Wyner-Ziv Coding Research Articles

A C-RAN Feasibility Study

The number of mobile phone users today is growing dramatically, which will cause network jamming and require a substantial amount of bandwidth. Cloud radio access network (C-RAN) is thus one of the interesting technology alternatives to solve this issue. C-RAN communicates with users using dispersed Remote Radio Heads (RRHs), each of which has one baseband unit (BBU). RRH performs soft relaying and is connected to the BBU via high capability, low latency front-haul lines. Due to the exchange of raw I/Q samples between the RRHs and the BBU, the C-RAN architecture places a restriction on front-haulfront-haul bandwidth. In BBU, many methods, including joint decompression and decoding (JDD) and wyner-ziv coding, are utilised to increase capacity.

Privacy, Secrecy, and Storage With Nested Randomized Polar Subcode Constructions

We consider a set of security and privacy problems under reliability and storage constraints that can be tackled by using codes and particularly focus on the secret-key agreement problem. Polar subcodes (PSCs) are polar codes (PCs) with dynamically-frozen symbols and have a larger code minimum distance than PCs with only statically-frozen symbols. A randomized nested PSC construction, where the low-rate code is a PSC and the high-rate code is a PC, is proposed for successive cancellation list (SCL) and sequential decoders. This code construction aims to perform lossy compression with side information, i.e., Wyner-Ziv (WZ) coding. Nested PSCs are used in the key agreement problem with physical identifiers and two terminals since WZ-coding constructions significantly improve on Slepian-Wolf coding constructions such as fuzzy extractors. Significant gains in terms of the secret-key vs. storage rate ratio as compared to nested PCs with the same list sizes are illustrated to show that nested PSCs significantly improve on all existing code constructions. The performance of the nested PSCs is shown to improve with larger list sizes, unlike the nested PCs considered. A design procedure to efficiently construct nested PSCs and possible improvements to the nested PSC designs are also provided.

A Low-Complexity and Adaptive Distributed Source Coding Design for Model Aggregation in Distributed Learning

A major bottleneck in distributed learning is the communication overhead of exchanging intermediate model update parameters between the worker nodes and the parameter server. Recently, it is found that local gradients among different worker nodes are correlated. Therefore, distributed source coding (DSC) can be applied to increase communication efficiency by exploiting such correlation. However, it is highly non-trivial to exploite the gradient correlations in distributed learning due to the unknown and time-varying gradient correlation. In this paper, we first propose a DSC framework, named successive Wyner-Ziv coding, for distributed learning based on quantization and Slepian-Wolf (SW) coding. We prove that the proposed framework can achieve the theoretically minimum communication cost from an information theory perspective. We also propose a low-complexity and adaptive DSC for distributed learning, including a gradient statistics estimator, rate controller, and a log-likelihood ratio (LLR) computer. The gradient statistics estimator estimates the gradient statistics online based only on the quantized gradients at previous iterations, hence it does not introduce extra communication cost. The computation complexity of the rate controller and the LLR computer is reduced to a linear growth in the number of worker nodes by introducing a semi-analytical Monte Carlo simulation. Finally, we design a DSC-based distributed learning process and find that the extra delay introduced by DSC does not scale with the number of worker nodes.

Fronthaul Compression and Beamforming Optimization for Uplink C-RAN With Intelligent Reflecting Surface-Enhanced Wireless Fronthauling

This letter studies a joint design of transmit beamforming and fronthaul compression for the uplink cloud radio access network (C-RAN) with intelligent reflecting surface (IRS) aided wireless fronthauling. In C-RAN, a number of users communicate with baseband unit (BBU) pool through multiple remote radio heads (RRH), wherein RRHs compress the received signals by Wyner-Ziv (WZ) coding and forward the quantization bits to BBU pool through wireless fronthaul link. With the goal of maximizing the uplink sum rate, an alternating algorithm is proposed for jointly optimizing the fronthaul quantization noise covariance matrices, the passive beamformer of IRS and the transmit beamformers of users and RRHs. Via numerical results, the effectiveness of the proposed joint design is verified.

An Energy Efficient OFDM–MIMO Systems for Multimedia Data Transmission Based on Hybrid Fuzzy Approach

In a 4G cellular communication system, energy efficiency and power consumption are the key parameters in evaluating and designing communication system in multimedia. In this paper, an efficient model with high energy is proposed for multiple input multiple output orthogonal frequency division multiplexing (MIMO–OFDM) mobile multimedia systems with less power consumption. Providing high-speed video coding technique, which is designed to substantially improve coding efficiency compared with other coding techniques. Next to that erasures coding and Wyner–Ziv coding is used for encoding and compression process. Finally, efficient energy based optimized power allocation with hybrid Fuzzy Grey Wolf Optimization (GWO) algorithm is presented to reduce the power consumption of MIMO–OFDM mobile multimedia communication systems. The value of power which is consumed at the 30th iteration is 20 W and the energy efficiency is 92% for the 20 W power. Experimental results show that the proposed hybrid Fuzzy GWO algorithm can assure the required service with maximum efficiency in MIMO–OFDM.

Code Constructions for Physical Unclonable Functions and Biometric Secrecy Systems

The two-terminal key agreement problem with biometric or physical identifiers is considered. Two linear code constructions based on Wyner-Ziv coding are developed. The first construction uses random linear codes and achieves all points of the key-leakage-storage regions of the generated-secret and chosen-secret models. The second construction uses nested polar codes for vector quantization during enrollment and for error correction during reconstruction. The simulation results show that the nested polar codes achieve privacy leakage and storage rates that improve on existing code designs. One proposed code achieves a rate tuple that cannot be achieved by existing methods.

Successive Wyner-Ziv Coding for the Binary CEO Problem Under Logarithmic Loss

The $L$-link binary Chief Executive Officer (CEO) problem under logarithmic loss is investigated in this paper. A quantization splitting technique is applied to convert the problem under consideration to a $(2L-1)$-step successive Wyner-Ziv (WZ) problem, for which a practical coding scheme is proposed. In the proposed scheme, low-density generator-matrix (LDGM) codes are used for binary quantization while low-density parity-check (LDPC) codes are used for syndrome generation; the decoder performs successive decoding based on the received syndromes and produces a soft reconstruction of the remote source. The simulation results indicate that the rate-distortion performance of the proposed scheme can approach the theoretical inner bound based on binary-symmetric test-channel models.

A New Coding Paradigm for the Primitive Relay Channel

We consider the primitive relay channel, where the source sends a message to the relay and to the destination, and the relay helps the communication by transmitting an additional message to the destination via a separate channel. Two well-known coding techniques have been introduced for this setting: decode-and-forward and compress-and-forward. In decode-and-forward, the relay completely decodes the message and sends some information to the destination; in compress-and-forward, the relay does not decode, and it sends a compressed version of the received signal to the destination using Wyner–Ziv coding. In this paper, we present a novel coding paradigm that provides an improved achievable rate for the primitive relay channel. The idea is to combine compress-and-forward and decode-and-forward via a chaining construction. We transmit over pairs of blocks: in the first block, we use compress-and-forward; and, in the second block, we use decode-and-forward. More specifically, in the first block, the relay does not decode, it compresses the received signal via Wyner–Ziv, and it sends only part of the compression to the destination. In the second block, the relay completely decodes the message, it sends some information to the destination, and it also sends the remaining part of the compression coming from the first block. By doing so, we are able to strictly outperform both compress-and-forward and decode-and-forward. Note that the proposed coding scheme can be implemented with polar codes. As such, it has the typical attractive properties of polar coding schemes, namely, quasi-linear encoding and decoding complexity, and error probability that decays at super-polynomial speed. As a running example, we take into account the special case of the erasure relay channel, and we provide a comparison between the rates achievable by our proposed scheme and the existing upper and lower bounds.

Location and Energy Saving of Underwater Sensor Network for Monitoring Seabed Landslide

Xu, Y.; Shan, H., and Jia, Y., 2018. Location and energy saving of underwater sensor network for monitoring seabed landslide. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 29–34. Coconut Creek (Florida), ISSN 0749-0208.The traditional positioning system of underwater sensor network for monitoring submarine landslide could complete the location of submarine landslide, but the positioning offset was large and it was unable to work for a long time. Therefore, the research on positioning and energy saving of underwater sensor network for monitoring submarine landslide was proposed. The gradient of water depth was calculated based on multibeam bathymetric data, and the gradient change was recognized. Combined with topography features, the scarp of submarine landslide was recognized, and the identification of position of submarine landslides was determined. Based on SLMP positioning algorithm, the mechanism of UPS framework was used to realize the accurate coordinate positioning of submarine landslide. DCT transform, LDPC coder-decoder and Key frame coder-decoder of encoding algorithm of underwater sensor were used to transform Wyner-Ziv domain code and thus to realize energy-saving operation of underwater sensor network monitoring. Experiment proves that the positioning accuracy of method in this paper is improved by 58.33%, and the energy can be saved more than 55.75%. Meanwhile, it has high efficiency.

Joint Optimization of Fronthaul Compression and Bandwidth Allocation in Uplink H-CRAN With Large System Analysis

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding. Different decoding strategies are also considered. This work aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem. In summary, this work provides an economic solution to the challenge of constrained fronthaul capacity, and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

Light-Weight Video Codec at Terminal for Video Coding in the Cloud

This paper proposes a new video transmission strategy that effectively transfers the computational complexity of video coding from the terminal to cloud environment, and this network architecture can generate an observably result: Light-weight Video Coding (LVC) codec at the terminal. The proposed network architecture effectively integrates Wyner-Ziv Video Coding (WZVC), which develops to construct a simple encoder in the uplink. And also we use Decoding-Friendly Encoder Design (DFED) in the downlink, which implements a decoder is lower complexity than the existing decoders. The experimental results show that the computational loading of the encoder is lower than that of H.264/AVC intra-frame coding. Meanwhile, the proposed WZVC architecture shows better performance than the state-of-the-art WZVC (DISCOVER) by maximum up to 4 dB. Moreover, DFED decreases a maximum 2% of the computational loading at the decoder, as compared with traditional H.264/AVC video decoding.

Lattice Codes Achieve the Capacity of Common Message Gaussian Broadcast Channels With Coded Side Information

Lattices possess elegant mathematical properties which have been previously used in the literature to show that structured codes can be efficient in a variety of communication scenarios, including coding for the additive white Gaussian noise channel, dirty-paper channel, Wyner–Ziv coding, coding for relay networks, and so forth. We consider the family of single-transmitter multiple-receiver Gaussian channels, where the source transmits a set of common messages to all the receivers (multicast scenario), and each receiver has coded side information , i.e., prior information in the form of linear combinations of the messages. This channel model is motivated by applications to multi-terminal networks, where the nodes may have access to coded versions of the messages from previous signal hops or through orthogonal channels. The capacity of this channel is known and follows from the work of Tuncel (2006), which is based on random coding arguments. In this paper, following the approach of Erez and Zamir, we design lattice codes for this family of channels when the source messages are symbols from a finite field $\mathbb {F}_{p}$ of prime size. Our coding scheme utilizes Construction A lattices designed over the same prime field $\mathbb {F}_{p}$ , and uses algebraic binning at the decoders to expurgate the channel code and obtain good lattice subcodes, for every possible set of linear combinations available as side information. The achievable rate of our coding scheme is a function of the size $p$ of underlying prime field, and approaches the capacity as $p$ tends to infinity.

Binary Wyner–Ziv code design based on compound LDGM–LDPC structures

In this study, a practical coding scheme is designed for the binary Wyner–Ziv (WZ) problem by using nested low-density generator-matrix (LDGM) and low-density parity-check (LDPC) codes. This scheme contains two steps in the encoding procedure. The first step involves applying the binary quantisation by employing LDGM codes and the second one is using the syndrome-coding technique by utilising LDPC codes. The decoding algorithm of the proposed scheme is based on the sum-product algorithm with the help of a side information available at the decoder side. It is theoretically shown that the compound structure has the capability of achieving the WZ bound. The proposed method approaches this bound by utilising the iterative message-passing algorithms in both encoding and decoding, although theoretical results show that it is asymptotically achievable.

Robust distributed video coding for wireless multimedia sensor networks

Coding complexity and error-resilience are the two key factors for video streaming in Wireless Multimedia Sensor Networks (WMSNs). Towards this objective, this paper proposes a Robust Distributed Video Coding (RDVC) framework to optimize the quality of video transmission for WMSNs. A new error-resilient Key frame coding scheme is proposed based on the protection of Wyner-Ziv Coding (WZC), in which additional Wyner-Ziv bits play a significant role for better error resilience and better Rate/Distortion (RD) performance. In addition, following the theoretical background of Distributed Joint Source Channel Coding (DJSCC), a novel distributed source channel codec based on Group Puncture Rate Adaptive IRA code (GPRA-IRA) is proposed, upon which the error-resilient Wyner-Ziv codec is designed. These experimental results show that the proposed RDVC scheme outperforms the relevant coding in terms of RD performance, notably by up to 1-3 dB, while achieving a lower encoding complexity.

副情報に遅延が生じる場合のWyner-Ziv符号化問題

副情報に遅延が生じる場合のWyner-Ziv符号化問題

On the Rate-Distortion Function for Binary Source Coding With Side Information

We present an in-depth analysis of the problem of lossy compression of binary sources in the presence of correlated side information, where the correlation is given by a generic binary asymmetric channel and the Hamming distance is the distortion metric. Our analysis is motivated by systematic rate-distortion gains observed when applying asymmetric correlation models in Wyner-Ziv video coding. First, we derive for the first time the rate-distortion function for conventional predictive coding in the binary-asymmetric-correlation-channel scenario. Second, we propose a new bound for the case where the side information is only available at the decoder—Wyner-Ziv coding. We conjecture this bound to be tight. We show that the maximum rate needed to encode as well as the maximum rate-loss of Wyner-Ziv coding relative to predictive coding corresponds to uniform sources and symmetric correlations. Importantly, we show that the upper bound on the rate-loss established by Zamir is not tight and that the maximum value is actually significantly lower. Moreover, we prove that the only binary correlation channel that incurs no rate-loss for Wyner-Ziv coding compared with predictive coding is the Z-channel. Finally, we complement our analysis with new compression performance results obtained with our state-of-the-art Wyner-Ziv video coding system.

Lossy Compression for Compute-and-Forward in Limited Backhaul Uplink Multicell Processing

We study the transmission over a cloud radio access network in which multiple base stations, acting as relay nodes, are connected to a central processor (CP) via error-free rate-limited backhaul links. We propose two lattice-based coding schemes. In the first scheme, each relay node decodes linear combinations of the users’ messages in the spirit of compute-and-forward (CoF), but departs from it essentially in that the decoded equations are remapped to equations on the users’ input symbols, sent compressed in a lossy manner to the CP, and are not required to be linearly independent. The compression accounts for the correlation between equations at the relay nodes through Wyner–Ziv coding. Also, by opposition to the standard CoF, an appropriate multi-user decoder is utilized to recover the sent messages. The second scheme generalizes the first one by also allowing, at each relay node, a joint compression of the decoded equation on the users’ input symbols and the received signal. Both schemes apply in general, but are especially suited for situations in which there are more users that relays. We show that both schemes can outperform standard CoF and successive Wyner–Ziv schemes in certain regimes, and illustrate the gains through some numerical examples.

On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloud-computing based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Further, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

Source-Interference Recovery Over Broadcast Channels: Asymptotic Bounds and Analog Codes

We consider the problem of joint recovery of a bivariate Gaussian source and of interference over the two-user Gaussian degraded broadcast channel in the presence of common interference. The interference, that is available non-causally at the encoder, is assumed to be Gaussian and correlated to the sources. The tradeoff between the distortion of the sources and the interference estimation error is studied; information-theoretic outer and inner bounds based on ideas from rate-distortion theory and hybrid coding are derived, respectively. More precisely, the outer bound is found by assuming additional knowledge at each user; the inner bound, however, is obtained by analyzing the distortion of a layered hybrid scheme based on proper power splitting, Costa and Wyner–Ziv coding. Low delay and complexity coding schemes based on analog mapping are next proposed. More specifically, parametric mappings based on linear and sawtooth curves are studied and optimized by minimizing an upper bound on the system’s distortion; nonparametric mappings based on joint optimization between the encoder and the decoder using an iterative algorithm are designed. Numerical results show that for the special cases that are previously considered by Abou Saleh et al. (with no fading), the derived outer bound is tighter and the proposed hybrid scheme has a lower complex structure with no loss in performance. In addition, the proposed low delay nonlinear schemes outperform the linear scheme and perform relatively close to the inner bound under certain system settings.

Duality of a Source Coding Problem and the Semi-Deterministic Broadcast Channel With Rate-Limited Cooperation

The Wyner-Ahlswede-K\"orner (WAK) empirical-coordination problem where the encoders cooperate via a finite-capacity one-sided link is considered. The coordination-capacity region is derived by combining several source coding techniques, such as Wyner-Ziv (WZ) coding, binning and superposition coding. Furthermore, a semi-deterministic (SD) broadcast channel (BC) with one-sided decoder cooperation is considered. Duality principles relating the two problems are presented, and the capacity region for the SD-BC setting is derived. The direct part follows from an achievable region for a general BC that is tight for the SD scenario. A converse is established by using telescoping identities. The SD-BC is shown to be operationally equivalent to a class of relay-BCs (RBCs) and the correspondence between their capacity regions is established. The capacity region of the SD-BC is transformed into an equivalent region that is shown to be dual to the admissible region of the WAK problem in the sense that the information measures defining the corner points of both regions coincide. Achievability and converse proofs for the equivalent region are provided. For the converse, we use a probabilistic construction of auxiliary random variables that depends on the distribution induced by the codebook. Several examples illustrate the results.