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A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain compare to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compare to the fixed RIS paradigm. Moreover, the NOMA scheme is capable to achieve a gain of 42% in contrast with the OMA scheme in terms of sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.Index terms-Deep reinforcement learning (DRL), federated learning (FL), intelligent reflecting surfaces (IRSs), non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), resource management

Background: Numerous studies have revealed that the abnormal expression of pyroptosis-related genes is closely related to the prognosis of lung adenocarcinoma (LUAD); however, a comprehensive analysis has yet to be conducted. This study aimed to reveal the influence of pyroptosis-related genes on the prognosis of LUAD and establish a prognostic model based on those genes, in order to evaluate the prognosis of LUAD. Methods:The data of tumor and normal samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential analysis was used to identify pyroptosis-related genes (obtained from the GeneCards database) that were differentially expressed (DE) in TCGA database. Univariate and stepwise multivariate Cox proportional hazards regression analyses were used to screen feature genes related to LUAD overall survival (OS) and construct gene signature. Gene set enrichment analysis (GSEA) was then performed to reveal potential functions related to gene signature.Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to reveal distinctions in each cell-subtype groups in the immune landscape of LUAD.Results: Overall, 26 DE genes (DEGs) associated with pyroptosis were obtained. Among them, 4 (MKI67, BTK, MST1, and TUBB6) were selected as prognostic genes and a 4-gene signature with a good prognostic performance in the TCGA and GEO was constructed. The gene signature was shown to be an independent prognostic factor of LUAD in subsequent analysis. Functional enrichment indicated that the 4-gene signature may participate in the tumorigenesis and development of LUAD through various pathways related to tumor progression to play a prognostic role in LUAD. Additionally, the results of the immune landscape indicated that the 4-gene signature may affect the prognosis of LUAD via cooperating with changes in the immune microenvironment. Conclusions:The key biomarkers and pathways identified in this study would deepen the comprehension of the molecular mechanism of pyroptosis in LUAD. More importantly, the 4-gene signature may serve as a novel potential prognostic model for LUAD.

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A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional DQN algorithm, the MDQN algorithm enables the experience of multiagent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the aid of the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than that of invoking the circular trajectory and the 2D trajectory, respectively.

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