Iterative Decoding and Turbo Equalization: The Z-Crease Phenomenon

Iterative probabilistic inference, popularly dubbed the soft-iterative paradigm, has found great use in a wide range of communication applications, including turbo decoding and turbo equalization. The classic approach of analyzing the iterative approach inevitably use the statistical and information-theoretical tools that bear ensemble-average flavors. This paper consider the per-block error rate performance, and analyzes it using nonlinear dynamical theory. By modeling the iterative processor as a nonlinear dynamical system, we report a universal "Z-crease phenomenon:" the zig-zag or up-and-down fluctuation -- rather than the monotonic decrease -- of the per-block errors, as the number of iteration increases. Using the turbo decoder as an example, we also report several interesting motion phenomenons which were not previously reported, and which appear to correspond well with the notion of "pseudo codewords" and "stopping/trapping sets." We further propose a heuristic stopping criterion to control Z-crease and identify the best iteration. Our stopping criterion is most useful for controlling the worst-case per-block errors, and helps to significantly reduce the average-iteration numbers.Comment: 6 page

Turbo Decoding and Detection for Wireless Applications

A historical perspective of turbo coding and turbo transceivers inspired by the generic turbo principles is provided, as it evolved from Shannon’s visionary predictions. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannon’s capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated spacetime and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted

A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems

In contrast to the classical cyclic prefix (CP)-OFDM, the time domain synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard interval (GI). Conventional channel estimation methods for TDS-OFDM are based on the exploitation of the PN sequence and consequently suffer from intersymbol interference (ISI). This paper proposes a novel dataaided channel estimation method which combines the channel estimates obtained from the PN sequence and, most importantly, additional channel estimates extracted from OFDM data symbols. Data-aided channel estimation is carried out using the rebuilt OFDM data symbols as virtual training sequences. In contrast to the classical turbo channel estimation, interleaving and decoding functions are not included in the feedback loop when rebuilding OFDM data symbols thereby reducing the complexity. Several improved techniques are proposed to refine the data-aided channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving average and Wiener filtering. Finally, the MMSE criteria is used to obtain the best combination results and an iterative process is proposed to progressively refine the estimation. Both MSE and BER simulations using specifications of the DTMB system are carried out to prove the effectiveness of the proposed algorithm even in very harsh channel conditions such as in the single frequency network (SFN) case

An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

In this work we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin

Adaptive OFDM System Design For Cognitive Radio

Recently, Cognitive Radio has been proposed as a promising technology to improve spectrum utilization. A highly flexible OFDM system is considered to be a good candidate for the Cognitive Radio baseband processing where individual carriers can be switched off for frequencies occupied by a licensed user. In order to support such an adaptive OFDM system, we propose a Multiprocessor System-on-Chip (MPSoC) architecture which can be dynamically reconfigured. However, the complexity and flexibility of the baseband processing makes the MPSoC design a difficult task. This paper presents a design technology for mapping flexible OFDM baseband for Cognitive Radio on a multiprocessor System-on-Chip (MPSoC)

Turbo Packet Combining Strategies for the MIMO-ISI ARQ Channel

This paper addresses the issue of efficient turbo packet combining techniques for coded transmission with a Chase-type automatic repeat request (ARQ) protocol operating over a multiple-input--multiple-output (MIMO) channel with intersymbol interference (ISI). First of all, we investigate the outage probability and the outage-based power loss of the MIMO-ISI ARQ channel when optimal maximum a posteriori (MAP) turbo packet combining is used at the receiver. We show that the ARQ delay (i.e., the maximum number of ARQ rounds) does not completely translate into a diversity gain. We then introduce two efficient turbo packet combining algorithms that are inspired by minimum mean square error (MMSE)-based turbo equalization techniques. Both schemes can be viewed as low-complexity versions of the optimal MAP turbo combiner. The first scheme is called signal-level turbo combining and performs packet combining and multiple transmission ISI cancellation jointly at the signal-level. The second scheme, called symbol-level turbo combining, allows ARQ rounds to be separately turbo equalized, while combining is performed at the filter output. We conduct a complexity analysis where we demonstrate that both algorithms have almost the same computational cost as the conventional log-likelihood ratio (LLR)-level combiner. Simulation results show that both proposed techniques outperform LLR-level combining, while for some representative MIMO configurations, signal-level combining has better ISI cancellation capability and achievable diversity order than that of symbol-level combining.Comment: 13 pages, 7 figures, and 2 table

Non-Orthogonal Narrowband Internet of Things: A Design for Saving Bandwidth and Doubling the Number of Connected Devices

IEEE Narrowband IoT (NB-IoT) is a low power wide area network (LPWAN) technique introduced in 3GPP release 13. The narrowband transmission scheme enables high capacity, wide coverage and low power consumption communications. With the increasing demand for services over the air, wireless spectrum is becoming scarce and new techniques are required to boost the number of connected devices within a limited spectral resource to meet the service requirements. This work provides a compressed signal waveform solution, termed fast-orthogonal frequency division multiplexing (Fast-OFDM), to double potentially the number of connected devices by compressing occupied bandwidth of each device without compromising data rate and bit error rate (BER) performance. Simulation is firstly evaluated for the Fast-OFDM with comparisons to single-carrier-frequency division multiple access (SC-FDMA). Results indicate the same performance for both systems in additive white Gaussian noise (AWGN) channel. Experimental measurements are also presented to show the bandwidth saving benefits of Fast-OFDM. It is shown that in a line-of-sight (LOS) scenario, Fast-OFDM has similar performance as SC-FDMA but with 50% bandwidth saving. This research paves the way for extended coverage, enhanced capacity and improved data rate of NB-IoT in 5th generation (5G) new radio (NR) networks