This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five other sensor modalities, namely LiDAR, RGB and depth camera, inertial measurement unit (IMU) and radar, all sampled at 100 Hz. It contains approximately 160,000 frames collected across four virtual towns, sixteen communication scenarios, and three weather conditions. This paper provides a comprehensive overview of the dataset, outlining its key features, overall framework, and technical implementation details. In addition, it explores potential research applications concerning communication and collaborative perception, exemplified by beam prediction using a multimodal large language model. The dataset is open in https://le-liang.github.io/mmw/.

Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity. However, existing strategies typically rely on block fading assumptions, which overlook rapid channel fluctuations within each round of FL gradient uploading, leading to a degradation in FL training performance. Therefore, this paper proposes a small-scale-fading-aware resource allocation strategy using a multi-agent reinforcement learning (MARL) framework. Specifically, we establish a one-step convergence bound of the FL algorithm and formulate the resource allocation problem as a decentralized partially observable Markov decision process (Dec-POMDP), which is subsequently solved using the QMIX algorithm. In our framework, each client serves as an agent that dynamically determines spectrum and power allocations within each coherence time slot, based on local observations and a reward derived from the convergence analysis. The MARL setting reduces the dimensionality of the action space and facilitates decentralized decision-making, enhancing the scalability and practicality of the solution. Experimental results demonstrate that our QMIX-based resource allocation strategy significantly outperforms baseline methods across various degrees of statistical heterogeneity. Additionally, ablation studies validate the critical importance of incorporating small-scale fading dynamics, highlighting its role in optimizing FL performance.

Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.

High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforcement learning (HMARL). We decompose the CSR process into two phases, i.e., a polling phase and a decision phase, and introduce the HMARL algorithm to enable efficient CSR. To enhance training efficiency, the proposed HMARL algorithm employs a hierarchical structure, where station selection and power control are determined by a high- and low-level policy network, respectively. Simulation results demonstrate that this approach consistently outperforms baseline methods in terms of throughput, mean delay and delay jitter across various network topologies. Moreover, the algorithm exhibits robust performance when coexisting with legacy APs. Additional experiments in a representative topology further reveal that the carefully designed reward function not only maximizes the overall network throughput, but also improves fairness in transmission opportunities for APs in high-interference regions.

This paper investigates the downlink channel estimation problem for frequency division duplex (FDD) multi-user massive multiple-input multiple-output (MIMO) systems. We model this problem within the sparse Bayesian learning (SBL) framework, where all unknowns are treated as random variables. Due to limited scattering at the base station, the channel exhibits sparsity in the angular domain. By introducing Gaussian mixture priors to characterize the user equipment internal sparsity and partially shared sparsity, we develop a Markov chain Monte Carlo (MCMC) method to implement Bayesian inference and accurately estimate all random variables in the model, including the channel matrix. Experimental results demonstrate that the MCMC-based SBL channel estimation algorithm outperforms existing approaches by over 5 dB in multi-user scenarios while reducing pilot overhead.

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.

Stand-alone perception systems in autonomous driving suffer from limited sensing ranges and occlusions at extended distances, potentially resulting in catastrophic outcomes. To address this issue, collaborative perception is envisioned to improve perceptual accuracy by using vehicle-to-everything (V2X) communication to enable collaboration among connected and autonomous vehicles and roadside units. However, due to limited communication resources, it is impractical for all units to transmit sensing data such as point clouds or high-definition video. As a result, it is essential to optimize the scheduling of communication links to ensure efficient spectrum utilization for the exchange of perceptual data. In this work, we propose a deep reinforcement learning-based V2X user scheduling algorithm for collaborative perception. Given the challenges in acquiring perceptual labels, we reformulate the conventional label-dependent objective into a label-free goal, based on characteristics of 3D object detection. Incorporating both channel state information (CSI) and semantic information, we develop a double deep Q-Network (DDQN)-based user scheduling framework for collaborative perception, named SchedCP. Simulation results verify the effectiveness and robustness of SchedCP compared with traditional V2X scheduling methods. Finally, we present a case study to illustrate how our proposed algorithm adaptively modifies the scheduling decisions by taking both instantaneous CSI and perceptual semantics into account.

In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance.

As an efficient data fusion method, over-the-air computation integrates computation and communication by exploiting the superposition property of multiple access channels. In this paper, a framework on deep learning enabled over-the-air computation is proposed, where both the pre-processing and post-processing functions are represented by deep neural networks (DNNs). In this way, the over-the-air computation can approximate any function via learning through the data. The deep over-the-air framework is useful to a variety of machine learning applications on the Internet-of-Things (IoT). The experiments on distribution regression and anomaly detection have shown the effectiveness of the proposed method.

Recently, deep learning based end-to-end communication systems have been developed, where both the transmitter and the receiver are represented as deep neural networks (DNN) and an end-to-end loss is optimized directly. In this paper, we address the effects of the more general wireless channels to the end-to-end framework. We formulate this problem as training a deep auto-encoder system with an adversarial convolutional layer and propose a training procedure with mini-batches of input samples and channels. Instead of using pilots to explicitly estimate the unknown channel, the auto-encoder learns to address the channel effects without any pilot information. In particular, the receiver contains two modules, designed for channel information extraction and data recovery, respectively. The features obtained from the channel information extraction module are combined with received signals by a bilinear production and then processed by the data recovery module to reconstruct the original input data. The experimental results show a performance improvement compared with the traditional methods in commonly seen wireless channels, including frequency-selective channels and multi-input multi-output (MIMO) channels.

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.

In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), where DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e., channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

As wireless networks evolve toward high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this paper, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.

In this article, we develop a decentralized resource allocation mechanism for vehicle-to- vehicle (V2V) communications based on deep reinforcement learning. Each V2V link is supported by an autonomous "agent", which makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Hence, the proposed method is decentralized, with minimum transmission overhead. From the simulation results, each agent can effectively learn how to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications.

In this article, we exploit deep reinforcement learning for joint resource allocation and scheduling in vehicle-to-vehicle (V2V) broadcast communications. Each vehicle, considered as an autonomous agent, makes its decisions to find the messages and spectrum for transmission based on its local observations without requiring or having to wait for global information. From the simulation results, each vehicle can effectively learn how to ensure the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) links.

Historically, most of the channel encoding and decoding algorithms have been designed to deal with and evaluated under the additive white Gaussian noise (AWGN) channel. However, in the reality, the channel is far more complicated than the AWGN assumption. Traditionally, qualizers are employed to combat the channel effects and frequency-selective fading of wireless channels. In this article, we take the advantage of deep learning approaches to handle the various channel distortions, by proposing an end-to-end approach. To train the model efficiently, the training data is obtained by simulation where the encoding process and the channel effects are viewed as a complete black box. This method can also be applied to time- varying channels for simultaneously channel estimation and symbol detection. Simulation results show that the deep learning based decoders have the ability to learn the complicated encoder function and address various channel effects. Furthermore, the deep learning based method provides an end-to-end approach, leading to a better performance in the channels with various distortions. This article represents the first step for a universal framework of information recovering from a large range of channel codes and channels.