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.

Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm KKT-embedding E2GA to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.

In integrated sensing and communication (ISAC) enabled unmanned aerial vehicle (UAV) systems, based on sensed information such as user positions, UAV scheduling could be optimized to enhance the communication performance. However, sensing errors are inevitable, leading to a performance degradation. This paper proposes a sensing-error-aware (SEA) multi-UAV scheduling scheme (SEA-scheduling). First, the impact of the sensing errors on communication performance is analyzed, and a SEA communication rate is derived. Then, targeting to maximize this SEA rate, multi-UAV collaborative scheduling is jointly optimized with sensing resource allocation. The problem is solved by decomposing into two subproblems, i.e., a joint UAV position schedule, user association and bandwidth allocation optimization subproblem (PUB) and a sensing resource optimization subproblem (SRO), which can be solved iteratively. A generative diffusion(GD)-driven multi-agent reinforcement learning (GD-MADRL) algorithm is proposed to solve PUB, and a classical simulated annealing (SA) algorithm is adopted to solve SRO. The main idea of GD-MADRL is to introduce the GD model in MADRL to generate training data with sensing errors, enhancing the robustness of generated UAV scheduling strategies. Simulation results demonstrate that when there are sensing errors, the proposed SEA-scheduling scheme improves the communication rate by up to 30% compared to existing sensing-error-unaware schemes.

The maximum distance separable (MDS) coding based caching is effective to reduce the delivery delay of content in low earth orbit (LEO) satellite networks. However, due to the severe packet loss of satellite links and the variations among them, the existing distance aware delivery node selection methods may lead to significant packet loss and delivery delay. To solve this problem, a packet loss aware delivery node selection method is proposed in this paper. First, the delivery delay of coded sub-contents is analyzed by taking packet loss into account. And the delivery node selection problem is formulated as a delivery delay minimization problem. Then, the problem is divided into multiple sub-problems according to the time slot, where each sub-problem is a single-constraint knapsack problem. Based on the Edmonds theorem, the greedy algorithm is used to obtain the optimal solution. Finally, simulations are carried out on a walkerdelta constellation to verify the performance of the proposed method. And the results show that the user perceived packet loss and the packet retransmission induced delivery delay can be reduced by 83% and 42.8%, respectively, when compared with the existing distance aware delivery node selection method.

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.

The multiplexing of Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) results in a loss of the eMBB transmission rate. Existing multiplexing resource allocation schemes focus on enhancing the eMBB data rate, ignoring the Quality of Experience (QoE) of URLLC users and degrading the profit of the operator. Considering all users' QoE, we define the operator profit as the weighted difference between the average QoE and the operator cost which integrates the eMBB transmission rate loss and the successful gain of the URLLC service. This paper focuses on the cost aware eMBB-URLLC multiplexing resource allocation (CAMRA) to maximize the operator profit. With our proposed event-driven Actor-Critic (EDAC) Deep Reinforcement Learning (DRL) algorithm, CAMRA scheme can enhance the operator profit by more than 30% compared to the Rate- Aware strategy,

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.

Resource management is important for wireless networks. However, most model-driven resource allocation algorithms are limited by computational complexity and suboptimal spectral efficiency. This paper focuses on learning-based resource management to improve spectral efficiency in multicellular networks with imperfect channel state information (CSI). Considering imperfect CSI, the joint subchannel selection and power allocation problem can be formulated as a probability-constrained non-convex optimization problem. By means of parameter transformation, the non-convex optimization problem with probabilistic constraints is first transformed into a non-probabilistic optimization problem. Then, to solve this problem, a dual-module network based on dueling deep Q-network and deep deterministic policy gradient algorithm is proposed to maximize spectral efficiency. Simulation results show that the proposed dual-module network outperforms model-driven optimization algorithms such as fractional programming and existing deep reinforcement learning algorithms in terms of spectral efficiency.