A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation p...A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation OSIC algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance but it has significantly less complexity in computation.Furthermore when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction DLR the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.展开更多
Multiple input multiple output(MIMO)communication systems have emerged as a key technology to enhance spectral efficiency and reliability in wireless communications.In recent years,deep neural network(DNN)-based appro...Multiple input multiple output(MIMO)communication systems have emerged as a key technology to enhance spectral efficiency and reliability in wireless communications.In recent years,deep neural network(DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection.Among these approaches,the Transformer architecture,known for its effectiveness in capturing long-range dependencies in sequential data,has gained significant attention.Therefore,this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture.This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures,which enables the model to capture both spatial and temporal dependencies in MIMO channels,thereby improving symbol detection accuracy and robustness under varying channel conditions.The proposed scheme's bit error rate(BER)performance is compared with traditional methods through simulations.The results show that the proposed method achieves a signal-to-noise ratio(SNR)gain of nearly 1.5 dB against the traditional detection methods,with the optimal maximum likelihood detector(MLD)only outperforming it by<0.5 dB.展开更多
基金The National Science and Technology Major Project(No.2012ZX03004005-003)the National Natural Science Foundation of China(No.61171081,61201175)the Innovation Technology Fund of Jiangsu Province(No.BC2012006)
文摘A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation OSIC algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance but it has significantly less complexity in computation.Furthermore when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction DLR the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.
基金supported by TUT/SCRI,together with the French South African Institute of Technology(F’SATI).
文摘Multiple input multiple output(MIMO)communication systems have emerged as a key technology to enhance spectral efficiency and reliability in wireless communications.In recent years,deep neural network(DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection.Among these approaches,the Transformer architecture,known for its effectiveness in capturing long-range dependencies in sequential data,has gained significant attention.Therefore,this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture.This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures,which enables the model to capture both spatial and temporal dependencies in MIMO channels,thereby improving symbol detection accuracy and robustness under varying channel conditions.The proposed scheme's bit error rate(BER)performance is compared with traditional methods through simulations.The results show that the proposed method achieves a signal-to-noise ratio(SNR)gain of nearly 1.5 dB against the traditional detection methods,with the optimal maximum likelihood detector(MLD)only outperforming it by<0.5 dB.
