Information-Coupled Turbo Codes for LTE Systems

We propose a new class of information-coupled (IC) Turbo codes to improve the transport block (TB) error rate performance for long-term evolution (LTE) systems, while keeping the hybrid automatic repeat request protocol and the Turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing a few common information bits. We propose a feed-forward and feed-back decoding scheme and a windowed (WD) decoding scheme for decoding the whole TB by exploiting the coupled information between CBs. Both decoding schemes achieve a considerable signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and our proposed IC Turbo codes from the EXIT functions of underlying convolutional codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes is derived and calculated by the constructed EXIT charts. Numerical results show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for various code parameters at a TB error rate level of $10^{-2}$, which complies with the derived SNR gain upper bound.Comment: 13 pages, 12 figure

Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning

A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability. Note to Practitioners —This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and demonstrates the feasibility of applying reinforcement learning to handle fault-tolerant control problems. The proposed method has a generic structure and has the potential to be implemented in other renewable energy systems

Task-Irrelevant Context Learned Under Rapid Display Presentation: Selective Attention in Associative Blocking

In the contextual cueing task, visual search is faster for targets embedded in invariant displays compared to targets found in variant displays. However, it has been repeatedly shown that participants do not learn repeated contexts when these are irrelevant to the task. One potential explanation lays in the idea of associative blocking, where salient cues (task-relevant old items) block the learning of invariant associations in the task-irrelevant subset of items. An alternative explanation is that the associative blocking rather hinders the allocation of attention to task-irrelevant subsets, but not the learning per se. The current work examined these two explanations. In two experiments, participants performed a visual search task under a rapid presentation condition (300 ms) in Experiment 1, or under a longer presentation condition (2,500 ms) in Experiment 2. In both experiments, the search items within both old and new displays were presented in two colors which defined the irrelevant and task-relevant items within each display. The participants were asked to search for the target in the relevant subset in the learning phase. In the transfer phase, the instructions were reversed and task-irrelevant items became task-relevant (and vice versa). In line with previous studies, the search of task-irrelevant subsets resulted in no cueing effect post-transfer in the longer presentation condition; however, a reliable cueing effect was generated by task-irrelevant subsets learned under the rapid presentation. These results demonstrate that under rapid display presentation, global attentional selection leads to global context learning. However, under a longer display presentation, global attention is blocked, leading to the exclusive learning of invariant relevant items in the learning session