Near-capacity dirty-paper code design : a source-channel coding approach

This paper examines near-capacity dirty-paper code designs based on source-channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat-accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity

Nested turbo codes for the costa problem

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively

Distributed Source Coding for Sensor Networks

n recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf pre-vious milestones in the information revolution. MIT Technology Review ranked wireless sensor networks that con-sist of many tiny, low-power and cheap wireless sensors as the number one emerging technology. Unlike PCs or the Internet, which are designed to support all types of applications, sensor networks are usually mission driven and application specific (be it detection of biological agents and toxic chemicals; environmental measure-ment of temperature, pressure and vibration; or real-time area video surveillance). Thus they must operate under a set of unique constraints and requirements. For example, in contrast to many other wireless devices (e.g., cellular phones, PDAs, and laptops), in which energy can be recharged from time to time, the energy provisioned for a wireless sensor node is not expected to be renewed throughout its mission. The limited amount of energy available to wireless sensors has a significant impact on all aspects of a wireless sensor network, from the amount of information that the node can process, to the volume of wireless communication it can carry across large distances. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies; it relies on many com-ponents working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies for sensor networks is distributed source coding (DSC), which refers to the compression of multiple correlated sensor out-puts [1]–[4] that do not communicate with each other (hence distributed coding). These sensors send their compressed outputs to a central point [e.g., the base station (BS)] for joint decoding.

Slepian-Wolf coded nested lattice quantization for Wyner-Ziv coding: High-rate performance analysis and code design

Nested lattice quantization provides a practical scheme for Wyner-Ziv coding. This paper examines the high-rate performance of nested lattice quantizers and gives the theoretical performance for general continuous sources. In the quadratic Gaussian case, as the rate increases, we observe an increasing gap between the performance of finite-dimensional nested lattice quantizers and the Wyner-Ziv distortion-rate function. We argue that this is because the boundary gain decreases as the rate of the nested lattice quantizers increases. To increase the boundary gain and ultimately boost the overall performance, a new practical Wyner-Ziv coding scheme called Slepian-Wolf coded nested lattice quantization (SWC-NQ) is proposed, where Slepian-Wolf coding is applied to the quantization indices of the source for the purpose of compression with side information at the decoder. Theoretical analysis shows that for the quadratic Gaussian case and at high rate, SWC-NQ performs the same as conventional entropy-coded lattice quantization with the side information available at both the encoder and the decoder. Furthermore, a non-linear minimum MSE estimator is introduced at the decoder, which is theoretically proven to degenerate to the linear minimum MSE estimator at high rate and experimentally shown to outperform the linear estimator at low rate. Practical designs of one- and two-dimensional nested lattice quantizers together with multi-level LDPC codes for Slepian-Wolf coding give performance close to the theoretical limits of SWC-NQ. I

Joint source-channel coding of binary sources with side information at the decoder using IRA codes

Abstract — We use systematic irregular repeat accumulate (IRA) codes as source-channel codes for the transmission of an equiprobable memoryless binary source with side information at the decoder. A special case of this problem is joint source-channel coding for a nonequiprobable memoryless binary source. The theoretical limits of this problem are given by combining the Slepian-Wolf theorem, the source entropy in the special case, with the channel capacity. The approach is based on viewing the correlation between the binary source output and the side information as a separate channel or an enhancement of the original channel. The joint source-channel encoding, decoding and code design procedures are explained in detail. The simulated performance results are better than the recently published solutions using turbo codes and very close to the theoretical limit. I

Source-channel approach to channel coding with side information

Code designs for channel coding with side information (CCSI) based on combined source-channel coding are disclosed. These code designs combine trellis-coded quantization (TCQ) with irregular repeat accumulate (IRA) codes. The EXIT chart technique is used for IRA channel code design (and especially for capacity-approaching IRA channel code design). We emphasize the role of strong source coding and endeavor to achieve as much granular gain as possible by using TCQ. These code designs synergistically combine TCQ with IRA codes. By bringing together TCQ and EXIT chart-based IRA code designs, we are able to approach the theoretical limit of dirty-paper coding.U

Data encoding and decoding using Slepian-Wolf coded nested quantization to achieve Wyner-Ziv coding

A system and method for realizing a Wyner-Ziv encoder may involve the following steps: (a) apply nested quantization to input data from an information source in order to generate intermediate data; and (b) encode the intermediate data using an asymmetric Slepian-Wolf encoder in order to generate compressed output data representing the input data. Similarly, a Wyner-Ziv decoder may be realized by: (1) applying an asymmetric Slepian-Wolf decoder to compressed input data using side information to generate intermediate values, and (b) jointly decoding the intermediate values using the side information to generate decompressed output data.U

Data encoding and decoding using Slepian-Wolf coded nested quantization to achieve Wyner-Ziv coding

A system and method for realizing a Wyner-Ziv encoder may involve the following steps: (a) apply nested quantization to input data from an information source in order to generate intermediate data; and (b) encode the intermediate data using an asymmetric Slepian-Wolf encoder in order to generate compressed output data representing the input data. Similarly, a Wyner-Ziv decoder may be realized by: (1) applying an asymmetric Slepian-Wolf decoder to compressed input data using side information to generate intermediate values, and (b) jointly decoding the intermediate values using the side information to generate decompressed output data.U

Data encoding and decoding using Slepian-Wolf coded nested quantization to achieve Wyner-Ziv coding

A system and method for realizing a Wyner-Ziv encoder may involve the following steps: (a) applying nested quantization to input data from an information source in order to generate intermediate data; and (b) encoding the intermediate data using an asymmetric Slepian-Wolf encoder in order to generate compressed output data representing the input data. Similarly, a Wyner-Ziv decoder may be realized by applying an asymmetric Slepian-Wolf decoder to compressed input data (representing samples of a first source) to obtain intermediate values, and then, jointly decoding the intermediate values using side information (samples of a second source having known correlation with respect to the first source).U

Source-channel approach to channel coding with side information

Code designs for channel coding with side information (CCSI) based on combined source-channel coding are disclosed. These code designs combine trellis-coded quantization (TCQ) with irregular repeat accumulate (IRA) codes. The EXIT chart technique is used for IRA channel code design (and especially for capacity-approaching IRA channel code design). We emphasize the role of strong source coding and endeavor to achieve as much granular gain as possible by using TCQ. These code designs synergistically combine TCQ with IRA codes. By bringing together TCQ and EXIT chart-based IRA code designs, we are able to approach the theoretical limit of dirty-paper coding.U