• Journal of Communications and Networks
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• v.17 no.6
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• pp.609-621
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• 2015

Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.535-544
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• 2012

Multi-Input Multi-Output (MIMO) transmission is now considered as one of essential techniques enabling high rate data transmissions in wireless communication systems. In addition, severe channel impairments in wireless systems should be compensated by using highly efficient forward error correction (FEC) codes. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection information (SDDI), are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of SDDI from the MIMO detection process. In this paper, we propose a soft MIMO detection scheme using QR decomposition of channel matrices as an efficient means to provide accurate SDDI to the iterative decoder. The proposed method employed a two sequential soft MIMO detection process in order to reduce computational complexity. Compared to the soft ZF method calculating the direct inverse of the channel matrix, the complexity of the proposed method can be further reduced as the number of antennas is increased, without any performance degradation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.61-66
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• 2013

In this paper, we propose an iterative soft dimension reduction based multi-input multi-output (MIMO) detection for coded spatial multiplexing system. In spite of better performance of iterative MIMO detection, its computational complexity gives a significant burden to the receivers. To mitigate this problem, we propose a scheme employing all ordering successive interference cancellation (AOSIC) for hard-decision detection and dimension reduction soft demodulator (DRSD) with iterative decoding for soft-decision detectors, respectively. This scheme can reduce complexity of iterative soft MIMO detection and provide better performance than other conventional detectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6A
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• pp.577-586
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• 2011

Modem digital communication systems are required to use forward error correction (FEC) codes to combat inevitable channel impairment. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection (SDD) information, are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of soft decision detection information. In order to use FEC codes with iterative decoding for Multi-Input Multi-Output (MIMO) system, reliable soft decision channel gain should be provided. In this paper, we investigate efficient SDD methods for turbo-coded MIMO system, and derive the corresponding formulas of SDD for various MIMO detection schemes. We present simulation results of the derived SDD schemes for turbo-coded MIMO systems, and show that the presented results almost approximate to maximum likelihood detection performance with much less computational load.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.734-742
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• 2008

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task and various demultiplexing methods have been developed. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD), sphere decoding (SD), QOC, and MOC schemes have been reported to achieve a (near) maximum likelihood (ML) hard decision performance. In general, however, the reliability of soft output of these schemes is not satisfactory. In this paper, we propose a method which enhances the reliability of soft output. By computer simulations, we demonstrate the improved performance by the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.11
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• pp.1051-1059
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• 2013

This paper proposes a complexity reduced list sphere decoding (LSD) scheme for joint iterative soft detection scheme for coded MIMO system. The conventional LSD scheme is based on searching the candidates with a fixed radius. However, once the candidate list is full, it is highly probable that the radius can be reduced. By reducing the radius, the complexity can be also reduced. We propose a simple and efficient radius update method for complexity reduction of list version sphere decoding and its application to iterative soft MIMO detection. We evaluate the performance of the proposed scheme with a joint soft-input-soft-ouput iterative MIMO detection in combination with turbo codes. Simulation results show that the proposed methods provide substantial complexity reduction while achieving similar bit error rate (BER) performance as the conventional LSD scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.394-402
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• 2008

In this paper, we propose a novel soft output generation method for spatially multiplexed MIMO systems. The receiver complexity of spatially mutiplexed MIMO system is in proportion to the number of candidate vectors. The ML signal detection method considers all possible vectors as candidates, thereby achieving a high performance, however, its complexity is very high. Low complexity receiver techniques involving a small number of candidate vectors, provide soft output values of low reliability. In this paper, we propose a method to improve reliability of the soft output values obtained using a small number of candidate vectors.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.22-28
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• 2015

In this paper, we propose a new computationally efficient joint iterative multi-input multi-output (MIMO) detection scheme using a soft interference cancellation and minimum mean squared-error (SIC-MMSE) method. The critical computational burden of the SIC-MMSE scheme lies in the multiple inverse operations of the complex matrices. We find a new way which requires only a single matrix inversion by utilizing the Taylor series expansion of the matrix, and thus the computational complexity can be reduced. The computational complexity reduction increases as the number of antennas is increased. The simulation results show that our method produces almost the same performances as the conventional SIC-MMSE with reduced computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.841-849
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• 2012

This paper proposes an low-complexity soft-output multiple-input multiple-output (soft-MIMO) detection algorithm for achieving soft-output maximum-likelihood (soft-ML) performance under max-log approximation. The proposed algorithm is based on a parallel tree-search (PTS) applying a channel ordering by a sorted-QR decomposition (SQRD) with altered sort order. The empty-set problem that can occur in calculation of log-likelihood ratio (LLR) for each bit is solved by inserting additional nodes at each search level. Since only the closest node is inserted among nodes with opposite bit value to a selected node, the proposed node insertion scheme is very efficient in the perspective of computational complexity. The computational complexity of the proposed algorithm is approximately 37-74% of that of existing algorithms, and from simulation results for a $4{\times}4$ system, the proposed algorithm shows a performance degradation of less than 0.1dB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.771-777
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• 2010

This paper proposes a new signal detection method, called QR-OSIC with Candidates (QOC) method, for spatially multiplexed multiple input multiple output (MIMO) systems. By using the ordered successive interference cancellation (OSIC) algorithm and the maximum likelihood (ML) metric, the proposed method achieves near-ML performance without requiring a large number of candidates. Although the proposed method can be used for both hard and soft decoding systems, it is especially useful for soft decoding systems since the LLR values for all the bits can be efficiently computed without using LLR estimation. The proposed method is also suitable for VLSI implementation since it leads to fixed throughput system.