Hyunsub Kim

Near-ML MIMO Detection Algorithm With LR-Aided Fixed-Complexity Tree Searching

In this paper, we propose a low-complexity multipleinput multiple-output (MIMO) detection algorithm with lattice-reduction-aided fixed-complexity tree searching which is motivated by the fixed-complexity sphere decoder (FSD). As the proposed scheme generates a fixed tree whose size is much smaller than that of the full expansion in the FSD, the computational complexity is reduced considerably. Nevertheless, the proposed scheme achieves a near-maximum-likelihood (ML) performance with a large number of transmit antennas and a high-order modulation. The experimental results demonstrate that the performance degradation of the proposed scheme is less than 0.5 dB at the bit error rate (BER) of 10-5 for a 8 × 8 MIMO system with 256 QAM. Also, the proposed method reduces the complexity to about 1.23% of the corresponding FSD complexity.

A high performance MIMO detection algorithm for DL MU-MIMO with practical errors in IEEE 802.11ac systems

In this paper, we propose a high performance multiple-input and multiple-output (MIMO) detection algorithm for downlink multiuser MIMO (DL MU-MIMO) with practical errors in IEEE 802.11ac systems. The DL MU-MIMO system using the precoding matrix, which is generated through imperfect channel state information caused by channel estimation error and channel feedback quantization error, has severe performance degradation since each station (STA) receives the desired signal for the STA as well as the interference signal for different STAs. Therefore, the proposed detection algorithm is developed by considering both the desired signal and the interference signal. Using the characteristics of the interference signal, the proposed detection algorithm is performed by additionally detecting several interference symbols. The proposed algorithm has better performance than the conventional algorithm considering only the desired signal. Simulation results show that the coded bit error rate performance of the proposed algorithm was improved in all modulation and coding scheme cases, compared to the conventional algorithm.

Low-complexity MIMO detection algorithm with adaptive interference mitigation in DL MU-MIMO systems with quantization error

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 complex MIMO 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.

Lattice-Reduction-Aided Partial Marginalization for Soft Output MIMO Detector With Fixed and Reduced Complexity

In this letter, we propose lattice-reduction (LR)-aided partial marginalization (PM) for soft output multiple-input multiple-output (MIMO) detection. PM has the advantages of a fully predictable runtime and convenience in parallelization while offering a well-defined tradeoff between the performance and the computational complexity. However, the computational complexity of PM to achieve a high level of performance is considerably high. In order to reduce the complexity of PM, the proposed scheme performs low-complexity LR-aided marginalization instead of exact marginalization (EM) to avoid the exhaustive approach of the EM, which mainly increases the overall complexity. The experimental results demonstrate that the computational complexity of the proposed scheme is considerably lower with negligible performance degradation compared to conventional PM.

Near-optimal MIMO detection algorithm with low and fixed complexity

In this paper, we propose a near-optimal multiple-input multiple-output (MIMO) detection algorithm with low and fixed complexity, which is based on the fixed-complexity sphere decoder (FSD). By employing the lattice reduction (LR) to the FSD, the computational complexity is reduced considerably while maintaining the near-optimal bit-error-rate (BER) performance. Although the application of LR to the FSD is not straightforward, an efficient solution is proposed in this paper. The numerical results demonstrate that the computational complexity of the proposed algorithm is reduced by about 43.02% and 76.49% for 64-QAM and 256-QAM compared to the optimal FSD while the performance degradation is negligible.

Adaptive CSI feedback scheme to maximize the throughput in IEEE 802.11ac system

In this letter, we propose a low-complexity adaptive channel state information (CSI) feedback scheme to maximize the throughput in IEEE 802.11ac systems. We analyze the two factors - the interference and the feedback overhead -and develop a method to attain an optimal CSI feedback quantization level with minimal additional complexity. Our numerical results and complexity comparisons verify the efficiency of the proposed scheme in terms of throughput and complexity.

A fast convergence LLL algorithm with fixed-complexity for SIC-based MIMO detection

In this paper, we propose a fixed-complexity variant of LLL (Lenstra-Lenstra-Lovasz) algorithm. LLL algorithm is widely used in MIMO signal processing to obtain full diversity gain with low complexity increase. However, because of its non-deterministic nature with varying complexity and high worst-case costs, the real implementation of original LLL algorithm is difficult. Although some fixed-complexity variants of LLL algorithm has been proposed, but still their complexity in large MIMO system is high. The proposed algorithm uses column selection method based on threshold which leads to the fast convergence in fewer iterations. Simulation result shows that the proposed algorithm converges faster than other fixed-complexity variants of LLL algorithms while it saves about 30% complexity in 8 × 8 MIMO system compared to existing fixed-complexity LLL (fc-LLL) algorithm.

Near-ML lattice reduction-aided detection scheme for low complexity MIMO-OFDM systems

In this paper, we propose a novel lattice reduction (LR) algorithm for the low-complexity multiple-input multiple-output (MIMO) detection with near-maximum-likelihood (ML) performance. The proposed LR algorithm is designed considering both the hardware complexity and the power consumption. First, a modified column traverse strategy is proposed to reduce the worst-case complexity (hardware complexity). Also, in order to reduce the average complexity (power consumption), we focus on the joint optimization by employing the early termination (ET) criterion in the context of MIMO detection, whereas the conventional approaches are based exclusively on channel characteristics. In order to make it possible for the LR-aided fixed-complexity sphere detector (FSD) to perform the partial detection, the LR process is thoroughly modified so that the ET criterion is able to be employed. Furthermore, we perform the joint optimization of these two approaches. The experimental results demonstrate that the worst-case and average complexity is reduced considerably maintaining the near-ML BER performance at the BER of 10−5.

MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection

In this paper, we propose a minimum-mean-squared-error (MMSE)-based lattice-reduction (LR)-aided fixed-complexity sphere decoder (FSD) for low-complexity near-maximum-likelihood (near-ML) multiple-input multiple-output (MIMO) detection. In order for the FSD to achieve optimal performance, the number of full expansion (FE) stages should be sufficient, which is the major cause of the increase in the computational complexity when either a large signal constellation or a large number of antennas are adopted. However, the proposed algorithm maintains the near-ML performance with the aid of the MMSE-based LR algorithm while reducing the number of FE stages. Although there exists the increase in the computational complexity for the application of the additional processing elements, the decrease in the number of FE stages results in the lower computational complexity of the overall algorithm. The numerical analysis demonstrates that there is a considerable decrease in the computational complexity while the performance degradation is negligible, compared to the optimal FSD.