In this paper, we propose a trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (mimo) receivers. We co...
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In this paper, we propose a trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (mimo) receivers. We construct a trellis diagram to represent the search space of a transmitted mimo signal. With the trellis model, we evenly distribute the workload of candidates searching among multiple trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each trellis node. By leveraging the trellis structure, we develop an approximate Log-MAP algorithm by using a small list of largest exponential terms to compute the LLR (log-likelihood ratio) values. The trellis-search based detector has a fixed-complexity and is very suitable for parallel VLSI implementation. As a case study, we have designed and synthesized a trellis-search based soft-input soft-output mimo detector for a 4 x 4 16-QAM system using a 1.08 V TSMC 65 nm technology. The detector can achieve a maximum throughput of 1.7 Gb/s with a core area of 1.58 mm(2).
In this paper, the multiple-input, multiple-output (mimo) radar signal processing algorithm is efficiently employed as an anticollision methodology for the identification of multiple chipless radio-frequency identific...
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In this paper, the multiple-input, multiple-output (mimo) radar signal processing algorithm is efficiently employed as an anticollision methodology for the identification of multiple chipless radio-frequency identification (RFID) tags. Tag-identifying methods for conventional chipped RFID tags rely mostly on the processing capabilities of application-specific integrated circuits (ASICs). In cases where more than one chipless tag exists in the same area, traditional methods are not sufficient to successfully read and distinguish the IDs, while the direction of each chipless tag can be obtained by applying mimo technology to the backscattering signal. In order to read the IDs of the tags, beamforming is used to change the main beam direction of the antenna array and to receive the tag backscattered signal. On this basis, the RCS of the tags can be retrieved, and associated IDs can be identified. In the simulation, two tags with different IDs were placed away from each other. The IDs of the tags were successfully identified using the presented algorithm. The simulation result shows that tags with a distance of 0.88 m in azimuth can be read by a mimo reader with eight antennas from 3 m away.
Big multi-step wind speed forecasting is hard to be realized due to the high-requirement of the built forecasting models. However, the big multi-step forecasting is expected in the wind power systems, which can provid...
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Big multi-step wind speed forecasting is hard to be realized due to the high-requirement of the built forecasting models. However, the big multi-step forecasting is expected in the wind power systems, which can provide sufficient time for the wind grids to be operated in the emergency cases. In the study, a new hybrid computational framework for the big multi-step wind speed forecasting is proposed, consisting of Wavelet Packet Decomposition (WPD), Elman Neural Networks (ENN), boosting algorithms and Wavelet Packet Filter (WPF). The novelty of the study is to investigate the big multi-step wind speed forecasting performance using various computing strategies in the proposed new hybrid WPD-BoostENN-WPF framework. Four different wind speed time series data are provided to complete the real forecasting experiments. The experimental results indicate that: (a) all of the proposed hybrid models have better performance than the corresponding single forecasting models in the big multi-step predictions. The 9 step MAE errors for the experimental data #1 from the proposed four hybrid forecasting models are only 1.2821 m/s, 1.1276 m/s, 1.1718 m/s and 1.2684 m/s, respectively;(b) the proposed four hybrid forecasting models have no significant forecasting difference;and (c) all of them are suitable for the big multi-step wind speed forecasting. (C) 2018 Elsevier Ltd. All rights reserved.
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