Codebook

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

Rank-Independent Codebook Design from a Quaternary Alphabet

Abstract: Single-user as well as multi-user MIMO systems employ codebook-based feedback to customize the signal transmission to the current channel conditions The two competing metrics for evaluating a codebook are packet-error-rate/throughput performance and codebook search complexity at the mobile To address this issue structured unitary codebooks are proposed where each codebook entry is restricted to a power of i and a single codebook is optimized for all ranks Such codebooks are shown to perform favorably with the best known (unconstrained) codebooks in terms of distance measures and are also shown to perform well in multi-user MIMO link-level simulations

On the trade-off between feedback and capacity in measured MU-MIMO channels

Abstract: In this work we study the capacity of multi-user multiple-input multiple-output (MU-MIMO) downlink channels with codebook-based limited feedback using real measurement data. Several aspects of MU-MIMO channels are evaluated. Firstly, we compare the sum rate of different MU-MIMO precoding schemes in various channel conditions. Secondly, we study the effect of different codebooks on the performance of limited feedback MU-MIMO. Thirdly, we relate the required feedback rate with the achievable rate on the downlink channel. Real multi-user channel measurement data acquired with the Eurecom MIMO OpenAir Sounder (EMOS) is used. To the best of our knowledge, these are the first measurement results giving evidence of how MU-MIMO precoding schemes depend on the precoding scheme, channel characteristics, user separation, and codebook. For example, we show that having a large user separation as well as codebooks adapted to the second order statistics of the channel gives a sum rate close to the theoretical limit. A small user separation due to bad scheduling or a poorly adapted codebook on the other hand can impair the gain brought by MU-MIMO. The tools and the analysis presented in this paper allow the system designer to trade-off downlink rate with feedback rate by carefully choosing the codebook.

A Beamforming Codebook Restriction for Cross-Tier Interference Coordination in Two-Tier Femtocell Networks

Abstract: This paper examines a novel multichannel multi-antenna-based cross-tier interference coordination strategy for two-tier femtocell networks. To reduce the cross-tier interference and improve the aggregate throughput of two-tier femtocell networks without exchanging feedback information by backhauling, we propose a beamforming codebook restriction strategy. The beamforming codebook restriction strategy enables the femtocell users to select the best channel that is robust to cross-tier interference before the transmission by reducing the size of the macrocell beamforming codebook. Although restricting the beamforming codebook increases the quantization error for macrocell users, the opportunistic channel selection strategy and the proportional fair scheduler compensate for the increased quantization error by exploiting the channel selection diversity gain and the multiuser diversity gain. Both analytical and numerical results demonstrate that the proposed strategies collaboratively improve the aggregate throughput of two-tier femtocell networks.

Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval

Abstract: In this paper, we investigate the problem of scalable visual feature matching in large-scale image search and propose a novel cascaded scalar quantization scheme in dual resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar quantization strategy. Specifically, for each dimension of the PCA-transformed feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database. The scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar quantization (CSQ) method is free of the costly visual codebook training and thus is independent of any image descriptor training set. The index structure of the CSQ is flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for large-scale image retrieval. Experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization.

Finite Rate Feedback for Spatially and Temporally Correlated MISO Channels in the Presence of Estimation Errors and Feedback Delay

Abstract: In this paper, the problem of finite-rate feedback for spatially and temporally correlated Rayleigh fading multiple input single output (MISO) channels with estimation errors at the receiver and feedback delay is addressed. A model that captures estimation errors, feedback delay, and finite-rate quantization of the channel is developed. A novel codebook design algorithm that minimizes the loss in ergodic capacity is proposed. Simulation results show that the new codebook designed under the consideration of estimation errors and feedback delay outperforms the codebook designed assuming ideal conditions. Analysis for the loss in ergodic capacity for spatially i.i.d channels with channel estimation errors and delay (EED) is presented and validated through simulations.