On performance analysis and implementation issues of iterative decoding for graph based codes

There is no doubt that long random-like code has the potential to achieve good performance because of its excellent distance spectrum. However, these codes remain useless in practical applications due to the lack of decoders rendering good performance at an acceptable complexity. The invention of turbo code marks a milestone progress in channel coding theory in that it achieves near Shannon limit performance by using an elegant iterative decoding algorithm. This great success stimulated intensive research oil long compound codes sharing the same decoding mechanism. Among these long codes are low-density parity-check (LDPC) code and product code, which render brilliant performance. In this work, iterative decoding algorithms for LDPC code and product code are studied in the context of belief propagation. A large part of this work concerns LDPC code. First the concept of iterative decoding capacity is established in the context of density evolution. Two simulation-based methods approximating decoding capacity are applied to LDPC code. Their effectiveness is evaluated. A suboptimal iterative decoder, Max-Log-MAP algorithm, is also investigated. It has been intensively studied in turbo code but seems to be neglected in LDPC code. The specific density evolution procedure for Max-Log-MAP decoding is developed. The performance of LDPC code with infinite block length is well-predicted using density evolution procedure. Two implementation issues on iterative decoding of LDPC code are studied. One is the design of a quantized decoder. The other is the influence of mismatched signal-to-noise ratio (SNR) level on decoding performance. The theoretical capacities of the quantized LDPC decoder, under Log-MAP and Max-Log-MAP algorithms, are derived through discretized density evolution. It is indicated that the key point in designing a quantized decoder is to pick a proper dynamic range. Quantization loss in terms of bit error rate (BER) performance could be kept remarkably low, provided that the dynamic range is chosen wisely. The decoding capacity under fixed SNR offset is obtained. The robustness of LDPC code with practical length is evaluated through simulations. It is found that the amount of SNR offset that can be tolerated depends on the code length. The remaining part of this dissertation deals with iterative decoding of product code. Two issues on iterative decoding of\u27 product code are investigated. One is, \u27improving BER performance by mitigating cycle effects. The other is, parallel decoding structure, which is conceptually better than serial decoding and yields lower decoding latency

Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

Extrinsic information modification in the turbo decoder by exploiting source redundancies for HEVC video transmitted over a mobile channel

An iterative turbo decoder-based cross layer error recovery scheme for compressed video is presented in this paper. The soft information exchanged between two convolutional decoders is reinforced both by channel coded parity and video compression syntactical information. An algorithm to identify the video frame boundaries in corrupted compressed sequences is formulated. This paper continues to propose algorithms to deduce the correct values for selected fields in the compressed stream. Modifying the turbo extrinsic information using these corrections acts as reinforcements in the turbo decoding iterative process. The optimal number of turbo iterations suitable for the proposed system model is derived using EXIT charts. Simulation results reveal that a transmission power saving of 2.28% can be achieved using the proposed methodology. Contrary to typical joint cross layer decoding schemes, the additional resource requirement is minimal, since the proposed decoding cycle does not involve the decompression function

20 years of turbo coding and energy-aware design guidelines for energy-constrained wireless applications

During the last two decades, wireless communication has been revolutionized by near-capacity error-correcting codes (ECCs), such as turbo codes (TCs), which offer a lower bit error ratio (BER) than their predecessors, without requiring an increased transmission energy consumption (EC). Hence, TCs have found widespread employment in spectrum-constrained wireless communication applications, such as cellular telephony, wireless local area network, and broadcast systems. Recently, however, TCs have also been considered for energy-constrained wireless communication applications, such as wireless sensor networks and the `Internet of Things.' In these applications, TCs may also be employed for reducing the required transmission EC, instead of improving the BER. However, TCs have relatively high computational complexities, and hence, the associated signal-processing-related ECs are not insignificant. Therefore, when parameterizing TCs for employment in energy-constrained applications, both the processing EC and the transmission EC must be jointly considered. In this tutorial, we investigate holistic design methodologies conceived for this purpose. We commence by introducing turbo coding in detail, highlighting the various parameters of TCs and characterizing their impact on the encoded bit rate, on the radio frequency bandwidth requirement, on the transmission EC and on the BER. Following this, energy-efficient TC decoder application-specific integrated circuit (ASIC) architecture designs are exemplified, and the processing EC is characterized as a function of the TC parameters. Finally, the TC parameters are selected in order to minimize the sum of the processing EC and the transmission EC

Decoding of Decode and Forward (DF) Relay Protocol using Min-Sum Based Low Density Parity Check (LDPC) System

Decoding high complexity is a major issue to design a decode and forward (DF) relay protocol. Thus, the establishment of low complexity decoding system would beneficial to assist decode and forward relay protocol. This paper reviews existing methods for the min-sum based LDPC decoding system as the low complexity decoding system. Reference lists of chosen articles were further reviewed for associated publications. This paper introduces comprehensive system model representing and describing the methods developed for LDPC based for DF relay protocol. It is consists of a number of components: (1) encoder and modulation at the source node, (2) demodulation, decoding, encoding and modulation at relay node, and (3) demodulation and decoding at the destination node. This paper also proposes a new taxonomy for min-sum based LDPC decoding techniques, highlights some of the most important components such as data used, result performances and profiles the Variable and Check Node (VCN) operation methods that have the potential to be used in DF relay protocol. Min-sum based LDPC decoding methods have the potential to provide an objective measure the best tradeoff between low complexities decoding process and the decoding error performance, and emerge as a cost-effective solution for practical application