Soft-input Soft-output Channel Decoders Research Articles

Low-Complexity LMMSE–SIC Turbo Receiver for Continuous Phase Modulation, Based on a Multiaccess–Multipath Analogy

Low-complexity iterative reception of Continuous Phase Modulation (CPM) is addressed using Linear Minimum Mean-Square Error (LMMSE) filtering empowered by Soft Interference Cancellation (SIC). The transmitter emits a convolutionally encoded, true CPM signal with a constant envelope. The receiver exploits Laurent–Mengali–Morelli’s (LMM) decomposition to reduce the detection complexity, where the transmitted true CPM signal is approximated as a superposition of a small number of parallel Pulse Amplitude Modulated (PAM) and phase–encoded terms. Pulses and phase–encoded pseudo-symbols arising from the LMM decomposition are treated, respectively, as parallel Inter–Symbol Interference (ISI) channels and Multiple Access Interference (MAI) components. Hence the transmitted signal is regarded to inherently contain controlled ISI and MAI, which are equalized for at the receiver front-end using LMMSE–SIC filters. The receiver back-end comprises a serially concatenated turbo scheme encompassing a Soft Input–Soft Output (SISO) CPM detector and a SISO channel decoder. Simulations conducted on various CPM schemes show that the proposed receiver provides significant complexity reduction for a small performance penalty. Consequently, CPM waveforms with long pulses, which have traditionally been deemed infeasible due to the prohibitive complexity, can now be implemented employing the proposed receiver. Hence substantial spectral side-lobe compaction offered by long CPM pulses can be harnessed.

Improved BDFE Using A Priori Information for Turbo Equalization

Turbo equalization improves communication system performance by iteratively exchanging information between soft-input soft-output (SISO) equalizer and SISO channel decoder. The trellis-based maximum a posteriori probability (MAP) algorithm serves as the optimum SISO equalizer for turbo equalization. However, MAP algorithm is unsuitable for systems with large modulation constellation size and severe inter-symbol interference (ISI) due to its prohibitively high computational complexity. In this paper, an improved SISO block decision feedback equalizer (BDFE) is proposed for low complexity turbo equalization. Unlike other sub-optimum equalizers which perform symbol by symbol detection, the proposed equalizer generates the soft output for each data bit by collecting information from a sequence of samples as in MAP algorithm. The sequence-based equalization is enabled by using not only soft a priori input from channel decoder, but also hard a priori information obtained from BDFE in previous iteration. The combination of soft a priori information and hard a priori information renders better performance with less iterations compared to other sub-optimum algorithms. In addition, the computational complexity of the proposed algorithm is on the same order as conventional SISO BDFE algorithm, and is much lower compared to the trellis-based MAP algorithm.

Doubly Iterative Equalization of Continuous-Phase Modulation

In this paper, a doubly iterative receiver is proposed for joint turbo equalization, demodulation, and decoding of coded binary continuous-phase modulation (CPM) in multipath fading channels. The proposed receiver consists of three soft-input soft-output (SISO) blocks: a front-end soft-information-aided minimum mean square error (MMSE) equalizer followed by a CPM demodulator and a back-end channel decoder. The MMSE equalizer, combined with an a priori soft-interference canceler (SIC) and an a posteriori probability mapper, forms a SISO processor suitable for iterative processing that considers discrete-time CPM symbols which belong to a finite alphabet. The SISO CPM demodulator and the SISO channel decoder are both implemented by the a posteriori probability algorithm. The proposed doubly iterative receiver has a central demodulator coupled with both the front-end equalizer and the back-end channel decoder. A few back-end demodulation/decoding iterations are performed for each equalization iteration so as to improve the a priori information for the equalizer. As presented in the extrinsic information transfer (EXIT) chart analysis and simulation results for different multipath fading channels, this provides not only faster convergence to low bit error rates, but also lower computational complexity.

A SiGe BiCMOS analog SISO decoder with I/O interface

Although recent implementations of analog iterative decoders have proven their potential for higher decoding speed and less power consumption than their digital counterparts, the CMOS or conventional BiCMOS technologies used so far seem to be incapable to cope with the need for high throughput that high-speed applications require. Within this context this work presents the design and test results of a high-speed analog SISO (Soft-Input Soft-Output) channel decoder for an 8-bit trellis code by exploiting the high-speed features of SiGe heterojunction bipolar transistors (HBTs). It is one of the first successful implementations of an error-correcting decoder in SiGe BiCMOS technology, which incorporates a high-speed I/O interface. A high-level model of the mismatch effects indicates that there is no significant performance penalty. Moreover, simulations and performance evaluations of an analog Turbo decoder based on the designed SISO decoder are provided. Even though the IC of the SISO module was tested at a throughput up to 3 Mbps, simulation results show that the decoder is capable to operate at 50 Mbps. The measured power consumption is 860 mW and the die area is 3.4 × 3 mm2.

Impact of the Gaussian Approximation on the Performance of the Probabilistic Data Association MIMO Decoder

The probabilistic data association (PDA) decoder is investigated for use in coded multiple-input multiple-output (MIMO) systems and its strengths and weaknesses are determined. The conventional PDA decoder includes two approximations. The received symbols are assumed to be statistically independent and a Gaussian approximation is applied for the interference and noise term. We provide an analytical formula for the exact probability density function (PDF) of the interference and noise term, which is used to discuss the impact of the Gaussian approximation in the presence of a soft-input soft-output channel decoder. The results obtained resemble those obtained for the well-known PDA multiuser detector in coded CDMA systems for which similar investigations have been done before.

A Low-Complexity SISO Multiuser Detector for Iterative Decoding of Asynchronous CDMA Systems With Convolutional Codes

The optimal decoding scheme for asynchronous code-division multiple-access (CDMA) systems that employ convolutional codes results in a prohibitive computational complexity. To reduce the computational complexity, an iterative receiver structure was proposed for decoding multiuser data in a convolutional coded CDMA system. At each iteration, extrinsic information is exchanged between a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders. A direct implementation of the optimal SISO multiuser detector, however, has exponential computational complexity in terms of the number of users which is still prohibitive for channels with a medium to large number of users. This paper presents a low-complexity SISO multiuser detector using the decision-feedback scheme, of which tentative hard decisions are made and fed back to the SISO multiuser from the previous decoding output. In the proposed scheme, the log-likelihood ratios (LLR) as well as the tentative hard decisions of code bits are fed back from the SISO decoders. The hard decisions are used to constrain the trellis of the SISO multiuser detector and the LLRs are used to provide a priori information on the code bits. The detector provides good performance/complexity tradeoffs. The computational complexity of the detector can be set to be as low as linear in the number of users. Simulations show that the performance of the low-complexity SISO multiuser detector approaches that of the single-user system for moderate to high signal-to-noise ratios even for a large number of users.

Iterative (turbo) soft interference cancellation and decoding for coded CDMA

The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive soft-output decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used as a priori information in the next iteration, just as in turbo decoding. Given the multipath CDMA channel model, a direct implementation of a sliding-window SISO multiuser detector has a prohibitive computational complexity. A low-complexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum mean-square error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signal-to-noise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and single-user performance can be approached.