A Universal Receiver for Uplink NOMA Systems

Given its capability in efficient radio resource sharing, non-orthogonal multiple access (NOMA) has been identified as a promising technology in 5G to improve the system capacity, user connectivity, and scheduling latency. A dozen of uplink NOMA schemes have been proposed recently and this paper considers the design of a universal receiver suitable for all potential designs of NOMA schemes. Firstly, a general turbo-like iterative receiver structure is introduced, under which, a universal expectation propagation algorithm (EPA) detector with hybrid parallel interference cancellation (PIC) is proposed (EPA in short). Link-level simulations show that the proposed EPA receiver can achieve superior block error rate (BLER) performance with implementation friendly complexity and fast convergence, and is always better than the traditional codeword level MMSE-PIC receiver for various kinds of NOMA schemes.Comment: This paper has been accepted by IEEE/CIC International Conference on Communications in China (ICCC 2018). 5 pages, 4 figure

Turbo-like Iterative Multi-user Receiver Design for 5G Non-orthogonal Multiple Access

Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing has been identified as a promising technology in 5G to help improving system capacity, user connectivity, and service latency in 5G communications. This paper provides a brief overview of the progress of NoMA transceiver study in 3GPP, with special focus on the design of turbo-like iterative multi-user (MU) receivers. There are various types of MU receivers depending on the combinations of MU detectors and interference cancellation (IC) schemes. Link-level simulations show that expectation propagation algorithm (EPA) with hybrid parallel interference cancellation (PIC) is a promising MU receiver, which can achieve fast convergence and similar performance as message passing algorithm (MPA) with much lower complexity.Comment: Accepted by IEEE 88th Vehicular Technology Conference (IEEE VTC-2018 Fall), 5 pages, 6 figure

Numerical Analysis and Verification of Residual Stress in T Joint of S355 Steel

T joint is a widely used welding form. The welding deformation and residual stress produced during the welding process will affect the integrity and reliability of the structure. In this study, S355 low alloy steel was used as test material, and the thermal-mechanical coupling characteristics of multi-layer welding were combined with SYSWELD finite element software to calculate the residual stress of T joint after welding. The residual stress of multi-layer welding T joint with tangent tube and sheet after welding was measured by X-ray diffraction method. The results showed that the numerical simulation agreed well with the experimental results. For the transverse residual stress, the maximum residual stress appeared near the weld toe, and the transverse stress perpendicular to the weld direction presented tensile stress at the weld center and presented stress at the tube surface far from the weld. For the longitudinal residual stress, the maximum residual stress also appeared near the weld toe, and the value was the largest at the center of the weld and decreased along the direction perpendicular to the weld. The research results can provide a reference for actual welding design

How does Team Learning from Failure Facilitate New Product Performance? The Double-edged Moderating Effect of Collective Efficacy

Learning from failure can foster innovation, but how a new product development (NPD) team’s learning from failure affects new product performance requires more insights. In particular, the question remains on how collective efficacy, which discerns team members’ belief to achieve desired goals, affects team learning from failure towards improving new product performance. Using social cognitive theory complemented by sensemaking and attribution theories, we examine the effects of NPD teams’ (experiential and vicarious) learning from failure on new product performance and the moderating effects of collective efficacy on these relationships. With survey data collected from 398 responses within 152 NPD teams in Chinese high-tech small and medium-sized enterprises, we find that both experiential and vicarious learning from failure enhance new product performance in terms of speed to market and product innovativeness. Further, as collective efficacy increases, the positive effect of experiential learning from failure on speed to market is strengthened. However, the positive effect of vicarious learning from failure on product innovativeness is weakened. Our results suggest that NPD teams can benefit from experiential and vicarious learning from failure to improve new product performance but must pay attention to the double-edged effect of collective efficacy

Junction Temperature Consistency Analysis of MMC Submodule

Although modular multilevel converter（MMC）is currently widely used in the field of DC power transmission due to its excellent topology performance, the natural DC bias characteristics inevitably cause thermal imbalance of internal devices. Too high temperature at the junction of power devices is one of the major causes of damage. Therefore, it is necessary to further investigate the factors that affect the device junction temperature. This paper calculated the power loss and junction temperature by combined the thermal impedance model and compared junction temperature under two typical modulation strategies

Model-Based Reinforcement Learning with Isolated Imaginations

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment. Second, we perform policy optimization based on the decoupled latent imaginations, where we roll out noncontrollable states into the future and adaptively associate them with the current controllable state. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild, such as self-driving cars that can anticipate the movement of other vehicles, thereby avoiding potential risks. On top of our previous work, we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups. Our empirical study demonstrates that Iso-Dream++ outperforms existing reinforcement learning models significantly on CARLA and DeepMind Control.Comment: arXiv admin note: substantial text overlap with arXiv:2205.1381