In this work, we propose a novel integrated sensing and communication (ISAC) framework for connected and autonomous vehicles (CAVs), which incorporates digital semantic communication (SemCom) to achieve both reliable communication and accurate sensing. Within this framework, the transmitting vehicle extracts semantic symbols from the source data and transmits them over orthogonal frequency division multiplexing (OFDM) sub-carriers, while simultaneously utilizing echo signals for radar-based environmental sensing. To achieve reliable SemCom, the transmitter must jointly optimize the quantization bitwidth for semantic symbols, the modulation order, the power allocation across semantic symbol dimensions, and the transmit beamforming strategy. These optimizations must also consider sensing performance, leading to a tradeoff between radar sensing and task-oriented SemCom. To address this joint optimization problem, we decompose it into three subproblems and develop corresponding solutions: 1) a hierarchical constrained proximal policy optimization (H-CPPO) algorithm to determine the quantization bitwidth, modulation order, and power allocation under frequency-flat channels, 2) a joint beamforming strategy to optimize the dual-function radar-SemCom transmit beamforming vector, and 3) a semantic importance-based signal-to-noise ratio (SNR) matching strategy that effectively adapts the optimal power allocation obtained under frequency-flat conditions to fading channels with random gains. Simulation results on a road image segmentation task show that, the proposed SemCom scheme achieves near-optimal segmentation accuracy while reducing radar beamforming error by up to 82% compared to the conventional digital system using the same quadrature phase shift keying (QPSK) modulation.

This paper proposes a novel integrated sensing and communication (ISAC) framework incorporating digital semantic communication (SemCom) to resolve the tradeoff between sensing and communication performance. In particular, to accomplish task-oriented semCom, the base station (BS) extracts semantic symbols and transmits each dimension over different orthogonal frequency division multiplex (OFDM) subcarriers. To achieve sensing objective, the BS broadcasts OFDM signals and receives echoes via a uniform linear array (ULA) to estimate echo channel state information (CSI) and obtain target parameters. Given the varying task-related importance of each dimension, the framework allocates quantization bits, modulation order, and transmission power accordingly to meet SemCom requirements. On the other hand, sensing performance is evaluated using the Cramér-Rao Bound (CRB) of echo CSI, with transmission power allocation optimized to enhance sensing. The problem is formulated to minimize the sensing CRB while satisfying SemCom task loss, total resources, and transmission efficiency constraints. To solve this problem, we introduce a Hybrid Action Space Proximal Policy Optimization (H-PPO) algorithm, which can simultaneously determine the power allocated for each dimension from a continuous action space, and select a proper number of quantization bits and modulation order from discrete action spaces. Simulations show that the proposed method enhances SemCom task performance by up to 77% and reduces sensing error by up to 58% compared to conventional digital systems.

This paper delves into the design of an energy efficient collaborative federated learning (CFL) methodology using which mobile devices exchange their FL model with a subset of their neighbors without reliance on a parameter server based on the distributed graph neural network (GNN) method. Each device is unable to send its FL model to every neighboring device due to device mobility and wireless resource limitations. To reduce the energy consumption of FL model transmission, each device must choose a subset of devices with which to share its FL model. This problem is formulated as an optimization problem to meet the constraints of delay and training loss while minimizing the energy consumption for model transmission. However, the formulated problem is difficult to solve since the device mobility patterns, and the relationship between the device connection scheme and CFL performance are unknown. To address this challenge, we analytically characterize the relationship between dynamic device connections and the performance of CFL methodology. Based on the analysis, a GNN based algorithm is proposed to enable each device to select a subset of its neighbors and the transmit power in a decentralized method. Compared to standard optimization methods that must determine device connections in a centralized manner, the GNN based method enables each device to use its neighboring devices' location and connection information to individually determine a subset of devices to transmit the local model. Given the device connections, the optimal transmit power of each device can be determined by convex optimization. Simulation results show that the proposed method can reduce the energy consumption for model transmission and training loss by up to 46% and 2%, respectively

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

This paper studies an end-to-end video semantic communication system for massive communication. In the considered system, the transmitter must continuously send the video to the receiver to facilitate character reconstruction in immersive applications, such as interactive video conference. However, transmitting the original video information with substantial amounts of data poses a challenge to the limited wireless resources. To address this issue, we reduce the amount of data transmitted by making the transmitter extract and send the semantic information from the video, which refines the major object and the correlation of time and space in the video. Specifically, we first develop a video semantic communication system based on major object extraction (MOE) and contextual video encoding (CVE) to achieve efficient video transmission. Then, we design the MOE and CVE modules with convolutional neural network based motion estimation, contextual extraction and entropy coding. Simulation results show that compared to the traditional coding schemes, the proposed method can reduce the amount of transmitted data by up to 25% while increasing the peak signal-to-noise ratio (PSNR) of the reconstructed video by up to 14%.

This paper studies the problem of the lightweight image semantic communication system that is deployed on Internet of Things (IoT) devices. In the considered system model, devices must use semantic communication techniques to support user behavior recognition in ultimate video service with high data transmission efficiency. However, it is computationally expensive for IoT devices to deploy semantic codecs due to the complex calculation processes of deep learning (DL) based codec training and inference. To make it affordable for IoT devices to deploy semantic communication systems, we propose an attention-based UNet enabled lightweight image semantic communication (LSSC) system, which achieves low computational complexity and small model size. In particular, we first let the LSSC system train the codec at the edge server to reduce the training computation load on IoT devices. Then, we introduce the convolutional block attention module (CBAM) to extract the image semantic features and decrease the number of downsampling layers thus reducing the floating-point operations (FLOPs). Finally, we experimentally adjust the structure of the codec and find out the optimal number of downsampling layers. Simulation results show that the proposed LSSC system can reduce the semantic codec FLOPs by 14%, and reduce the model size by 55%, with a sacrifice of 3% accuracy, compared to the baseline. Moreover, the proposed scheme can achieve a higher transmission accuracy than the traditional communication scheme in the low channel signal-to-noise (SNR) region.

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

In this paper, we consider the optimization of federated learning (FL) over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp). In such a system, MIMO devices transmit their locally trained FL models to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. AirComp enables efficient wireless model aggregation by the PS in bandwidth-limited settings. However, wireless channel fading can produce distortions in AirComp-based FL. To tackle this challenge, we develop a novel aggregation scheme that combines digital modulation with AirComp to mitigate wireless fading while ensuring communication efficiency. We formulate this as a joint transmit-receive beamforming design optimization problem which dynamically adjusts the beamforming matrices to minimize the FL training loss with transmission errors. To solve this problem based on limited information at the PS, we employ an artificial neural network (ANN) to estimate the local FL models of all devices. Then, we derive a closed-form optimal design of the transmit and receive beamforming matrices based on predicted FL models. Numerical evaluations validate the advantages of the proposed methodology in terms of model training performance compared with baselines.

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.