Codebook

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

High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Abstract: Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional, communication overhead becomes a bottleneck for edge learning. To address this issue, we propose a novel framework of hierarchical gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional Grassmannian codebook, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a socalled hinge vector, which is compressed using another low-dimensional Grassmannian quantizer designed under the criterion of minimum distortion. The other feature of the framework is a bit-allocation scheme for reducing the distortion, which divides the total quantization bits to determine the resolutions of low-dimensional quantizers. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while achieving similar learning accuracies.

Method and apparatus for creating a multi-user mimo codebook using a single user mimo codebook

Abstract: A wireless communication method and apparatus for creating a codebook in a multiple input/multiple output (MIMO) wireless communication system are disclosed. The method includes adapting a single user codebook, wherein the single user codebook comprises a plurality single user beamforming vectors, into a multi-user codebook, wherein the multi-user codebook comprises a plurality of multi-user beamforming vectors. The method further includes grouping the codebook into a plurality of unitary matrices, selecting a plurality of beamforming vectors from the plurality of unitary matrices, forming a rank specific code-book from the beamforming vectors and the unitary matrices, and selecting a subset of a total number of pairs to form the plurality of unitary matrices.

Apparatus and method for determining beamforming vector in a codebook-based beamforming system

Abstract: An apparatus and method for determining a beamforming vector in a codebook-based transmission beamforming system are provided. In a feedback method for a receiver in a codebook-based transmission beamforming system, forthcoming channel values for a forthcoming period are generated using M present and previous channel estimates. Effective channel gains for all transmission beamforming vectors included in a codebook are calculated using the forthcoming channel values. A codebook index corresponding to the maximum effective channel gains is fed back to a transmitter.

MU-MIMO with Channel Statistics-Based Codebooks in Spatially Correlated Channels

Abstract: We investigate the performance of limited feedback zero forcing beamforming-based MU-MIMO in correlated channels when users have the opportunity to adapt their codebooks as a function of their own channel statistics (CDIT). An upper bound on the rate loss is derived in terms of the amount of feedback and the statistics of the channel. In order to maintain a constant rate loss with respect to the perfect CSIT case, it is shown that the number of feedback bits should scale linearly with the SNR (in dB) with a slope proportional to the rank of the user transmit correlation matrix and logarithmically with the ratio of the second to the first largest eigenvalue of the transmit correlation matrix. Simulation results confirm analytical derivations and suggest that a channel statistics-based codebook is a reasonable simple approach to boost the throughput in correlated channels while incurring only a small overhead.

Codebook sub-sampling for csi feedback on pucch for 4tx mimo

Abstract: Channel state information (CSI) feedback in a wireless communication system is disclosed. User equipment transmits a CSI feedback signal via a Physical Uplink Control CHannel (PUCCH). If the UE is configured in a first feedback mode, the CSI comprises a first report jointly coding a Rank Indicator (RI) and a first precoding matrix indicator (PMI1), and a second report coding Channel Quality Indicator (CQI) and a second precoding matrix indicator (PMI2). If the UE is configured in a second feedback mode, the CSI comprises a first report coding RI, and a second report coding CQI, PMI1 and PMI2. The jointly coded RI and PMI1 employs codebook sub-sampling, and the jointly coding PMI1, PMI2 and CQI employs codebook sub-sampling.