Large Codebook Research Articles

Rate-Overhead Tradeoff for IOS-Aided Beam Training: How Large Codebook Is Enough for the IOS?

Intelligent omni-surface (IOS) is a novel metasurface to provide full-dimensional services by jointly engineering its reflective and refractive properties. In this letter, we study an IOS-aided system where the beamforming scheme is developed via beam training with codebooks at the base station (BS), IOS, and users to avoid the extremely high overhead of acquiring perfect channel state information (CSI). A larger codebook size brings a higher peak data rate but requires more spectrum and time resources for beam training, leading to low system throughput. To investigate the tradeoff between the peak data rate and training overhead, we mathematically model the relationship between the codebook size of the IOS and the throughput. Given different user distributions, we discuss the codebook size under the constraint of the asymmetric power of the reflective and refractive signals. Moreover, the influence of the power ratio between reflective and refractive signals on the codebook size is also analyzed. Simulation results verify our theoretical analysis.

Novel Methods for Leveraging Large Cohort Studies for Qualitative and Mixed-Methods Research.

Qualitative research methods, while rising in popularity, are still a relatively underutilized tool in public health research. Usually reserved for small samples, qualitative research techniques have the potential to enhance insights gained from large questionnaires and cohort studies, both deepening the interpretation of quantitative data and generating novel hypotheses that might otherwise be missed by standard approaches; this is especially true where exposures and outcomes are new, understudied, or rapidly changing, as in a pandemic. However, methods for the conduct of qualitative research within large samples are underdeveloped. Here, we describe a novel method of applying qualitative research methods to free-text comments collected in a large epidemiologic questionnaire. Specifically, this method includes: 1) a hierarchical system of coding through content analysis; 2) a qualitative data management application; and 3) an adaptation of Cohen's κ and percent agreement statistics for use by a team of coders, applying multiple codes per record from a large codebook. The methods outlined in this paper may help direct future applications of qualitative and mixed methods within large cohort studies.

Optimal alphabet for single text compression

A text written using symbols from a given alphabet can be compressed using the Huffman code, which minimizes the length of the encoded text. It is necessary, however, to employ a text-specific codebook, i.e. the symbol-codeword dictionary, to decode the original text. Thus, the compression performance should be evaluated by the full code length, i.e. the length of the encoded text plus the length of the codebook. We studied several alphabets for compressing texts – letters, n-grams of letters, syllables, words, and phrases. If only sufficiently short texts are retained, an alphabet of letters or two-grams of letters is optimal. For the majority of Project Gutenberg texts, the best alphabet (the one that minimizes the full code length) is given by syllables or words, depending on the representation of the codebook. Letter 3 and 4-grams, having on average comparable length to syllables/words, perform noticeably worse than syllables or words. Word 2-grams also are never the best alphabet, on the account of having a very large codebook. We also show that the codebook representation is important – switching from a naive representation to a compact one significantly improves the matters for alphabets with large number of symbols, most notably the words.Thus, meaning-expressing elements of the language (syllables or words) provide the best compression alphabet.

Learning Token-Based Representation for Image Retrieval

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.

An Improved EPA-Based Receiver Design for Uplink LDPC Coded SCMA System

Sparse code multiple access (SCMA) is an emerging paradigm for efficient enabling of massive connectivity in future machine-type communications (MTC). In this letter, we conceive the uplink transmissions of the low-density parity check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA system, which is based on message passing algorithm (MPA) for multiuser detection followed by individual LDPC decoding, may suffer from the drawback of the high complexity and large decoding latency, especially when the system has large codebook size and/or high overloading factor. To address this problem, we introduce a novel receiver design by applying the expectation propagation algorithm (EPA) to the joint detection and decoding (JDD) involving an aggregated factor graph of LDPC code and sparse codebooks. Our numerical results demonstrate the superiority of the proposed EPA based JDD receiver over the conventional Turbo receiver in terms of both significantly lower complexity and faster convergence rate without noticeable error rate performance degradation.

Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. These pre-defined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. This leads to large codebook sizes with high beam training overhead which makes it hard for these systems to support highly mobile applications. To overcome these limitations, this paper develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on the receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wolpertinger</i> -variant architecture that is capable of efficiently searching the large discrete action space. The proposed learning framework respects the RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results confirm the ability of the developed framework to learn near-optimal beam patterns for line-of-sight (LOS), non-LOS (NLOS), mixed LOS/NLOS scenarios and for arrays with hardware impairments without requiring any channel knowledge.

Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This article presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by red-green-blue (RGB) cameras deployed at the base stations. In particular, the article proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. This is mainly achieved via a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\sim} 90\%$</tex-math></inline-formula> of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.

A Direct and Generalized Construction of Polyphase Complementary Sets With Low PMEPR and High Code-Rate for OFDM System

A major drawback of orthogonal frequency division multiplexing (OFDM) systems is their high peak-to-mean envelope power ratio (PMEPR). The PMEPR problem can be solved by adopting large codebooks consisting of complementary sequences with low PMEPR. In this paper, we present a new construction of polyphase complementary sets (CSs) using generalized Boolean functions (GBFs), which generalizes Schmidt's construction in 2007, Paterson's construction in 2000 and Golay complementary pairs (GCPs) given by Davis and Jedwab in 1999. Compared with Schmidt's approach, our proposed CSs lead to lower PMEPR with higher code-rate for sequences constructed from higher-order (≥ 3) GBFs. We obtain polyphase complementary sequences with maximum PMEPR of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+1</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k+2</sup> - 2M where k,M are non-negative integers that can be easily derived from the GBF associated with the CS.

Encoding Pathlet and SIFT Features With Bagged VLAD for Historical Writer Identification

Offline writer identification plays an important role in forensic document examination and historical document analysis. Today, challenges still exist in historical writer identification (WI), where documents may present very complex handwriting styles. In this paper, we propose novel techniques for a detailed description and accurate identification of handwriting in historical documents. Because handwriting contours are one of the most salient components to characterize one’s handwriting style, a novel pathlet feature is proposed to describe their rich properties beyond slant and curvature in a principled way; these properties can be exploited in a VLAD-like encoding framework for fine-grained handwriting description. Besides the pathlet feature, we extract unidirectional SIFT feature to describe handwriting corners and junctions. To effectively encode the pathlet and SIFT features, a novel encoding method, named bagged VLAD, is further proposed to address the problem that a large codebook sparsely spreads out the data points and leads to a degraded performance, allowing a much larger codebook for improved encoding performance. Our proposed method achieves state-of-the-art performance on ICDAR2017 Historical-WI database and ICDAR2019 HDRC-IR database, and has won the first place in ICDAR2019 HDRC-IR competition.

A proposal of prior probability-oriented clustering in feature encoding strategies.

Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the trade-off between recognition performance and computational complexity and a traditional framework has the disadvantage to image recognition issues when a large codebook; the number of unique clusters becomes smaller than a designated codebook size because some clusters converge to close positions. This paper focusses on the disadvantage from a perspective of the distribution of prior probabilities and presents a clustering framework including two objectives that are alternated to the k-means and the GMM. Our approach is first evaluated with synthetic clustering datasets to analyze a difference to traditional clustering. In the experiment section, although our approach alternated to the k-means generates similar results to the k-means results, our approach is able to finely tune clusters for our objective. Our approach alternated to the GMM significantly improves our objective and constructs intuitively appropriate clusters, especially for huge and complicatedly distributed samples. In the experiment on image recognition issues, two state-of-the-art encodings, the Fisher Vector (FV) using the GMM and the Vector of Locally Aggregated Descriptors (VLAD) using the k-means, are evaluated with two publicly available image datasets, the Birds and the Butterflies. For the results of the VLAD with our approach, the recognition performances tend to be worse compared to the original VLAD results. On the other hand, the FV using our approach is able to improve the performance, especially in a larger codebook size.

An Efficient SCMA Codebook Design Based on Lattice Theory for Information-Centric IoT

Different from traditional communication systems, information-centric Internet of things (IC-IoT) as a novel smart paradigm needs to guarantee end-to-end connectivity for rapidly growing smart devices. How to meet the demands of massive connection has become a key problem for IC-IoT. Sparse code multiple access (SCMA) as a code-domain non-orthogonal multiple access (NOMA) technique has been intensively investigated. With SCMA, each user employs different sub-carrier frequencies for transmission, and different users can share the same sub-carrier frequency via superposition coding. The spectrum efficiency is thus improved. However, designing large-scale codebook sets is still an open problem for SCMA. In this paper, a new lattice-based codebook design method is proposed via mother constellation optimization. First, the optimization problem of the mother constellation is formulated. Through analysis, the problem can be converted into two sub-problems. Accordingly, we first use the lattice theory to find a constellation containing infinite points with large coding gain. After that, we search for a boundary that contains a set of points via spherical packing. Such an approach enables us to obtain a real constellation satisfying a power saving criterion. Secondly, the mother constellation with large power variance is obtained from the real multi-dimensional constellation. Finally, lattice-based codebooks are generated by combining the mother constellation and the mapping matrices with constellation rotation. Simulations demonstrate that the designed codebooks have improved bit error rate (BER) performance with large codebook size, especially at high signal to noise ratio (SNR).

Soft-Decision Decoding of Permutation Block Codes in AWGN and Rayleigh Fading Channels

We introduce an efficient soft-decision decoder for permutation block codes when assuming AWGN and slow fading frequency-nonselective channels. The decoder implements a combination of the Hungarian algorithm and Murty's algorithm for the kth assignment. Results show that the proposed algorithm achieves better coding gain when compared with hard-decision decoding. A salient feature of the decoder is that it operates within practical computational complexity, especially for large codebooks.

Balancing search space partitions by sparse coding for distributed redundant media indexing and retrieval

Effective partitioning multimedia indexes is key for efficient kNN search. But existing algorithms are based on document similarity, without partition size or redundancy constraints. Our goal is to create an index partitioning algorithm that addresses the specific properties of a distributed system: load balancing across nodes, redundancy in node failure and efficient node usage under concurrent querying. We propose the representation of data with overcomplete codebooks. Each document is quantized into a small set of codewords and indexed on per-codeword partitions. Quantization algorithms are designed to fit data as best as possible, leading to a bias toward codewords that fit the principal directions of data in the original space. In this paper, we propose the balanced KSVD (B-KSVD) algorithm: It distributes data uniformly across codewords, according to the distribution in the original space. The comprehensive experiments focused on measuring the effectiveness of partition size balancing and retrieval quality. Results show that B-KSVD better balances partition sizes (i.e., lower SD in partition size distribution), compared to k-means and KSVD baselines. B-KSVD achieves 38% 1-recall by inspecting only 1% of the full index, distributed over 10 partitions. k-means creates partitions with higher size variation and requires either larger codebooks or the inspection of larger portions of the index to achieve similar retrieval performance.

Spatial Channel Estimation-Based FDD-MIMO Interference Alignment Systems

The practical feasibility of interference alignment (IA) is a major challenge in real-world implementation. The majority of the research contributions assume ideal channel reciprocity, which may be valid in time division duplex systems. In frequency division duplex (FDD) systems, large codebooks are utilized for channel quantization, and consume the feedback channel capacity. In this letter, a spatial channel estimation method is proposed for FDD-MIMO IA systems. The method utilizes the path correlation between the forward and reverse channels, where channels are on different frequency bands. A transformation matrix is employed to estimate the angles of departure of the forward channel. The proposed method shows a significant improvement of the sum rate without the need of feedback overhead bits.

Blind Image Quality Assessment Based on High Order Statistics Aggregation.

Blind image quality assessment (BIQA) research aims to develop a perceptual model to evaluate the quality of distorted images automatically and accurately without access to the non-distorted reference images. The state-of-the-art general purpose BIQA methods can be classified into two categories according to the types of features used. The first includes handcrafted features which rely on the statistical regularities of natural images. These, however, are not suitable for images containing text and artificial graphics. The second includes learning-based features which invariably require large codebook or supervised codebook updating procedures to obtain satisfactory performance. These are time-consuming and not applicable in practice. In this paper, we propose a novel general purpose BIQA method based on high order statistics aggregation (HOSA), requiring only a small codebook. HOSA consists of three steps. First, local normalized image patches are extracted as local features through a regular grid, and a codebook containing 100 codewords is constructed by K-means clustering. In addition to the mean of each cluster, the diagonal covariance and coskewness (i.e., dimension-wise variance and skewness) of clusters are also calculated. Second, each local feature is softly assigned to several nearest clusters and the differences of high order statistics (mean, variance and skewness) between local features and corresponding clusters are softly aggregated to build the global quality aware image representation. Finally, support vector regression is adopted to learn the mapping between perceptual features and subjective opinion scores. The proposed method has been extensively evaluated on ten image databases with both simulated and realistic image distortions, and shows highly competitive performance to the state-of-the-art BIQA methods.

Interference Alignment With Quantized Grassmannian Feedback in the K-User Constant MIMO Interference Channel

A simple channel state information (CSI) feedback scheme is proposed for interference alignment (IA) over the K-user constant multiple-input-multiple-output interference channel (MIMO IC). The proposed technique relies on the identification of invariants in the IA equations, which enables the reformulation of the CSI quantization problem as a single quantization on the Grassmann manifold at each receiver. The scaling of the number of feedback bits with the transmit power sufficient to preserve the multiplexing gain that can be achieved under perfect CSI is established. We show that the CSI feedback requirements of the proposed technique are better (lower) than what is required when using previously published methods (including state-of-the-art codebook-based as well as analog feedback techniques), for system dimensions (number of users and antennas) of practical interest. Furthermore, we show through simulations that this advantage persists at low SNR, in the sense that the proposed technique yields a higher sum-rate performance for a given number of feedback bits. Finally, to complement our analysis, we introduce a statistical model that faithfully captures the properties of the quantization error obtained for random vector quantization (RVQ) on the Grassmann manifold for large codebooks; this enables the numerical (Monte Carlo) analysis of general Grassmannian RVQ schemes for codebook sizes that would be impractically large to simulate.

Scene Categorization Through Combining LBP and SIFT Features Effectively

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

Improving throughput for downlink multi user MIMO-LTE advanced networks using SINR approximation and hierarchical CSI feedback

In multi user MIMO-LTE advanced networks, the main issue is related to signal to noise ratio SINR mismatch which can result in reduced throughput and performance. Also the conventional code books may result in feedback overhead when the channels change slowly. Hence in this paper, we propose downlink multi user MIMO-LTE advanced networks using SINR approximation and hierarchical CSI feedback. Initially, signal and spatially correlated flat fading channels model of MU-MIMO is defined. Then a signal to noise ratio SINR approximation techniques is employed which utilises the channel state information at the base station. An advance structural codebook and an idea of hierarchical feedback are introduced. The main idea of hierarchical feedback is that if the channel is altered slowly, the channel state information feedback can be aggregated over multiple feedback intervals so that the aggregated bits index a larger codebook. There are pre-defined numbers of levels in a hierarchical codebook tree. This increased codebook size can effectively improve the performance of MU-MIMO. By simulation results, we show that the proposed technique improves the throughput and reduces the overhead.

GPU Accelerated Self-Organizing Map for High Dimensional Data

The self-organizing map (SOM) model is an effective technique applicable in a wide range of areas, such as pattern recognition and image processing. In the SOM model, the most time-consuming procedure is the training process. It consists of two time-consuming parts. The first part is the calculation of the Euclidean distances between training vectors and codevectors. The second part is the update of the codevectors with the pre-defined neighborhood structure. This paper proposes a graphics processing unit (GPU) algorithm that accelerates these two parts using the graphics rendering ability of GPUs. The distance calculation is implemented in the form of matrix multiplication with compute shader, while the update process is treated as a point-rendering process with vertex shader and fragment shader. Experimental results show that our algorithm runs much faster than previous CUDA implementations, especially for the large neighborhood case. Also, our method can handle the case with large codebook size and high dimensional data.

Self-organizing maps for the design of multiple description vector quantizers

Multiple description coding is an appealing tool to guarantee graceful signal degradation in the presence of unreliable channels. While the principles of multiple description scalar quantization are well-understood and solid guidelines exist to design effective systems, the same does not hold for vector quantization, especially at low bit-rates, where burdensome and unsatisfactory design techniques discourage its use altogether in applications.In this work we use the self-organizing maps to design multiple description VQ codebooks. The proposed algorithm is flexible, fast and effective: it deals easily with a large variety of situations, including the case of more than two descriptions, with a computational complexity that remains fully affordable even for large codebooks, and a performance comparable to that of reference techniques.A thorough experimental analysis, conducted in a wide range of operating conditions, proves the proposed technique to perform on par with well-known reference methods based on greedy optimization, but with a much lower computational burden. In addition, the resulting codebook can be itself optimized, thus providing even better performance. All experiments are fully reproducible, with all software and data available online for the interested researchers.