modal data in multi-modal tasks. It leads to an incomplete understanding of complex information, causing increased communication overhead and diminished performance. To address these problems, we propose a multi-modal fusion-based multi-task semantic communication (MFMSC) framework. In contrast to traditional semantic communication approaches, MFMSC can effectively handle various tasks across multiple modalities. Furthermore, we design a fusion module based on Bidirectional Encoder Representations from Transformers (BERT) for multi-modal semantic information fusion. By leveraging the powerful semantic understanding capabilities and self-attention mechanism of BERT, we achieve effective fusion of semantic information from different modalities. We compare our model with multiple benchmarks. Simulation results show that MFMSC outperforms these models in terms of both performance and communication overhead.

LLM4CP: Adapting Large Language Models for Channel Prediction

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction method (LLM4CP) to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM, preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves SOTA prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

Beam Pattern Modulation Embedded Hybrid Transceiver Optimization for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid architecture in mmWave systems compromises multiplexing gain due to the constraints of limited radio frequency chains. Moreover, additional sensing functionalities exacerbate the impairment of spectrum efficiency (SE). In this paper, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC) transceiver design, which spares one RF chain for sensing and the others for communication. To compensate for the reduced SE, index modulation across communication beams is applied. We formulate an optimization problem aimed at minimizing the mean squared error (MSE) of the sensing beampattern, subject to a symbol MSE constraint. This problem is then solved by sequentially optimizing the analog and digital parts. Both the multi-aperture structure (MAS) and the multi-beam structure (MBS) are considered for the design of the analog part. We conduct theoretical analysis on the asymptotic pairwise error probability (APEP) and the Cram\'er-Rao bound (CRB) of direction of arrival (DoA) estimation. Numerical simulations validate the overall enhanced ISAC performance over existing alternatives.

Superposed IM-OFDM : An Enhanced OFDM for Integrated Sensing and Communications

Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design. While orthogonal frequency division multiplexing (OFDM) has been a popular basic waveform, its primitive version falls short in sensing due to the inherent unregulated auto-correlation properties. Furthermore, the sensitivity to Doppler shift hinders its broader applications in dynamic scenarios. To address these issues, we propose a superposed index-modulated OFDM (S-IM-OFDM). The proposed scheme improves the sensing performance without excess power consumption by translating the energy efficiency of IM-OFDM onto sensing-oriented signals over OFDM. Also, it maintains excellent communication performance in time-varying channels by leveraging the sensed parameters to compensate for Doppler. Compared to conventional OFDM, the proposed S-IM-OFDM waveform exhibits better sensing capabilities and wider applicability in dynamic scenarios. Both theoretical analyses and simulations corroborate its dual benefits.

A Survey on Federated Learning in Intelligent Transportation Systems

The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and vehicular edge computing. However, most current ITS applications rely on a centralized training approach where users upload source data to a cloud server with high computing power for management and centralized training. This approach has limitations such as poor real-time performance, data silos, and difficulty in guaranteeing data privacy. To address these limitations, federated learning (FL) has been proposed as a promising solution. In this paper, we present a comprehensive review of the application of FL in ITS, with a particular focus on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. For each scenario, we provide an in-depth analysis of its key characteristics, current challenges, and specific manners in which FL is leveraged. Moreover, we discuss the benefits that FL can offer as a potential solution to the limitations of the centralized training approach currently used in ITS applications.

Integrated Sensing and Communications towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require narrow beam alignment, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the ViWi dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach.

Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs

Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation towards the interpretable machine learning, node importance in social network, attribution models, etc. However, its heavy computational burden has been long recognized but rarely investigated. Specifically, in a $d$-player coalition game, calculating a Shapley value requires the evaluation of $d!$ or $2^d$ marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence it becomes infeasible to calculate the Shapley value when $d$ is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the C. elegans nervous system and the 9/11 terrorist network.