The closed-loop transmit diversity technique is used to increase the capacity of the downlink channel in multiple-input-multiple-output (MIMO) communication systems.The WCDMA standard [1] endorsed by 3GPP [2] adopts two modes of downlink closedloop schemes based on partial channel information that is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. In this article, some soft reconstruction techniques are introduced to improve the performance of Mode 1 of 3GPP, by taking advantage of the redundancy available in the channel information. We propose some algorithms for reconstruction of beamforming weights in the base station.The performance is examined in a simulated 3GPP framework in the presence of different feedback error rates at various mobile speeds. It is demonstrated that the proposed algorithms have substantial gain over the conventional approach for low to high mobile speeds. Index TermsClosed-loop transmit diversity, channel feedback, channel state information (CSI), downlink communication, FDD WCDMA, mode 1 of 3GPP, joint source-channel coding
A key element for many fading-compensation techniques is a (long-range) prediction tool for the fading channel. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. In this article, we propose an adaptive fading channel prediction algorithm using a sum-sinusoidal-based state-space approach. This algorithm utilizes an improved adaptive Kalman estimator, comprising an acquisition mode and a tracking algorithm. Furthermore, for the sake of a lower computational complexity, we propose an enhanced linear predictor for channel fading, including a multi-step linear predictor and the respective tracking algorithm. Comparing the two methods in our simulations show that the proposed Kalman-based algorithm significantly outperforms the linear method, for both stationary and non-stationary fading processes, and especially for long-range predictions. The performance and the self-recovering structure, as well as the reasonable computational complexity, makes the algorithm appealing for practical applications 1 .
A single-pixel camera for THz imaging has been proposed in [1]. In this article, we propose a new design for a single-detector THz imaging system based on Compressive Sampling (CS), which does not need raster scanning of the object in front of the THz beam. We exploit a time-efficient and costeffective design to acquire the CS measurements. As a result, the image acquisition time in the proposed imaging system is only limited to the speed of the THz detector. The proposed approach is applicable to other types of imaging as well.
Abstract-In this article, a time-domain calibration procedure is proposed for pulsed Terahertz Integrated Circuits (TIC) used in onchip applications, where the conventional calibration methods are not applicable. The proposed post-detection method removes the unwanted linear distortions, such as interfering echoes and frequency dispersion, by using only one single-port measurement. The method employs a wave-transfer model for analysis of the TIC, and the model parameters are obtained by a proposed blind estimation algorithm. A complete implementation of the method is demonstrated for a fabricated TIC, when used in an on-chip sensing application. The features of interest in the measured signal, such as absorption lines, can be masked or weakened by the distortion of the THz signal happening in a TIC. The proposed signal recovery approach improves the detection of those otherwise hidden features, and can significantly enhance the performance of existing TICs. To show the effectiveness of the proposed de-embedding method, numerical results are presented for simulated and measured signals. The method presented in this article is enabling for accurate TIC applications, and can be utilized to optimally design novel TIC structures for specific purposes.
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