In this paper, we propose a low complexity multipleinputmultiple-output (MIMO) detection algorithm with adaptiveinterference mitigation in downlink multiuser MIMO (DL MUMIMO)systems with quantization error of the channel state information(CSI) feedback. In DL MU-MIMO systems using theimperfect precoding matrix caused by quantization error of theCSI feedback, the station receives the desired signal as well as theresidual interference signal. Therefore, a complexMIMO detectionalgorithm with interference mitigation is required for mitigatingthe residual interference. To reduce the computational complexity,we propose a MIMO detection algorithm with adaptive interferencemitigation. The proposed algorithm adaptively mitigates theresidual interference by using the maximum likelihood detection(MLD) error criterion (MEC). We derive a theoretical MEC byusing the MLD error condition and a practical MEC by approximatingthe theoretical MEC. In conclusion, the proposed algorithmadaptively performs interference mitigation when satisfyingthe practical MEC. Simulation results show that the proposed algorithmreduces the computational complexity and has the sameperformance, compared to the generalized sphere decoder, whichalways performs interference mitigation.

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.

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.

This paper presents an adaptive interference-aware receiver for multiuser multiple-input multiple-output (MU-MIMO) downlink systems in wireless local area network (WLAN) systems. The MU-MIMO downlink technique is one of the key techniques that are newly applied to WLAN systems in order to support a very high throughput. However, the simultaneous communication of several users causes inter-user interference (IUI), which adversely affects receivers. Therefore, in order to prevent IUI, a precoding technique is defined at the transmitter based on feedback from the receiver. Unfortunately, however, the receiver still suffers from interference, because the precoding technique is prone to practical errors from the feedback quantization and subcarrier grouping scheme. Whereas ordinary detection schemes are available to mitigate such interference, such schemes are unsuitable because of their low performance or high computational complexity. In this paper, we propose an switching algorithm based on the norm ratio between an effective channel matrix for the desired signal and that of the interfering signals. Simulation results based on the IEEE 802.11ac standard show that the proposed algorithm can achieve near-optimal performance with a 70% reduction in computational 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.

In multi-user MIMO downlink system, a receiver suffers from interferences because precoding technique cannot be perfectly performed. In this paper, we propose an adaptive interference-aware receiver choosing interference whitening or interference detection based on the channel state. The proposed scheme can achieve the near optimal performance with low computational complexity.

In this letter, we propose an efficient near-optimal detectionscheme (that makes use of a generalized sphere decoder (GSD))for blind multi-user multiple-input multiple-output (MU-MIMO)systems. In practical MU-MIMO systems, a receiver suffers frominterference because the precoding matrix, the result of theprecoding technique used, is quantized with limited feedback andis thus imperfect. The proposed scheme can achieve nearoptimalperformance with low complexity by using a GSD todetect several additional interference signals. In addition, theproposed scheme is suitable for use in blind systems.

In this letter, we propose an exact maximum likelihood (ML) criterion based on semidefinite relaxation (SDR) in multiple-input multiple-output systems. Although a conventional SDR criterion for determining whether a symbol is the ML solution exists, its results cannot be guaranteed when noise is present. In place of the conventional criterion's positive semidefinite (PSD) discriminant, we propose a new, exact ML criterion based on the condition that all diagonal values are positive (PDV), a simple characteristic and necessary condition of PSD. The proposed criterion has a lower calculation complexity for testing than does a PSD and can ensure that the ML solution is always satisfactory.