Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Deep Unfolding for Cooperative Rate Splitting Multiple Access in Hybrid Satellite Terrestrial Networks

Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.

Quick System-Level DDR3 Signal Integrity Simulation Research

---Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integrity （SI） simulation flow for the DDR3 interface, two system-level SI simulation methodologies, which are board-level S-parameter extraction in the frequency-domain and system-level simulation assumptions in the time domain, are introduced in this paper. By comparing the flow of Speed2000 and PowerSI/Hspice, PowerSI is chosen for the printed circuit board （PCB） board-level S-parameter extraction, while Tektronix oscilloscope （TDS7404） is used for the DDR3 waveform measurement. The lab measurement shows good agreement between simulation and measurement. The study shows that the combination of PowerSI and Hspice is recommended for quick system-level DDR3 SI simulation.

BP算法的改进及仿真研究

BP算法是神经网络中最常用的算法之一。分析传统的BP算法思想,发现它存在着收敛速度慢的问题。针对此问题,归纳一些行之有效的改进方法,本文论述这些方法的主要思想,并分析它们的改进效果,即仿真结果,结果表明改进算法的学习收敛速度大大的优于标准BP算法。

ART充填治疗儿童恒磨牙龋的临床研究

目的比较非创伤性修复治疗(ART)和常规玻璃离子充填方法治疗儿童第一恒磨牙龋的充填体保留情况。方法 82名患儿,浅龋、中龋共104颗,配对分为两组:ART组52颗,手用器械去腐,Ketac molar充填;对照组52颗,机钻去腐,Fu ji II LC充填。随诊6个月、12个月,评价两组充填体保留情况。结果 6个月后复查ART组48颗,对照组51颗,充填体存留率分别为93.75%和96.08%,经统计学检验差异无统计学意义(P>0.05)。12月后复查ART组41颗,对照组46颗,充填体存留率分别为85.37%和86.96%,差异无统计学意义(P>0.05)。12个月时ART组和对照组的累计生存率分别为80.03%和83.55%,差异亦无统计学意义(P>0.05)。结论 6个月、12个月后ART组和对照组的充填体存留率和累计生存率相似,ART和常规玻璃离子充填的效果相似,ART可用作儿童恒磨牙龋的治疗。