• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1722-1724
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• 2015

This paper proposes a partial detection scheme using QRD-M, DFE, and iterative schemes for efficiency in terms of detection performance and complexity in a MIMO-OFDM system. The proposed scheme detects signals by using the different detection methods in according to spatial stream. In the proposed scheme, QRD-M with high detection performance and high complexity is used in spatial stream that requires low complexity, and DFE with low detection performance and low complexity is used in spatial stream that requires high complexity. Also, the iterative detection is performed in the detected spatial stream by using DFE. From the simulation, it is confirmed that although proposed scheme has increased complexity, detection performance is greatly improved by the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.100-107
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• 2011

Algorithms based on the QR decomposition of the equivalent space-time channel matrix have been proved useful in the detection of V-BLAST systems. Especially, the parallel detection (PD) algorithm offers ML approaching performance up to 4 transmit antennas with reasonable complexity. We show that when directly applied to STBCs, the PD algorithm may suffer a rather significant SNR degradation over ML detection, especially at high SNRs. However, simply extending the PD algorithm to allow p ${\geq}$ 2 candidate layers, i.e. p-PD, regains almost all the loss but only at a significant increase in complexity. Here, we propose a simplification to the p-PD algorithm specific to STBCs without a corresponding sacrifice in performance. The proposed algorithm results in significant complexity reductions for moderate to high order modulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.43-50
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• 2007

In the paper, we address QRM-MLD (Maximum Likelihood Detection with QR Decomposition and M-algorithm) signal detection method for spatially multiplexed MIMO (Multiple Input Multiple Output) systems. Recently, the QRM-MLD signal detection method which can achieve 1Gbps transmission speed for next generation mobile communication was implemented in a MIMO testbed for the mobile moving at a pedestrian speed. In the paper, we propose a novel signal detection method 'reduced complexity QRM-MLD' that achieves identical error performance as the QRM-MLD while reducing the computational complexity significantly. We rigorously compare the two detection methods in terms of computational complexity to show the complexity reduction of the proposed method. We also perform a set of computer simulations to demonstrate that two detection methods achieve identical error performance.

• ETRI Journal
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• v.45 no.3
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• pp.418-432
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• 2023

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10^-3.

• 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.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.210-217
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• 2016

In this paper, we propose a low complexity multiple-input multiple-output (MIMO) detection algorithm with adaptive interference mitigation in downlink multiuser MIMO (DL MU-MIMO) systems with quantization error of the channel state information (CSI) feedback. In DL MU-MIMO systems using the imperfect precoding matrix caused by quantization error of the CSI feedback, the station receives the desired signal as well as the residual interference signal. Therefore, a complexMIMO detection algorithm with interference mitigation is required for mitigating the residual interference. To reduce the computational complexity, we propose a MIMO detection algorithm with adaptive interference mitigation. The proposed algorithm adaptively mitigates the residual interference by using the maximum likelihood detection (MLD) error criterion (MEC). We derive a theoretical MEC by using the MLD error condition and a practical MEC by approximating the theoretical MEC. In conclusion, the proposed algorithm adaptively performs interference mitigation when satisfying the practical MEC. Simulation results show that the proposed algorithm reduces the computational complexity and has the same performance, compared to the generalized sphere decoder, which always performs interference mitigation.

• ETRI Journal
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• v.35 no.3
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• pp.371-377
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• 2013

An effective signal detection algorithm with low complexity is presented for multiple-input multiple-output orthogonal frequency division multiplexing systems. The proposed technique, QR-MLD, combines the conventional maximum likelihood detection (MLD) algorithm and the QR algorithm, resulting in much lower complexity compared to MLD. The proposed technique is compared with a similar algorithm, showing that the complexity of the proposed technique with T=1 is a 95% improvement over that of MLD, at the expense of about a 2-dB signal-to-noise-ratio (SNR) degradation for a bit error rate (BER) of $10^{-3}$. Additionally, with T=2, the proposed technique reduces the complexity by 73% for multiplications and 80% for additions and enhances the SNR performance about 1 dB for a BER of $10^{-3}$.

• ETRI Journal
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• v.40 no.2
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• pp.218-226
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• 2018

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

• ETRI Journal
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• v.44 no.2
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• pp.346-354
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• 2022

The SYN flooding attack is widely used in cyber attacks because it paralyzes the network by causing the system and bandwidth resources to be exhausted. This paper proposed a self-information approach for detecting the SYN flooding attack and provided a detection algorithm with a hierarchical policy on a detection time domain. Compared with other detection methods of entropy measurement, the proposed approach is more efficient in detecting the SYN flooding attack, providing low misjudgment, hierarchical detection policy, and low time complexity. Furthermore, we proposed a detection algorithm with limiting system resources. Thus, the time complexity of our approach is only (log n) with lower time complexity and misjudgment rate than other approaches. Therefore, the approach can detect the denial-of-service/distributed denial-of-service attacks and prevent SYN flooding attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2405-2411
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• 2009

MMSE, ZF and ML are the decoding mechanisms for V-BLAST system, and ML shows the best performance decoding the original signal among them. However, it has a problem that the computation complexity is increased exponentially according to the number of transmit antennas and transmit degrees. In this paper, we propose a low complexity linear ML detection algorithm having low computation complexity, then analyze the system performance in BER and computation complexity comparing with other algorithms. In the simulation, the BER performance of the proposed algorithm is superior than ZF and MMSE detection algorithms, and similar to ML detection algorithm. However, its computation complexity was 50% less than ML algorithm. From the results, we confirm that the proposed algorithm is superior than other ML detection algorithms.