Codeword Index Research Articles

An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering

Information has been uploaded and downloaded through the Internet, day in and day out, ever since we immersed ourselves in the Internet. Data security has become an area demanding high attention, and one of the most efficient techniques for protecting data is data hiding. In recent studies, it has been shown that the indices of a codebook can be reordered to hide secret bits. The hiding capacity of the codeword index reordering scheme increases when the size of the codebook increases. Since the codewords in the codebook are not modified, the visual performance of compressed images is retained. We propose a novel scheme making use of the fundamental principle of the codeword index reordering technique to hide secret data in encrypted images. By observing our experimental results, we can see that the obtained embedding capacity of 197,888 is larger than other state-of-the-art schemes. Secret data can be extracted when a receiver owns a data hiding key, and the image can be recovered when a receiver owns an encryption key.

Closed-Loop Wireless Power Transfer With Adaptive Waveform and Beamforming: Design, Prototype, and Experiment

A closed-loop far-field wireless power transfer (WPT) system with adaptive waveform and beamforming using limited feedback is designed, prototyped, and experimented. Spatial domain and frequency domain are jointly exploited by utilizing waveform and beamforming at the transmitter in WPT system to adapt to the multipath fading channel and boost the output dc power. A closed-loop architecture based on a codebook design and an over-the-air limited feedback with low complexity is proposed. The codebook consists of multiple codewords where each codeword represents particular waveform and beamforming. The transmitter sweeps through the codebook and the receiver then feeds the optimal codeword index back to the transmitter, so that the waveform and beamforming can be adaptive for maximizing the output dc power without requiring explicit channel estimation and the knowledge of accurate Channel State Information. The proposed closed-loop WPT with adaptive waveform and beamforming using limited feedback is prototyped using a Software Defined Radio equipment and measured in two real indoor environments. It is experimentally shown that the proposed closed-loop WPT with adaptive waveform and beamforming is able to enhance the output dc power by up to 14.7 dB in comparison with conventional 1-tone 1-antenna WPT system.

Learning-Based Adaptive IRS Control With Limited Feedback Codebooks

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are configured by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design, random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.

An Improved NID Steganography Method for Low Bitrate Speech on VoIP

In the procedure of encoding process on low bitrate speech, fixed codebook division is an efficient and promising embedding method for steganography. An improved neighbor index division (NID) steganography method based on the high bitrate frame of G.723.1 codec (6.3kbit/s) is proposed, which employs the parity and low distortion of neighbor indices for G.723.1 fixed codebooks. Differing from previously NID method which performs quantized index modulation (QIM) beforehand, the proposed method divides codeword indices into separate sub-codebooks according to the secret message bits dynamically in the original G.723.1 codec quantization period. Compared with existing NID method, our proposed method doesn’t need to divide the codebook before the encoding starts. The embedding and codebook dividing happen simultaneously, which utilizes the characteristics of specific secret message bits. The experiment results show that the proposed method has a much lower quality degradation for the decoding speech and still fulfills the low latency requirement for communication.

Transmit Precoding Based Low Complexity Codebooks and Finite Feedback Rate for Space Shift Keying MIMO Systems

Transmit Precoding (TPC) considerably improves the performance of Space Shift Keying (SSK) systems. Codebook-based TPC is able to tackle several difficulties encountered by non-codebook TPC techniques. Channel State Information (CSI) is required at the receiver only, and the index of the best codeword is easily identified and fed-back from receiver to transmitter with low rate message. Motivated by these advantages, this paper contributes to the codebook-based TPC in the following aspects. First, the paper presents a Factorized form of the Full-Combination (FC) codebook with phase rotation only, and shows that only four phases are needed, which significantly simplifies practical implementation. As a second contribution, the paper introduces two statistically filtered codebooks: Index-Filtration FC and Average-Filtration FC. These codebooks considerably reduce system complexity due to their small codebook size, while maintaining almost the same BER of the FC codebook. As a third contribution, this paper proposes a new codeword selection criteria based on Long term statistics of the CSI (LCSI), instead of the conventional criteria using instantaneous CSI. This reduces the feedback rate of the index of the selected codeword while providing performance improvement over non-precoded SSK. Simulation results show the effectiveness of the proposed codebooks and the performance improvement with LCSI selection criteria.

A Generalized Transmit and Receive Diversity Condition for Feedback-Assisted MIMO Systems: Theory and Applications in Full-Duplex Spatial Modulation

It is widely exploited that the feedback-assisted multiple-input multiple-output (MIMO) systems, which rely on channel state information (CSI) at the transmitter not only improve the spectral efficiency but also increase the attainable diversity gains. Owing to the limited bandwidth of the feedback channel, it is impractical to feed back perfect CSI or the transmit precoding (TPC) matrix to be used by the transmitter. This issue has been studied for over a decade now and it is addressed by feeding the TPC codeword index back to the transmitter. In this paper, we derive the conditions to be satisfied by the transmit and receive codebooks (TCBs and RCBs) for achieving full transmit and receive diversity gains. Furthermore, based on the conditions derived, we propose several RCBs by exploiting the properties of circulant matrices constructed with the aid of Cyclotomic polynomials. The proposed RCBs are shown to offer several benefits when employed in full-duplex (FD) spatial modulation (SM) systems, which include: 1) reduced hardware complexity of the self-interference (SI) cancellation circuitry, 2) robustness to SI, 3) maintain the diversity gain in the face of strong line-of-sight (LoS) channels. Furthermore, we study the performance of the proposed RCBs in an emerging drone communication scenario where several drones act as FD relays. Our simulation results show that the proposed RCBs indeed do attain the diversity gains predicted by our theoretical results.

Limited-Feedback Resource Allocation in Heterogeneous Cellular Networks

In this paper, we develop and analyze three limited-feedback resource allocation algorithms suitable for uplink transmission in heterogeneous wireless networks (HetNets). In this setup, one macro-cell shares the spectrum with a set of underlay cognitive small-cells via the orthogonal frequency-division multiple access (OFDMA), where the interference from small-cells to the macro-cell should be kept below a predefined threshold. The resource allocation algorithms aim to maximize the weighted sum of instantaneous rates of all users over all cells by jointly optimizing power and subcarrier allocation under power constraints. Since in practice, the HetNet backhaul capacity is limited, reducing the amount of channel state information (CSI) feedback signaling passed over the backhaul links is highly desirable. To reach this goal, we apply the Lloyd algorithm to develop the limited-feedback two-phase resource allocation scheme. In the first offline phase , an optimal codebook for power and subcarrier allocation is designed and sent to all nodes. In the second online phase , based on channel realizations, the appropriate codeword of the designed codebook is chosen for transmission parameters, and the macro-cell only sends the codeword index represented by a limited number of bits for subcarrier and power allocation to its own users and small-cells. Then, each small-cell informs its own users by their related codewords. The offline phase involves a mixed-integer nonconvex resource allocation problem encountering high computational complexity. To solve it efficiently, we apply the general iterative successive convex approximation (SCA) approach, where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. The simulation results reveal that the Lloyd algorithm can offer a performance close to the perfect-CSI case (without a limited number of feedback bits).

Texture Classification Using Local Pattern Based on Vector Quantization.

Local binary pattern (LBP) is a simple and effective descriptor for texture classification. However, it has two main disadvantages: (1) different structural patterns sometimes have the same binary code and (2) it is sensitive to noise. In order to overcome these disadvantages, we propose a new local descriptor named local vector quantization pattern (LVQP). In LVQP, different kinds of texture images are chosen to train a local pattern codebook, where each different structural pattern is described by a unique codeword index. Contrarily to the original LBP and its many variants, LVQP does not quantize each neighborhood pixel separately to 0/1, but aims at quantizing the whole difference vector between the central pixel and its neighborhood pixels. Since LVQP deals with the structural pattern as a whole, it has a high discriminability and is less sensitive to noise. Our experimental results, achieved by using four representative texture databases of Outex, UIUC, CUReT, and Brodatz, show that the proposed LVQP method can improve classification accuracy significantly and is more robust to noise.

Design Guidelines for Limited Feedback in the Spatially Correlated Broadcast Channel

A linear beamformer design for the broadcast channel where the base station is equipped with $N_t$ antennas and signals to $M = 2$ users (each with a single antenna)and where the vector channels are spatially correlated is considered. This problem is of relevance in wireless standardization efforts where two users are simultaneously scheduled (instead of the theoretically feasible $N_t$ user scheduling)to minimize signaling overhead. The users are assumed to have perfect channel state information (CSI), whereas the base station has statistical information of the channels. In the first part of this work, the role of the relevance of feedback is studied via the quantification of the gap in ergodic sum-rate between the perfect CSI and statistics-only extremes. In this direction, the importance of orthogonality of the dominant eigenmodes of the two users in maximizing the gap is established. In the second part of this work, in scenarios with a large gap, limited feedback beamforming codebooks and a codeword selection metric are designed for the low-rate feedback setting. The main contribution here is the proposal of a generalized eigenvector codebook and feedback of the codeword index that maximizes an estimate of the signal-to-interference-and-noise ratio $(\textsf{SINR})$ of each user. Extensions of this scheme are also proposed for the general $N_t$ antenna case with $M$ users, where $M \le N_t$ . It is shown via numerical studies that this scheme leads to significant performance improvement across a large family of channels over schemes such as Grassmannian/Random Vector Quantization/single-user codebooks with the channel projection metric for codeword selection.

Optimized Cartesian K-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named optimized cartesian K-means (ock-means), to better encode the data points for more accurate approximate nearest neighbor search. In ock-means, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life data sets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.

Limited Feedback for 3D Massive MIMO under 3D-UMa and 3D-UMi Scenarios

For three-dimensional (3D) massive MIMO utilizing the uniform rectangular array (URA) in the base station (BS), we propose a limited feedback transmission scheme in which the channel state information (CSI) feedback operations for horizontal domain and vertical domain are separate. Compared to the traditional feedback scheme, the scheme can reduce the feedback overhead, code word index search complexity, and storage requirement. Also, based on the zenith of departure angle (ZoD) distribution in 3D-Urban Macro Cell (3D-UMa) and 3D-Urban Micro Cell (3D-UMi) scenarios, we propose the angle quantization codebook for vertical domain, while the codebook of long term evolution-advanced (LTE-Advanced) is still adopted in horizontal domain to preserve compatibility with the LTE-Advanced. Based on the angle quantization codebook, the subsampled 3-bit DFT codebook is designed for vertical domain. The system-level simulation results reveal that, to compromise the feedback overhead and system performance, 2-bit codebook for 3D-UMa scenario and 3-bit codebook for 3D-UMi scenario can meet requirements in vertical domain. The feedback period for vertical domain can also be extended appropriately to reduce the feedback overhead.

Optimized K-Means Hashing for Approximate Nearest Neighbor Search

Hashing which maps data into binary codes in Hamming space has attracted more and more attentions for approximate nearest neighbor search due to its high efficiency and reduced storage cost. K-means hashing (KH) is a novel hashing method which firstly quantizes the data by codewords and then uses the indices of codewords to encode the data. However, in KH, only the codewords are updated to minimize the quantization error and affinity error while the indices of codewords remain the same after they are initialized. In this paper, we propose an optimized k-means hashing (OKH) method to encode data by binary codes. In our method, we simultaneously optimize the codewords and the indices of them to minimize the quantization error and the affinity error. Our OKH method can find both the optimal codewords and the optiaml indices, and the resulting binary codes in Hamming space can better preserve the original neighborhood structure of the data. Besides, OKH can further be generalized to a product space. Extensive experiments have verified the superiority of OKH over KH and other state-of-the-art hashing methods.

What should be fed back for per‐cell codebook‐based limited feedback coordinated multi‐point systems?

Per‐cell codebook‐based limited feedback is highly desirable in coordinated multi‐point (CoMP) systems owing to flexibility and compatibility. Except for the per‐cell codeword indices, each user should feed back other information for the central unit to reconstruct global channel direction information (CDI) and estimate channel quality information (CQI) for downlink transmission. In this paper, we analyze the essential feedback information for per‐cell codebook‐based coherent CoMP systems. To show what should be fed back to estimate the CQI, we first derive a conservative CQI estimation to avoid the outage in transmission, which depends on the quantization accuracy and the norm of the global channel. We then analyze whether the per‐cell small‐scale fading channel norms are necessary to feed back in obtaining the global channel norm and reconstructing the global CDI. Next, we analyze whether the feedback of phase ambiguity (PA) is necessary. Both the analysis and simulation results show that the global channel norm should be fed back instead of the per‐cell channel norms. The benefit of the PA feedback is evident only when the number of base stations is large and the number of antennas at each base station is small.

Adaptive embedding techniques for VQ-compressed images

An embedding algorithm, which can adaptively embed a binary message into a VQ-compressed image, is proposed in this paper. The proposed algorithm is divided into three phases. In the codeword grouping phase, a new group of codewords is initiated by the two most similar codewords which do not belong to any group. For each codeword which does not belong to any group, if the codeword is similar to all of the codewords in the group, it will be added to the group. In the embedding phase, each codeword in a group will be assigned to embed a certain sub-message whose length is determined by the number of codewords in the group. The more codewords a group has, the higher the embedding capacity of a codeword in the group will be. In the extracting phase, given a codeword and the number of codewords in the group to which the codeword belongs, the embedded message can be extracted from the codeword by simply determining the order of the codeword in the group. Experimental results show that the proposed algorithm performs better than previous algorithms with regard to embedding capacity and image quality. For the test images, when the embedding capacity is less than 5 bits per codeword index, the difference of the PSNR values between the stego-image and its VQ-compressed cover image will be no more than 5 dB on average.

Closed-loop Grouped Space–Time Block Code: Encoding, Decoding and Codeword Selection

A design of closed-loop grouped space---time block codes (G-STBCs) including encoding, decoding and codeword selection is proposed for the downlink over Rayleigh flat-fading channels. In particular, at the transmitter, the antenna array is partitioned into a number of groups, each of which is encoded based on the orthogonal STBC (O-STBC). At the receiver, by exploiting the algebraic structure of orthogonal codes, a low-complexity, in recursion form, group-wise ordered successive interference cancellation (OSIC) detector is developed. Moreover, the G-STBC codeword is designed and a G-STBC codeword selection criterion that minimizes the BER performance under the constraints of a fixed spectral efficiency and total transmit power is then proposed. The selection index of the G-STBC codeword and the associated modulation type are determined at the receiver and conveyed to the transmitter with a limited feedback overhead to choose an appropriate mode for transmission. Finally, Numerical examples are used for illustrating the performance of the proposed G-STBCs, OSIC based detection and G-STBC codeword selection criterion.

Linear Dispersion Codes with Limited Feedback

LDCs system with finite-rate error-free feedback is proposed in this letter. The optimal transmission codeword is selected at the receiver and the codeword index is sent to the transmitter. A simple random search algorithm is introduced for codebook generation. Moreover, the max-min singular value criterion is adopted for codeword selection. Simulation results showed that, with only 3-4 feedback bits, the low-complexity Zero-Forcing receiver can approach the Maximum-Likelihood (ML) performance.

Multiple Antenna Broadcast Channels With Shape Feedback and Limited Feedback

In this paper, we consider two different models of partial channel state information at the base station transmitter (CSIT) for multiple antenna broadcast channels: 1) the shape feedback model where the normalized channel vector of each user is available at the base station and 2) the limited feedback model where each user quantizes its channel vector according to a rotated codebook that is optimal in the sense of mean squared error and feeds back the codeword index. This paper is focused on characterizing the sum rate performance of both zero-forcing dirty paper coding (ZFDPC) systems and channel inversion (CI) systems under the given two partial CSIT models. Intuitively speaking, a system with shape feedback loses the sum rate gain of adaptive power allocation. However, shape feedback still provides enough channel knowledge for ZFDPC and CI to approach their own optimal throughput in the high signal-to-noise ratio (SNR) regime. As for limited feedback, we derive sum rate bounds for both signaling schemes and link their throughput performance to some basic properties of the quantization codebook. Interestingly, we find that limited feedback employing a fixed codebook leads to a sum rate ceiling for both schemes for asymptotically high SNR.

3D Model Retrieval Based on Vector Quantisation Index Histograms

This paper proposes a novel technique to retrieval 3D mesh models using vector quantisation index histograms. Firstly, points are sampled uniformly on mesh surface. Secondly, to a point five features representing global and local properties are extracted. Thus feature vectors of points are obtained. Third, we select several models from each class, and employ their feature vectors as a training set. After training using LBG algorithm, a public codebook is constructed. Next, codeword index histograms of the query model and those in database are computed. The last step is to compute the distance between histograms of the query and those of the models in database. Experimental results show the effectiveness of our method.

A VQ-Based Approach to Thermal Image Analysis for Printed Circuit Boards Diagnosis

This paper proposes a novel method to analyze the thermal image of a printed circuit board (PCB) for fault detection. In this method, a gold thermal image is first generated from the thermal images of the PCB in normal operation, and then compressed into a codebook with a certain number of codewords. Each codeword represents a block of image size four by four. Each block in the thermal image for the board under test (BUT) is then encoded in the same way. The codewords in the codebook are arranged in ascending order with respect to their mean values. Any abnormal functional block in BUT can be identified by comparing the codeword index with that of the corresponding block in the gold thermal image. The memory size for storing the templates for comparison is, thus, significantly reduced without diagnosis performance degradation. Also, there is not a necessity for feature extraction such as the feature-based diagnostic methods. In addition, an adaptive threshold criterion is proposed to improve the detection sensitivity. From the experimental results, this proposed method is demonstrated to be very effective in abnormal functional block identification for PCBs based on the thermal image. Furthermore, this method is highly modularized for hardware implementation and parallel realization to speed up the processing time.

The index entropy of a mismatched codebook

Entropy coding is a well-known technique to reduce the rate of a quantizer. It plays a particularly important role in universal quantization, where the quantizer codebook is not matched to the source statistics. We investigate the gain due to entropy coding by considering the entropy of the index of the first codeword, in a mismatched random codebook, that D-matches the source word. We show that the index entropy is strictly lower than the "uncoded" rate of the code, provided that the entropy is conditioned on the codebook. The number of bits saved by conditional entropy coding is equal to the divergence between the "favorite type" (the limiting empirical distribution of the first D-matching codeword) and the codebook-generating distribution. Specific examples are provided.