Soft-input Soft-output Research Articles

Low Complexity, Quasi-Optimal MIMO Detectors for Iterative Receivers

We propose a novel family of Soft-Input Soft-Output detectors for iterative, point-to-point, MIMO receivers. Compared to the optimal Maximum A Posteriori receiver, low complexity is achieved restricting the detector search to small subsets of the entire QAM hyper-symbol constellation, through simple criteria. These criteria are applied to an improved version of the non-iterative Layered ORthogonal lattice Detector. We show that, notwithstanding the suboptimal low-complexity implementation, this detector approaches the EXtrinsic Information Transfer of the MAP detector. Therefore, when included in an iterative receiver it delivers the same performance. Furthermore, the deterministic complexity and highly parallelizable structure of the proposed detector are well suited for HDL and ASIC implementation. To focus on a specific setting, we consider the indoor MIMO wireless LAN 802.11n standard, taking into account errors in Channel Estimation and a frequency selective, spatially correlated channel model.

A Scalable VLSI Architecture for Soft-Input Soft-Output Single Tree-Search Sphere Decoding

Multiple-input multiple-output (MIMO) wireless transmission imposes huge challenges on the design of efficient hardware architectures for iterative receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping, often approached by sphere decoding (SD). In this paper, we introduce the - to our best knowledge - first VLSI architecture for SISO SD applying a single tree-search approach. Compared with a soft-output-only base architecture similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the architectural modifications for soft input still allow a one-node-per-cycle execution. For a 4x4 16-QAM system, the area increases by 57% and the operating frequency degrades by 34% only.

Improving the structure of multiple dimension turbo codes using multiple identical component encoders

In this paper, we propose an improved structure for M-dimension(M ⩾ 2) turbo codes using M identical component encoders. The presented structure only applies single recursive convolutional encoder to encode M interleaved versions of information sequence and single tail sequence to operate termination. For the commonly used two-dimension (M = 2) turbo codes, two cascade soft input soft output (SISO) decoders are detailed to decode the corresponding codes. With the two cascade SISO decoders, this coding structure obviously outperforms traditional turbo codes at high code rates, especially, about 0.3 dB gain can be obtained at code rate 0.9 in additive white Gaussian noise channel with quadrature phase shift keying modulation. This makes the proposed coding structure very attractive for future radio communication systems with high throughput requirements.

Turbo equalization for doubly-selective fading channels using nonlinear kalman filtering and basis expansion models

We present a turbo (iterative) equalization receiver with fixed-lag nonlinear Kalman filtering for coded data transmission over doubly-selective channels. The proposed receiver exploits the complex exponential basis expansion model (CEBEM) for the overall channel variations, and an autoregressive (AR) model for the BEM coefficients. We extend an existing turbo equalization approach based on symbol-wise AR modeling of channels to channels based on BEM's. In the receiver an adaptive equalizer using nonlinear Kalman filters with delay is coupled with a soft-input soft-output (SISO) decoder to iteratively perform equalization and decoding. The adaptive equalizer jointly optimizes the estimates of the BEM coefficients and data symbols, thereby automatically accounting for correlation between data symbols and channel tap gains. An extrinsic information transfer (EXIT) chart analysis of the proposed approach is also presented. Simulation examples demonstrate that our CE-BEM-based approach significantly outperforms the existing symbol-wise AR model-based turbo equalizer.

A Flexible LDPC/Turbo Decoder Architecture

Low-density parity-check (LDPC) codes and convolutional Turbo codes are two of the most powerful error correcting codes that are widely used in modern communication systems. In a multi-mode baseband receiver, both LDPC and Turbo decoders may be required. However, the different decoding approaches for LDPC and Turbo codes usually lead to different hardware architectures. In this paper we propose a unified message passing algorithm for LDPC and Turbo codes and introduce a flexible soft-input soft-output (SISO) module to handle LDPC/Turbo decoding. We employ the trellis-based maximum a posteriori (MAP) algorithm as a bridge between LDPC and Turbo codes decoding. We view the LDPC code as a concatenation of n super-codes where each super-code has a simpler trellis structure so that the MAP algorithm can be easily applied to it. We propose a flexible functional unit (FFU) for MAP processing of LDPC and Turbo codes with a low hardware overhead (about 15% area and timing overhead). Based on the FFU, we propose an area-efficient flexible SISO decoder architecture to support LDPC/Turbo codes decoding. Multiple such SISO modules can be embedded into a parallel decoder for higher decoding throughput. As a case study, a flexible LDPC/Turbo decoder has been synthesized on a TSMC 90 nm CMOS technology with a core area of 3.2 mm2. The decoder can support IEEE 802.16e LDPC codes, IEEE 802.11n LDPC codes, and 3GPP LTE Turbo codes. Running at 500 MHz clock frequency, the decoder can sustain up to 600 Mbps LDPC decoding or 450 Mbps Turbo decoding.

Architecture and Implementation of a First-Generation Iterative Detection Read Channel

This paper explores the architecture of a first-generation iterative detection read channel solution that delivers up to an 8% increase in drive capacity compared to previous generation electronics. Using low density parity check (LDPC) coding, detected data is continually improved by a detection structure that implements multiple iterations through a set of soft-input soft-output (SISO) and message-passing (MP) blocks. A specially constructed LDPC code is used to optimize performance in the presence of inter-symbol interference (ISI). Also, Reed-Solomon error correction coding (ECC) is retained for optimal data integrity. A 65 nm technology is used in the implementation of this design.

A fixed-lag particle smoother for blind SISO equalization of time-varying channels

We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixed-lag smoothing distribution of the symbols under doubly-selective channels. We perform an exact evaluation of the optimal importance distribution, at a reduced computational cost when compared to other optimal solutions proposed for the same state-space model. The method is applied as a soft input-soft output (SISO) blind equalizer in a turbo receiver framework and simulation results are obtained to show its outstanding BER performance.

Improved SISO MMSE Detection for Joint Coded-Precoded OFDM under Imperfect Channel Estimation

An improved soft-input soft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation. Compared with the traditional mismatched detection which uses the channel estimate as its exact value, the signal model of the proposed detector is more accurate and the influence of channel estimation error (CEE) can be effectively mitigated. Simulations indicate that the proposed scheme can improve the bit error rate (BER) performance with fewer pilot symbols.

Unified scaling factor approach for turbo decoding algorithms

Turbo (iterative) decoding can be implemented with a large family of soft-input soft-output (SISO) algorithms, including suboptimal but practical algorithms such as the soft-output Viterbi algorithm (SOVA) and max-log-MAP algorithm. The performance with these practical algorithms can be improved with simple scaling factor methods. However, their theoretical analysis was mainly based on the relaxed assumption proposed by Papke et al. that the extrinsic information is Gaussian distributed. This study proposes a novel scaling factor approach for reducing both the overestimation of reliability values and the correlation between the intrinsic and extrinsic information. Explicit formulae for computing the scaling factors are derived based on mathematical statistics. A key difference compared to the scaling factor method of Papke et al. is that the proposed scaling factors can be computed off-line. The numerical results show that when implemented in the decoding algorithm of block component codes, the proposed scaling factor approach improves the performance of turbo decoding over additive white Gaussian noise and Rayleigh fading channels. It is superior than the method by Papke et al. in both performance and complexity.

Cooperative Network Coding with Soft Information Relaying in Two-way Relay Cchannels

Network coding in wireless network can improve network throughput by exploiting the broadcast nature of the wireless network. Taking into consideration the error prone nature of wireless networks, we investigate the design of network coding implemented in physical layer, and propose a cooperative network coding scheme with soft information forwarding (SIR), which combines soft network coding and distributed turbo coding scheme in two-way relay channels (TWRC) in this paper. In order to mitigate the error propagation effect due to the imperfect decoding at the relay, soft network coding is deployed in the proposed scheme, where soft-input soft-output (SISO) encoder and decoder for recursive systematic convolutional (RSC) codes are implemented. Moreover, the soft information of both systematic bits and redundancy parity bits are forwarded at the relay by using higher-order constellations to achieve diversity gain. Aided by distance spectrum of turbo codes, concept of uniform interleaver, and Gaussian approximation of soft decoding information, union bound on the error performance of our scheme is analyzed. Both the analysis and simulation confirm that the proposed scheme achieves significant gain over conventional network coding in quasi-static fading channels.

A novel turbo-MIMO transceiver

A novel BLAST transceiver named turbo-like BLAST (TLBLAST) for MIMO communications is proposed, which combines the characteristics of HBLAST and VBLAST with the structure of turbo encoder. The high data rate transmission can be implemented and in each transmitted antenna, different encode schemes can be used to supply different protection levels. The system performance is improved effectively through serially concatenating a soft input soft output (SISO) detector and decoder by iterative process with comparable complexity of VBLAST. Simulation results show that the performance of TLBLAST is better than HBLAST and VBLAST in Rayleigh flat fading channels.

Improved random redundant iterative HDPC decoding

An iterative algorithm for soft-input soft-output (SISO) decoding of classical algebraic cyclic block codes is presented below. Inspired by other approaches for high performance belief propagation (BP) decoding, this algorithm requires up to 10 times less computational complexity than other methods that achieve similar performance. By utilizing multiple BP decoders, and using random permutation taken from the permutation group of the code, this algorithm reaches near maximum likelihood performance. A computational complexity comparison of the proposed algorithm versus other methods is presented as well. This includes complexity versus performance analysis, allowing one to trade between the former and the latter, according to ones needs.

Turbo joint channel estimation, synchronization, and decoding for coded OFDM systems

This paper presents a turbo joint synchronization, channel estimation and decoding scheme for coded OFDM. In this proposed scheme, the estimator uses soft-decision information from the channel soft-input soft-output (SISO) decoder to produce better estimates of the unknown parameters, which will in turn help the channel SISO decoder to make more reliable decision. The whole process will be performed in an iterative manner and good system performance can be achieved with only a few iterations for moderate initial synchronization errors.

Superposition Coded Modulation With Peak-Power Limitation

We apply clipping to superposition coded modulation (SCM) systems to reduce the peak-to-average power ratio (PAPR) of the transmitted signal. The impact on performance is investigated by evaluating the mutual information driven by the induced peak-power-limited input signals. It is shown that the rate loss is marginal for moderate clipping thresholds if optimal encoding/decoding is used. This fact is confirmed in examples where capacity-approaching component codes are used together with the maximum <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> probability (MAP) detection. In order to reduce the detection complexity of SCM with a large number of layers, we develop a suboptimal soft compensation (SC) method that is combined with soft-input soft-output (SISO) decoding algorithms in an iterative manner. A variety of simulation results for additive white Gaussian noise (AWGN) and fading channels are presented. It is shown that with the proposed method, the effect of clipping can be efficiently compensated and a good tradeoff between PAPR and bit-error rate (BER) can be achieved. Comparisons with other coded modulation schemes demonstrate that SCM offers significant advantages for high-rate transmissions over fading channels.

Iterative joint source-channel decoding of VLC exploiting source semantics over realistic radio-mobile channels

Joint source-channel decoding of variable length codes (VLC) for image and video transmission over wireless links is a subject of increasing interest. This paper proposes an optimum decoder of VLC sequences which exploits inherent redundancy in source data, namely the VLC syntax and source constraints. The proposed decoder is able to deliver both Maximum Likelihood optimum hard-output and soft-output solutions. The complexity of the algorithm is analyzed and a reduced complexity version of this algorithm with a slightly sub-optimal performance is proposed. An iterative joint source-channel decoding system is designed for demonstrating the performance of the proposed soft-input soft-output (SISO) decoder. Realistic contexts have been used for simulations: typical video sequences and typical radiomobile channels.

Low Complexity Iterative ICI Cancellation and Equalization for OFDM Systems Over &lt;newline/&gt;Doubly Selective Channels

In this paper, a low complexity iterative intercarrier interference (ICI) cancellation and equalization technique is proposed for use in OFDM systems over doubly selective channels. In the iterative parallel interference cancellation/minimum mean square error (PIC/MMSE) detector has a high complexity and a restriction on the structure which can not remove the ICI in the initial stage. Therefore, an error propagation occurs due to the ICI regenerated by the incorrect output of soft-input soft-output (SISO) decoder. In order to reduce the error propagation, an MMSE detector based on the successive interference cancellation (SIC) is used in the initial stage. The low complexity MMSE detector is also derived to minimize the error propagation by quantifying the decision error before SISO decoding. In the first iteration, simulation results show that the proposed scheme outperforms the conventional PIC/MMSE scheme by about 3 dB at bit error rate (BER)=1 times 10-3 while maintaining the equivalent computational complexity. In the subsequent iteration, it is possible to cancel the ICI out in the received signals by the aid of soft log-likelihood ratio (LLR) fed from the SISO decoder. Converting the LLR to the decision error probability, the error covariance matrix is obtained more accurately. As a result, the error propagation can be effectively reduced by dealing with only the dominant components, when considering decision errors. Finally, simulation results show that the proposed scheme outperforms the conventional PIC/MMSE scheme.

A new framework for soft decision equalization in frequency selective MIMO channels

We introduce a novel framework for soft-input, soft-output (SISO) equalization in frequency selective multipleinput multiple-output (MIMO) channels based on the well-known belief propagation (BP) algorithm. As in the BP equalizer, we model the multipath channels using factor graphs (FGs) where the transmitted and received signals are represented by the function and variable nodes respectively. The edges connecting the function and variable nodes illustrate the dependencies of the multipath channel and soft decisions are developed by exchanging information on these edges iteratively. We incorporate powerful techniques such as groupwise iterative multiuser detection (IMUD), probabilistic data association (PDA) and sphere decoding (SD) in order to reduce the computational complexity of BP equalizer with relatively small degradation in performance. The computational complexity of this new reduced-complexity BP (RCBP) equalizer grows linearly with block size and memory length of the channel. The proposed framework has a flexible structure that allows for parallel as well as serial detection. We will illustrate through simulations that the RCBP equalizer can even handle overloaded scenarios where the channel matrix is rank deficient, and it can achieve excellent performance by applying iterative equalization using the low-density parity check codes (LDPC).

Double Turbo Equalization of Continuous Phase Modulation with Frequency Domain Processing

In this paper, a doubly-iterative linear receiver, equipped with a soft-information aided frequency domain minimum mean-squared error (MMSE) equalizer, is proposed for the combined equalization and decoding of coded continuous phase modulation (CPM) signals over long multipath fading channels. In the proposed receiver architecture, the front-end frequency domain equalizer (FDE) is followed by the soft-input, softoutput (SISO) CPM demodulator and channel decoder modules. The receiver employs double turbo processing by performing back-end demodulation/decoding iterations per each equalization iteration to improve the a priori information for the front-end FDE. As presented by the computational complexity analysis and simulations, this process provides not only a significant reduction in the overall computational complexity, but also a performance improvement over the previously proposed iterative and noniterative MMSE receivers.

Introducing skip mode in distributed video coding

Although it was proven in the 1970s already by Wyner and Ziv and Slepian and Wolf that, under certain conditions, the same rate–distortion boundaries exist for distributed video coding (DVC) systems as for traditional predicting systems, until now no practical DVC system has been developed that even comes close to the performance of state-of-the-art video codecs such as H.264/AVC in terms of rate–distortion. Some important factors for this are the lower accuracy of the motion estimation performed at the decoder, the inaccurate modeling of the correlation between the side information and the original frame, and the absence in most state-of-the-art DVC systems of anything conceptually similar to the notion of skipped macroblocks in predictive coding systems. This paper proposes an extension of a state-of-the-art transform domain residual DVC system with an implementation of skip mode. The skip mode has an impact at two different places: in the turbo decoder, more specifically the soft input, soft output (SISO) convolutional decoder, and in the puncturing of the parity bits. Results show average bitrate gains up to 39% (depending on the sequence) achieved by combining both approaches. Furthermore, a hybrid video codec is presented where the motion estimation task is shifted back to the encoder. This results in a drastic increase in encoder complexity, but also in a drastic performance gain in terms of rate–distortion, with average bitrate savings up to 60% relative to the distributed video codec. In the hybrid video codec, smaller but still important average bitrate gains are achieved by implementing skip mode: up to 24%.

Graph-Based Channel Detection for Multitrack Recording Channels

We propose a low complexity detection technique for multihead multitrack recording systems. By exploiting sparseness of two-dimensional partial response (PR) channels, we develop an algorithm which performs belief propagation (BP) over corresponding factor graphs. We consider the BP-based detector not only for partial response channels but also for more practical conventional media and bit-patterned media storage systems, with and without media noise. Compared to the maximum likelihood detector which has a prohibitively high complexity that is exponential with both the number of tracks and the number of intersymbol interference (ISI) taps, the proposed detector has a much lower complexity and a fast parallel structure. For the multitrack recording systems with PR equalization, the price is a small performance penalty (less than one dB if the intertrack interference (ITI) is not too high). Furthermore, since the algorithm is soft-input soft-output in nature, turbo equalization can be employed if there is an outer code. We show that a few turbo equalization iterations can provide significant performance improvement even when the ITI level is high.