Codebook Construction Research Articles

New Constructions of Codebooks Nearly Meeting the Welch Bound

Optimal codebooks meeting the Welch bound with equality are desirable in many areas, such as direct spread code division multiple access communications, compressed sensing and so on. However, it is difficult to construct such optimal codebooks. There have been a number of attempts to construct codebooks nearly meeting the Welch bound. In this paper, we introduce a generic construction of codebooks using generalized bent functions from [Formula: see text] to [Formula: see text], where [Formula: see text] is a prime and [Formula: see text] is a positive integer with [Formula: see text]. With this construction, we obtain two new families of codebooks nearly meeting the Welch bound. These codebooks have new parameters and could have a small alphabet size. In particular, a class of regular generalized bent functions from [Formula: see text] to [Formula: see text] is presented.

Two constructions of asymptotically optimal codebooks via the trace functions

In this paper, we present two new constructions of complex codebooks with multiplicative characters, additive characters and trace functions over finite fields, and determine the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, in the first construction, we generalize the result in [28]. In the second construction, we generalize the results in [12], we can achieve Welch bound for any odd prime p, we also derive the whole distribution of their inner products. The parameters of these codebooks are new.

Generalized asymptotically optimal codebooks

Recently, Luo et al. proposed two new codebooks based on the operations of some given sets, which are asymptotically optimal. In the paper, we present new construction of codebooks. We determine maximal cross-correlation amplitude of the constructed codebooks. The constructed codebooks are near optimal to the Welch bound. This new codebooks in the paper generalize Luo et al.′s constructions. The parameters of constructed codebooks are nimble and new under some circumstances.

Efficient Data Collection Over Multiple Access Wireless Sensors Network

Data collection in Wireless Sensor Networks (WSN) draws significant attention, due to emerging interest in technologies ranging from Internet of Things (IoT) networks to simple “Presence” applications, which identify the status of the devices (active or inactive). Numerous Medium Access Control (MAC) protocols for WSN, which can address the challenge of data collection in dense networks, were suggested over the years. Most of these protocols utilize the traditional layering approach, in which the MAC layer is unaware of the encapsulated packet payload, and therefore there is no connection between the data collected, the physical layer and the signaling mechanisms. Nonetheless, in many of the applications that intend to utilize such protocols, nodes may need to exchange very little information, and do so only sporadically, that is, while the number of devices in the network can be very large, only a subset wishes to transmit at any given time. Thus, a tailored protocol, which matches the signaling, physical layer and access control to traffic patterns is required. In this work, we design and analyze a data collection protocol based on information theoretic principles. In the suggested protocol, the sink collects messages from up to K sensors simultaneously, out of a large population of sensors, without knowing in advance which sensors will transmit, and without requiring any synchronization, coordination or management overhead. In other words, neither the sink nor the other sensors need to know who are the actively transmitting sensors, and this data is decoded directly from the channel output. We provide a simple codebook construction with very simple encoding and decoding procedures. We further design a secure version of the protocol, in which an eavesdropper observing only partial information sent on the channel cannot gain significant information on the messages transmitted or even which are the sources that sent these messages.

On the Design of Near-Optimal Sparse Code Multiple Access Codebooks

Sparse code multiple access (SCMA) is one of the competitive non-orthogonal multiple access (NOMA) techniques for the next generation of communication systems. In this paper, a systematic construction procedure for the design of SCMA codebooks under various channel environments is proposed in order to achieve near-optimal designs, especially for cases which consider small-scale SCMA parameters. The procedure includes the rotation angle design, the construction of multi-dimensional codebooks, and the design of labelling rules. The detailed design criteria and the related properties are derived and investigated. A low-complexity construction algorithm is further proposed for the cases of fading channels. Numerical results show that the designed SCMA codebooks provide improvements in error performance for all of the considered channel conditions, when compared to the codebooks presented in the prior literature.

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Biometric recognition for human identification plays a key role in the rapid development of computer vision and pattern recognition research areas. The biometrics, refers to the automatic identification of a person based behavioral characteristics, physiological properties or traits. Signature recognition is one such human identification method and can be performed either in offline or online mode. This paper proposed an offline handwritten signature recognition which is based on image processing technique, scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) features and support vector machines (SVMs). The handwritten signature images were then recognized through the proposed method that involves identification of regions of interest and representation of those regions as SIFT or SURF, construction of codebooks and the multi-class classification of the feature histograms using support vector machines (SVMs). Experiments have been carried out with our dataset of 1600 samples and a recognition rate in excess of 95% was obtained over the ten-fold cross validations.

Two classes of near-optimal codebooks with respect to the Welch bound

<p style='text-indent:20px;'>An <inline-formula><tex-math id="M1">\begin{document}$ (N,K) $\end{document}</tex-math></inline-formula> codebook <inline-formula><tex-math id="M2">\begin{document}$ {\mathcal C} $\end{document}</tex-math></inline-formula> is a collection of <inline-formula><tex-math id="M3">\begin{document}$ N $\end{document}</tex-math></inline-formula> unit norm vectors in a <inline-formula><tex-math id="M4">\begin{document}$ K $\end{document}</tex-math></inline-formula>-dimensional vectors space. In applications of codebooks such as CDMA, those vectors in a codebook should have a small maximum magnitude of inner products between any pair of distinct code vectors. In this paper, we propose two constructions of codebooks based on <inline-formula><tex-math id="M5">\begin{document}$ p $\end{document}</tex-math></inline-formula>-ary linear codes and on a hybrid character sum of a special kind of functions, respectively. With these constructions, two classes of codebooks asymptotically meeting the Welch bound are presented.

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

AbstractRecently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.

A New Construction of Codebooks Meeting the Levenshtein Bound

Codebooks with low coherence have extensive applications in many fileds such as code division multiple access (CDMA) communication systems, MIMO communications, compressed sensing and so on. In this paper, based on additive characters over finite fields, we propose a construction of optimal codebook with respect to the Levenshtein bound and verify that it is a new construction. By shortening the length of the optimal codebooks, we present a construction of codebooks asymptotically meeting the Levenshtein bound. To the best of our knowledge, the parameters of the asymptotically optimal codebooks are new.

A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification

Automated histopathological image analysis is a challenging problem due to the complex morphological structure of histopathology images. Bag-of-features is one of the prominent image representation methods which has been successfully applied in histopathological image analysis. There are four phases in the bag-of-features method, namely feature extraction, codebook construction, feature encoding, and classification. Out of which feature encoding is one of the prime phases. In feature encoding phase, images are represented in terms of visual words before feeding into support vector machine classifier. However, the feature encoding phase of the bag-of-features framework considers the one feature to encode each image in terms of visual words due to which the system can not use the merits of other features. Therefore, to improve the efficacy of the bag-of-features framework, a new weighted two-dimensional vector quantisation encoding method is proposed in this work. The proposed method is tested on two histopathological image datasets for classification. The experimental results show that the combination of SIFT and ORB features with two dimensional vector quantisation encoding method returns 80.13% and 77.13% accuracy on ADL and Blue Histology datasets respectively which is better than other considered encoding methods.

On the Accuracy of Quantization Cell Approximation in MIMO Broadcast Systems Based on Limited Feedback

This study investigates the accuracy of quantization cell approximation (QCA) in a multiple-input multiple-output (MIMO) broadcast channel. QCA is an analytical quantization model used to approximate the quantized channel state information (CSI) in limited-feedback-based MIMO systems. It has been widely used in important studies for analytical tractability because it approximates the quantized CSI as a simple beta random variable multiplied by a deterministic value. Moreover, the effect of quantization is solely concentrated on the deterministic value such that the corresponding performance analysis is stochastically independent of the quantization process. Nevertheless, the accuracy of QCA has not been carefully demonstrated in previous studies. In this study, a generalized version of QCA is proposed with a complete analysis. Because the proposed QCA requires the use of a specific distance measure, the validity of the distance measure is first investigated. Based on the proposed distance measure, the accuracy of QCA is estimated by analyzing the difference between the spectral efficiencies achieved using QCA and random matrix quantization (RMQ). The corresponding results show that the difference gradually decreases and converges to zero as the number of feedback bits increases. As QCA and RMQ provide performance upper and lower bounds, respectively, in terms of codebook construction, these results prove the asymptotic validity of QCA with respect to the number of feedback bits. Both analysis and simulation results demonstrate that the difference in spectral efficiencies is also small for a moderate number of feedback bits. In addition, this study also demonstrates an asymptotic difference in spectral efficiencies with respect to the signal-to-noise-ratio (SNR). The difference increases with the SNR, but it is bounded by a finite value. Thus, the difference in the worst case SNR can also be suppressed by increasing the number of feedback bits.

Four Constructions of Asymptotically Optimal Codebooks via Additive Characters and Multiplicative Characters

In this paper, we present four new constructions of complex codebooks with multiplicative characters, additive characters, and quadratic irreducible polynomials and determine the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, we generalize the result obtained by Zhang and Feng and contain theirs as a special case. The parameters of these codebooks are new.

Predictive Strategy for Energy Harvesting MIMO Systems With Finite Alphabet Inputs and Limited Feedback

Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the aid of context information, by constructing a precoding codebook offline at both the transmitter and receiver, to relax the time pressure for the online strategy. Two schemes were provided to accelerate the codebook construction. One is to estimate IMI during optimization by using a reference function. The other is by starting the optimization of each precoder from an adjacent precoder. With the channel distribution information (CDI), the feedback overhead is reduced by minimizing the codebook size. By the methods above, the building speed of the precoding codebook can be up to two orders of magnitude faster than no accelerating strategies. Meanwhile, through topology analysis, we propose a higher layer predictive strategy, which determines the index of allocated energy at each slot within a frame. The higher layer strategy takes full advantage of the constructed precoding codebook and context information when estimating the future throughput. Simulation results demonstrate the effectiveness and practicality of the proposed two-layer strategy.

Two constructions of asymptotically optimal codebooks according to the welch bound

In a number of applications, the codebook that minimizes the maximal cross-correlation amplitude (Imax) is often desirable. There are two major ingredients in this paper. These two contents are to propose two constructions of codebooks. The codebooks generated by these constructions asymptotically meet the Welch bound. What we find is the codebook constructed in Section 2 can also asymptotically meet the Levenshtein bound. The parameters of these codebooks are new.

Two constructions of asymptotically optimal codebooks

Codebooks with low-coherence have wide applications in many fields such as direct spread code division multiple access communications, compressed sensing, signal processing and so on. In this paper, we propose two constructions of complex codebooks from the operations of certain sets. The complex codebooks produced by these constructions are proved to be asymptotically optimal with respect to the Welch bound. In addition, the parameters of the complex codebooks presented in this paper are new and flexible in some cases.

Two Constructions of Asymptotically Optimal Codebooks via the Hyper Eisenstein Sum

Codebooks with low-coherence have wide utilization in many fields, such as direct spread code division multiple access communications, compressed sensing and so on. There are two major ingredients in this paper. The first is to present a new character sum, the hyper Eisenstein sum and study the properties of this character sum. As an application, the second ingredient is to propose two constructions of codebooks with the hyper Eisenstein sum. The codebooks generated by these constructions asymptotically meet the Welch bound. The parameters of these codebooks are new.

Statistical MIMO beam codebook design for mmWave transmissions

We propose a novel method for constructing beam codebooks, i.e., a set of beamforming weights, to be used for beam sweeping and data transmission in mmWave massive MIMO channels. The proposed beam codebooks are designed according to the statistical distribution of the channel’s angles of departure in order to efficiently sectorize the angular space covered by the beams. In particular, the cumulative distribution function of these angles is used to sectorize the angular beam space in order to design the beams. We first present a general closed-form construction of beam codebooks for an arbitrary distribution and then tailor the results to the Laplace distribution. We consider both narrow and broad beams. The resulting beams are a priori better adapted to the channel statistics and thus enhance the spectral efficiency of the transmission as compared to the state-of-the-art beam designs. Finally, we investigate the performance of the design and also present some analytical bounds quantifying the inherent loss of a directional beamforming compared to the optimal beamforming with perfect channel state information at the transmitter.

Deep Learning-Aided SCMA

Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.

Human action recognition: a construction of codebook by discriminative features selection approach

Human activity recognition (HAR) has significance in the domain of pattern recognition. HAR handles the complexity of human physical changes and heterogeneous formats of same human actions performed under dissimilar subjects. This research contributes a unique method focusing on the changes in human movement. The purpose is to identify and categorise human actions from video sequences. The interest points (IPs) are extracted from the subject video and motion history images (MHIs) are constructed and analysed after image segmentation. Discriminative features (DFs) are selected and the visual vocabulary is learned from the extracted DF (EDF). The EDF are then quantised by using visual vocabulary and images are represented based upon frequencies of visual words (VW). VW are formed from the EDF and then, a histogram of VW is developed based on the feature vectors extracted from MHI. These feature vectors are used for training support vector machine (SVM) for the classification of actions into various categories. Benchmark datasets like KTH, Weizmann and HMDB51 are used for evaluation and comparison with existing action recognition approaches depicts the better performance of adopted strategy.

Human action recognition: a construction of codebook by discriminative features selection approach

Human activity recognition (HAR) has significance in the domain of pattern recognition. HAR handles the complexity of human physical changes and heterogeneous formats of same human actions performed under dissimilar subjects. This research contributes a unique method focusing on the changes in human movement. The purpose is to identify and categorise human actions from video sequences. The interest points (IPs) are extracted from the subject video and motion history images (MHIs) are constructed and analysed after image segmentation. Discriminative features (DFs) are selected and the visual vocabulary is learned from the extracted DF (EDF). The EDF are then quantised by using visual vocabulary and images are represented based upon frequencies of visual words (VW). VW are formed from the EDF and then, a histogram of VW is developed based on the feature vectors extracted from MHI. These feature vectors are used for training support vector machine (SVM) for the classification of actions into various categories. Benchmark datasets like KTH, Weizmann and HMDB51 are used for evaluation and comparison with existing action recognition approaches depicts the better performance of adopted strategy.