Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.Comment: 14 pages, 10 figure

An Efficient Federated Learning Framework for Training Semantic Communication System

Semantic communication has emerged as a pillar forthe next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whosetraining performance heavily relies on data availability. Existingstudies often make unrealistic assumptions of a readily accessibledata source, where in practice, data is mainly created on the clientside. Due to privacy and security concerns, the transmission ofdata is restricted, which is necessary for conventional centralizedtraining schemes. To address this challenge, we explore semanticcommunication in a federated learning (FL) setting that utilizesclient data without leaking privacy. Additionally, we designour system to tackle the communication overhead by reducingthe quantity of information delivered in each global round.In this way, we can save significant bandwidth for resourcelimited devices and reduce overall network traffic. Finally, weintroduce a mechanism to aggregate the global model fromclients, called FedLol. Extensive simulation results demonstratethe effectiveness of our proposed technique compared to baselinemethods

Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications

Vehicle-to-Everything (V2X) communications is pivotal for modern transportation systems, but the challenges arise in scenarios with buildings, leading to signal obstruction and limited coverage. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Researchers are exploring on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360coverage while utilizing the advantages of RIS technology. In this paper, a STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitude and phase shift values of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two sub-problems. For the first sub-problem, we model the control variables as a Markov Decision Process and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Numerical results demonstrate that our solution approach outperforms the vanilla DDQN approach by 5.2%, and our proposed system outperforms the conventional RIS by 39%

Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications

Vehicle-to-Everything (V2X) communications play a crucial role in ensuring safe and efficient modern transportation systems. However, challenges arise in scenarios with buildings, leading to signal obstruction and coverage limitations. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Therefore, researchers have started focusing on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360\degree coverage while utilizing the advantages of RIS technology. In this paper, a STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitudes, and phase shifts of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two sub-problems. For the first sub-problem, we model the control variables as a Markov Decision Process (MDP) and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Extensive numerical analysis is developed to demonstrate the superiority of our proposed algorithm compared to benchmark schemes.Comment: 12 pages, 9 figure

Isogeometric analysis for size-dependent nonlinear thermal stability of porous FG microplates

In this article, we present for the first time a research analysis for the size-dependent effects on thermal buckling and post-buckling behaviors of functionally graded material micro-plates with porosities (imperfect FGM) using isogeometric analysis. A seventh-order shear deformation plate theory associated with the modified couple stress theory (MCST) is particularly imposed to capture the size-dependent phenomenon within imperfect FGM microplates. The material properties of imperfect FGM micro-plates with three different distributions of porosities including even, uneven and logarithmic-uneven varying across the plate thickness are derived from the modified rule-of-mixture assumption. The nonlinear governing equation for size-dependent imperfect FGM micro-plate under uniform, linear and nonlinear temperature rise is derived using the Von-Karman assumption and Hamilton's principle. Through numerical example, the effect of temperature rise, boundary conditions, power index, porosity volume fraction, porosity distribution pattern and material length scale parameter on thermal buckling and post-buckling behaviors of FGP micro-plates are investigated

Imbalance Cost-Aware Energy Scheduling for Prosumers Towards UAM Charging: A Matching and Multi-agent DRL Approach

In this paper, an energy scheduling problem is formulated for the prosumer-based urban area, where prosumers are regarded as the drone charging stations for urban air mobility (UAM). Particularly, since electric vertical take-off and landing aircraft (eVTOL) is regarded as the anticipated technique for future UAM, we consider eVTOL drone taxis for transporting passengers. The objective is to minimize the overall energy supply-demand imbalance cost. This problem covers two aspects: 1) association between passengers and eVTOLs, and 2) energy balance strategy determination through power grid energy scheduling for each prosumer. For the first aspect, a destination collision-aware Gale-Shapely matching game (DC-MG) approach is proposed, where the distance concern of passengers, the remaining energy of eVTOLs, and the destination collision are comprehensively considered. Subsequently, hierarchical agglomerative clustering (HAGC)-based multi-agent dueling double deep Q network (MA3DQN) with a multi-step bootstrapping (MSB) approach (CMA3DQN) is proposed, where the input (i.e., energy demand) depends on the output of the first aspect. Particularly, the HAGC approach is adopted to group all prosumers into several agents to reduce the input feature size of each agent. Then the MA3DQN with MSB approach is applied to achieve the best grid energy balance strategy per prosumer. Finally, the experimental results demonstrate the effectiveness of the proposed method. Particularly, the imbalance cost achieved by the proposed joint method is separately 128.71× , 12.57× , and 11.72× less than the random energy scheduling approach, the independent multi-agent dueling DQN approach, and the approach of employing the double deep Q network per cluster