Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data (e.g., pose image) and command (i.e., prompt), thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). Thus, to assess the quality of experience (QoE) of ISAC-based AIGC services, this paper proposes a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with the number of users. To solve the CAQA-AIGC problem with low complexity, a standard linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model (GDM) algorithms without LP, LPDRL-F converges faster and finds better resource allocation solutions, thus improving AvgCAQA by more than 10%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.

With the growing diversity and density of mobile nodes, indoor wireless channels are becoming increasingly complex. Existing channel modeling methods struggle to balance accuracy and adaptability, which calls for low-complexity models capable of capturing dynamic indoor environments efficiently. This paper proposes a novel propagation graph (PG) framework that models the channel effects of dynamic objects indoors by designing a scattering-aware point cloud neural network (SPointNet). The proposed PG framework explicitly incorporates reflection, transmission, and diffuse scattering into channel modeling, and employs a physics-aware scatterer discretization and classification strategy, which reduces the complexity of the conducted graph. Then, SPointNet enables fast estimation of scattering coefficients, allowing the model to bypass exhaustive analysis of material properties. Finally, we conduct channel measurements in real indoor environments to validate the proposed approach. Experimental results show that the proposed model accurately models channel responses while significantly reducing modeling time compared to traditional PG-based methods.

The deployment of object detection tasks on embedded or mobile platforms has become increasingly prevalent, driven by the heightened demand across various scenarios. However, for object recognition tasks such as gesture recognition, the use of overly complex network models presents a formidable obstacle in achieving real-time detection tasks, and the majority of lightweight convolution menthods based on depth-separated convolution lack accuracy. In this paper, we propose a lightweight and highly accurate convolutional neural network for gesture recognition on terminal device (GRTD-Net), specially designed for devices with scarce computing power and tight hardware resources. In GRTD-Net, we proposed the convolution method R2SGConv that masterfully harmonizes model size and accuracy, elegantly achieving a delicate balance between efficiency and lightweight design. Moreover, we propose a neck network paradigm with good feature fusion capability to compensate for the accuracy degradation due to the use of lightweight convolutional modules in neck networks. Experimental results show that the proposed GRTD-Net model improves the mAP0.5and mAP0.95by 0.8% and 2.2%, reduces model parameters by 35.2%, increases FPS by 20.7%, and reduces the inference latency by 2.9 ms, compared with the popular YOLOv5 algorithm on the dataset Gesture. We successfully deployed GRTD-Net on ARM devices and proved its practicality in constrained environments.

With the growing demand for high spectral efficiency in large-scale ad hoc networks, traditional half-duplex communication fails to meet the requirements. Although in-band full-duplex (IBFD) communication improves spectral efficiency, there is serious mutual interference (MI) in high-density node environments. This paper proposes a novel Frequency Hopping and Duplex Mode Selection (FH-DMS) method for MI suppression. By leveraging stochastic geometry theory, the throughput of the IBFD network with FH-DMS is derived. Furthermore, the optimization of the number of frequency hopping points and the FD mode selection probability is analyzed to maximize throughput. Simulation results show that the proposed method reduces MI intensity nearly tenfold and increases throughput by approximately ten times, particularly in high-density, self-interference-limited high-quality communication scenarios.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation enhances transmission in high-mobility scenarios but also increases detection complexity, especially when fractional Doppler shifts are present. To tackle these issues, we propose a low-complexity sparse pre-conditioned conjugate gradient-linear minimum mean square error (SPCG-LMMSE) detector, which can obtain the LMMSE solution without needing matrix inversion. Based on the sparsity and quasi-banded structure of the ODDM time-domain equalization matrix, the algorithm utilizes an improved conjugate gradient method to approach the optimal solution quickly. Simulation results demonstrate that the detector can significantly reduce complexity while maintaining excellent performance, with additional gains in multipath and high-order modulation scenarios.

Orthogonal time-frequency space (OTFS) modulation has emerged as a promising solution for reliable communication in high-mobility scenarios, addressing the limitations of traditional orthogonal frequency-division multiplexing (OFDM) systems. However, existing OTFS detection methods, including linear, nonlinear, and AI-based detectors, struggle with either high computational complexity or suboptimal performance. To overcome these limitations, we propose a low-complexity graph attention network-based OTFS detector (GAT-OTFS) tailored for the reduced cyclic prefix (RCP) OTFS scenario. Our approach leverages the sparsity of the DD domain equivalent channel to reduce complexity by precisely constructing a sparse graph for GNN-based detection. By introducing an attention mechanism, the GAT-OTFS effectively captures the correlation between different channel paths, enhancing detection accuracy. Simulation results demonstrate that the GAT-OTFS offers substantial performance improvements over existing detectors, with reduced complexity, making it a viable solution for future high-mobility communication systems.

In low earth orbit satellite (LEO-Sat) communication systems based on orthogonal time frequency space (OTFS) modulation, existing channel estimation methods usually use high-power pilots, leading to a high peak-to-average power ratio (PAPR). In this paper, we propose a comb-type pilot based frame structure by inserting multiple identical Zadoff-Chu (ZC) sequences along the Doppler dimension. The ZC sequences are arranged at equal intervals in the delay dimension. The power of pilots is consistent with the power of data symbols, which can achieve low PAPR at the transmitter. At the receiver, based on the analyzed power distribution of the correlation function, we derive the power threshold to identify the received pilots. Then, an iterative ZC-sequences based channel estimation algorithm is proposed aided by data detection and interference cancellation. Through iterations, the data interference can be alleviated and estimation accuracy can be improved. Simulation results demonstrate that the proposed scheme can achieve superior channel estimation performance with low PAPR in the time domain.