A simplified minimum mean square error (MMSE) detector is proposed for joint detection and decoding of multiple-input multiple-output (MIMO) systems. The matrix inversion lemma and the singular value decomposition (SVD) of the channel matrix are used to simplify the computation of the coefficient of the MMSE filter. Compared to the original MMSE detector, the proposed detector has a much lower computational complexity with only a marginal performance loss. The proposed detector can also be applied to MIMO systems with high order modulations.

Science Letters： A simplified MMSE-based iterative receiver for MIMO systems

In this paper, belief propagation (BP) iterative estimation of chaos-modulated signals is considered in massive multiple-input multiple-output (MIMO) systems. We show that signals encrypted by a certain class of chaos algorithms cannot be decoded accurately by BP decoding. To address this, we reconstruct the algorithm to allow BP decoding to be applied to the data transmitted across the massive MIMO system. Numerical results show that the proposed encryption successfully improves the bit error ratio performance. Moreover, no significant difference in the computation time required by the BP decoding is observed between the proposed encryption and the binary phase-shift keying. This suggests that the proposed encryption is practical to use with the BP method and that this approach may enhance the security of massive MIMO systems.

Chaotic Encryption for Belief Propagation Decoding in Massive MIMO Systems

Multilevel Modulated Chaotic Encryption and Belief Propagation Decoding

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.

Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

In MIMO systems, soft iterative detection and decoding can produce the near capacity performance. One of the promising detection techniques known as sphere decoder can play an important role in order to meet the requirements of achieving near optimal performance. The single tree search (STS) is based on the sphere decoding which can produce near optimal performance in iterative detection and decoding. The main hindering in STS method is that it is computationally complex. The complexity increases as we increase the iterations. In this paper, we propose to reduce complexity of the STS method by limiting the candidates of calculating soft information to those bits whose a priori information provided by turbo decoder is less reliable. Simulation results show that the proposed method can reduce the complexity with negligible performance degradation compared to the conventional full search and STS methods.

D. M. Saqib Bhatti

Papers

Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Complexity reduced soft MIMO detetion using single tree search