Codebook

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

MIMO Systems with Limited Rate Differential Feedback in Slowly Varying Channels

Abstract: In this paper, an adaptive limited feedback linear precoding technique for temporally correlated multiple-input multiple-output (MIMO) channels is proposed, where the receiver has perfect channel knowledge but the transmitter only receives a quantized channel direction. To perform adaptation to the time correlation structure, we employ a differential feedback, where the "amount" of the perturbation added to the previous precoder is determined by the statistics of the directional variation. Based on random matrix quantization analysis, we develop a spherical cap codebook approach, where the cap is centered at the previous precoder and the radius of the cap is determined proportional to the identified directional variation. If the channel is highly correlated in time, it is shown that the proposed differential feedback scheme achieves significant throughput improvement in the large codebook size regime. The rest of the paper is devoted to developing a systematic spherical cap codebook generation method. The developed approach employs a feedback scheme that uses a differential rotation of the previously used precoder. Our codebook adaptation is based on generating a perturbation in Euclidean space and projecting the perturbation onto the unitary space. Simulation results show that the proposed adaptation scheme accurately tracks the channel using only a small rate of feedback.

OCF--A new coding algorithm for high quality sound signals

Abstract: Optimum Coding in the Frequency domain (OCF) uses entropy coding of quantized spectral coefficients to efficiently code high quality sound signals with 3 bits/sample. In an iterative algorithm psychoacoustic weigthing is used to get the quantization noise to be masked in every critical band. The coder itself uses iterative quantizer control to get each data block to be coded with a fixed number of bits. Details about the OCF-Coder are presented together with information about the codebook needed and the training for the entropy coder. An algorithm for calculating a noise-to-mask ratio is presented which helps to identify, where quantization noise could be audible.

Channel Adaptive Quantization for Limited Feedback MIMO Beamforming Systems

Abstract: Multiple-input multiple-output (MIMO) wireless systems can achieve significant diversity and array gain by using transmit beamforming and receive combining techniques. In the absence of full channel knowledge at the transmitter, the transmit beamforming vector can be quantized at the receiver and sent to the transmitter using a low-rate feedback channel. In the literature, quantization algorithms for the beamforming vector are designed and optimized for a particular channel distribution, commonly the uncorrelated Rayleigh distribution. When the channel is not uncorrelated Rayleigh, however, these quantization strategies result in a degradation of the receive signal-to-noise ratio (SNR). In this paper, switched codebook quantization is proposed where the codebook is dynamically chosen based on the channel distribution. The codebook adaptation enables the quantization to exploit the spatial and temporal correlation inherent in the channel. The convergence properties of the codebook selection algorithm are studied assuming a block-stationary model for the channel. In the case of a nonstationary channel, it is shown using simulations that the selected codebook tracks the distribution of the channel resulting in improvements in SNR. Simulation results show that in the case of correlated channels, the SNR performance of the link can be significantly improved by adaptation, compared with nonadaptive quantization strategies designed for uncorrelated Rayleigh-fading channels

SPARCs for Unsourced Random Access

Abstract: Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number $K_a$ are active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. We present a concatenated coding construction for U-RA on the AWGN channel, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. We propose a modified version of the approximate message passing (AMP) algorithm as an inner decoder and give a precise asymptotic analysis of the error probabilities of the AMP decoder and of a hypothetical optimal inner MAP decoder. This analysis shows that the concatenated construction can achieve a vanishing per-user error probability in the limit of large blocklength and a large number of active users at sum-rates up to the symmetric Shannon capacity, i.e. as long as $K_aR < 0.5\log_2(1+K_a\SNR)$. This extends previous point-to-point optimality results about SPARCs to the unsourced multiuser scenario. Furthermore, we give an optimization algorithm to find the power allocation for the inner SPARC code that minimizes the required $\SNR$.

Scalar quantization for large scale image search

Abstract: Bag-of-Words (BoW) model based on SIFT has been widely used in large scale image retrieval applications. Feature quantization plays a crucial role in BoW model, which generates visual words from the high dimensional SIFT features, so as to adapt to the inverted file structure for indexing. Traditional feature quantization approaches suffer several problems: 1) high computational cost---visual words generation (codebook construction) is time consuming especially with large amount of features; 2) limited reliability---different collections of images may produce totally different codebooks and quantization error is hard to be controlled; 3) update inefficiency--once the codebook is constructed, it is not easy to be updated. In this paper, a novel feature quantization algorithm, scalar quantization, is proposed. With scalar quantization, a SIFT feature is quantized to a descriptive and discriminative bit-vector, of which the first tens of bits are taken out as code word. Our quantizer is independent of collections of images. In addition, the result of scalar quantization naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error can be flexibly reduced and controlled by efficiently enumerating nearest neighbors of code words. The performance of scalar quantization has been evaluated in partial-duplicate Web image search on a database of one million images. Experiments reveal that the proposed scalar quantization achieves a relatively 42% improvement in mean average precision over the baseline (hierarchical visual vocabulary tree approach), and also outperforms the state-of-the-art Hamming Embedding approach and soft assignment method.