Codebook

About: Codebook is a research topic. Over the lifetime, 8492 publications have been published within this topic receiving 115995 citations.

Downlink 8 tx codebook sub-sampling for csi feedback

Abstract: This invention is codebook sub-sampling of the reporting of RI, CQI, W1 and W2. If CSI mode 1 is selected RI and W1 are jointly encoded using codebook sub-sampling in report 1. If CSI mode 2 is selected W1 and W2 are jointly encoded using codebook sub-sampling in report 2.

Multi-rank beamforming precoding apparatus and method

Abstract: A multi-rank beamforming (MRBF) scheme in which the downlink channel is estimated and an optimal precoding matrix to be used by the MRBF transmitter is determined accordingly. The optimal precoding matrix is selected from a codebook of matrices having a recursive structure which allows for efficient computation of the optimal precoding matrix and corresponding Signal to Interference and Noise Ratio (SINR). The codebook also enjoys a small storage footprint. Due to the computational efficiency and modest memory requirements, the optimal precoding determination can be made at user equipment (UE) and communicated to a transmitting base station over a limited uplink channel for implementation over the downlink channel.

Off-line handwritten Chinese character recognition as a compound Bayes decision problem

Abstract: A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantization as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.

Progressive refinement of beamforming vectors for high-resolution limited feedback

Abstract: Limited feedback enables the practical use of channel state information in multiuser multiple-input multiple-output (MIMO) wireless communication systems. Using the limited feedback concept, channel state information at the receiver is quantized by choosing a representative element from a codebook known to both the receiver and transmitter. Unfortunately, achieving the high resolution required with multiuser MIMO communication is challenging due to the large number of codebook entries required. This paper proposes to use a progressively scaled local codebook to enable high resolution quantization and reconstruction for multiuser MIMO with zero-forcing precoding. Several local codebook designs are proposed including one based on a ring and one based on mutually unbiased bases; both facilitate efficient implementation. Structure in the local codebooks is used to reduce search complexity in the progressive refinement algorithm. Simulation results illustrate sum rate performance as a function of the number of refinements.

Effective Codebooks for Human Action Representation and Classification in Unconstrained Videos

Abstract: Recognition and classification of human actions for annotation of unconstrained video sequences has proven to be challenging because of the variations in the environment, appearance of actors, modalities in which the same action is performed by different persons, speed and duration, and points of view from which the event is observed. This variability reflects in the difficulty of defining effective descriptors and deriving appropriate and effective codebooks for action categorization. In this paper, we propose a novel and effective solution to classify human actions in unconstrained videos. It improves on previous contributions through the definition of a novel local descriptor that uses image gradient and optic flow to respectively model the appearance and motion of human actions at interest point regions. In the formation of the codebook, we employ radius-based clustering with soft assignment in order to create a rich vocabulary that may account for the high variability of human actions. We show that our solution scores very good performance with no need of parameter tuning. We also show that a strong reduction of computation time can be obtained by applying codebook size reduction with Deep Belief Networks with little loss of accuracy.