U-shaped Split Federated Learning (U-SFL) has been widely applied in image coding, as it can effectively balance parallel training, model privacy, and local computational cost. The performance of U-SFL largely depends on the selection of split points and the aggregation frequency, and thus, model splitting (MS) and model aggregation (MA) strategies are critical. In UAV scenarios, fluctuating communication links and computational resources can significantly impact U-SFL performance. To address this challenge, we propose a resource-adaptive U-SFL (AU-SFL) framework, which adaptively selects optimal MS and MA strategies based on the available computational and communication resources. Specifically, we first conduct a theoretical convergence analysis that systematically quantifies the individual and joint impacts of MS and MA strategies on convergence behavior. Then, we formulate an optimization problem aimed at minimizing training latency, grounded in the theoretical convergence analysis. Subsequently, an alternating MS-MA algorithm is proposed to solve the problem, which is decomposed into two subproblems and solved alternatively. Extensive experiments on multiple benchmark datasets demonstrate that AU-SFL achieves a 1.61 times reduction in convergence time while improving multi-scale structural similarity index (MS-SSIM) by 4.4%, conclusively validating our adaptive optimization strategy’s effectiveness.

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.

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. This paper proposes a deep learning-based task-oriented communication architecture for end-to-end (E2E) semantics transmission, where extracted semantics is compressed by the proposed adaptable semantic compression (ASC) method. However, accommodating multiple users in a delay-intolerant system poses a challenge. Higher compression ratios conserve channel re-sources but cause semantic distortion, while lower ratios demand more resources and may lead to transmission failure due to delay constraints. To address this, we optimize both compression ratio and resource allocation to maximize task success probability. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm that separates the problem into two subproblems and solving them iteratively. Simulation results show that the proposed algorithm can obtain at least 14.3% success gains over baseline algorithms.

Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this article, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the proposed TESC scheme to two emblematic text tasks: 1) sentiment analysis and 2) question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) the TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency and 2) the TESC scheme enjoys about 150% performance gain compared to the traditional communication method.

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)-based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.

Current image coding methods for semantic communication typically concentrate on intelligent tasks or image reconstruction separately, and seldom consider both aspects simultaneously. To balance these two aspects during wireless image transmission, we propose a joint source-channel coding method based on the semantics of pixels (SP), which can retain both pixel information for reconstruction and semantic information for intelligent tasks. Specifically, we first design a gradient-based mechanism to quantify the semantic importance of downstream intelligent tasks on pixels. Then, we design the SP-based loss function to train the deep joint source-channel coding network. Experiment results demonstrate that the proposed method maintains reconstruction performance and improves the task performance by 1.61% and 4.06%, respectively, compared to the state-of-the-art deep joint source-channel coding method and traditional separate source-channel coding method at the same transmission rate and signal-to-noise ratio.

Task-oriented semantic communication has received growing interests, which can significantly reduce the amount of transmitted data without affecting task performance. In this paper, a novel semantic communication system based on semantic triplets (SCST) is proposed, in which the semantics is represented via the explainable semantic triplets. Specifically, we propose a semantic extraction method to convert the transmitted texts into semantic triplets, which can be further compressed via the designed semantic filtering method. The semantic triplets then will be encoded and transmitted via the wireless channel to complete intelligent tasks at the receiver. Moreover, we then apply the SCST to sentiment analysis task and question-answering task to verify the effectiveness, where the semantic encoder and decoder are designed respectively considering the final task. The experiment results show that the proposed SCST can obtain at least 43.5% and 52% accuracy gains, compared to the baselines using traditional communication method.

The sixth-generation mobile communication system proposes the vision of smart interconnection of everything, which requires accomplishing communication tasks while ensuring the performance of intelligent tasks. A joint source-channel coding method based on semantic importance is proposed, which aims at preserving semantic information during wireless image transmission and thereby boosting the performance of intelligent tasks for images at the receiver. Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task’s perception results with respect to the features. Then, we design the semantic loss function in the way of using semantic weights to weight the features. Finally, we train the deep joint source-channel coding network using the semantic loss function. Experiment results demonstrate that the proposed method achieves up to 57.7% and 9.1% improvement in terms of intelligent task’s performance compared with the source-channel separation coding method and the deep source-channel joint coding method without considering semantics at the same compression rate and signal-to-noise ratio, respectively.

Limited radio frequency (RF) resources restrict the number of users that can participate in federated learning (FL) thus affecting FL convergence speed and performance. In this paper, we first introduce visible light communication (VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication system, in which each indoor user can use both VLC and RF to transmit its FL model parameters. Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance. The problem is first separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The final user selection and bandwidth allocation are obtained by iteratively solving these two subproblems. Simulation results show that the proposed FL algorithm that efficiently uses VLC and RF for FL model transmission can improve the prediction accuracy by up to 10% compared with a conventional FL system using only RF.