Soft-input Soft-output Multiuser Detector Research Articles

Adaptive Iterative Soft-Input Soft-Output Parallel Decision-Feedback Detectors for Asynchronous Coded DS-CDMA Systems

The optimum and many suboptimum iterative soft-input soft-output (SISO) multiuser detectors require a priori information about the multiuser system, such as the users' transmitted signature waveforms, relative delays, as well as the channel impulse response. In this paper, we employ adaptive algorithms in the SISO multiuser detector in order to avoid the need for this a priori information. First, we derive the optimum SISO parallel decision-feedback detector for asynchronous coded DS-CDMA systems. Then, we propose two adaptive versions of this SISO detector, which are based on the normalized least mean square (NLMS) and recursive least squares (RLS) algorithms. Our SISO adaptive detectors effectively exploit the a priori information of coded symbols, whose soft inputs are obtained from a bank of single-user decoders. Furthermore, we consider how to select practical finite feedforward and feedback filter lengths to obtain a good tradeoff between the performance and computational complexity of the receiver.

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.

Coding-Spreading Tradeoff in LDPC-Coded CDMA With Turbo Multiuser Detection

We consider the problem of coding-spreading tradeoff in code-division multiple-access (CDMA) systems employing multiuser joint decoding. In particular, we assume the users employing the capacity-achieving low-density parity-check (LDPC) codes and at the receiver, turbo multiuser detection is used to implement joint decoding. We solve the coding-spreading optimization based on the extrinsic information signal-to-noise ratio (SNR) evolution curves for the soft-input soft-output (SISO) multiuser detectors and the SISO LDPC decoders. Two types of SISO multiuser detectors are treated, namely, the soft interference cancellation minimum mean-square-error (SIC-MMSE) detector and the soft interference cancellation matched filter (SIC-MF) detector. Moreover, both single-cell and multicell scenarios are considered. For each of these cases, we are able to characterize the extrinsic information SNR analytically, for both finite-size systems and for the so-called large systems where asymptotic performance results must be evoked. Our analysis indicates that the SIC-MMSE-based system offers a significant gain in spectral efficiency compared with the SIC-MF counterpart, in both single-cell and multicell scenarios. This is in contrast to the single-user decoding case, where it has been shown that the MMSE detector offers little advantage over the conventional matched-filter in terms of capacity in multicell scenario. Moreover, the results on coding-spreading tradeoff for finite-size systems and large systems match very well.

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.