scholarly journals Decoupling of Broadband Optical MIMO Systems Using the Multiple Shift SBR2 Algorithm

2017 ◽  
Vol 6 (1) ◽  
pp. 30
Author(s):  
Zeliang Wang ◽  
André Sandmann ◽  
John G. McWhirter ◽  
Andreas Ahrens
Keyword(s):  
Bit Error Rate ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Singular Value ◽  
Mimo Channel ◽  
Multiple Input ◽  
Channel Interference ◽  
Input Multiple Output ◽  
Value Decomposition ◽  
Multi Mode

Polynomial singular value decomposition (PSVD) plays a very important role in broadband multiple-input multiple-output (MIMO) systems. One of its applications lies in the decoupling of MIMO convolutive mixing channel matrixin order to recover the transmitted signals corrupted by the channel interference (CI) at the receiver. In this paper, a novel algorithm, known as multiple shift second order sequential best rotation (MS-SBR2), is proposed to compute the approximate PSVD of the broadband MIMO channel matrix. Experimental examples, including a measured (2 × 2) optical MIMO channel impulse response using the multi-mode fiber (MMF) testbed, are presented to examine the proposed algorithm. Bit error rate (BER) performances are evaluated among different transmission schemes. In addition, power allocation (PA) schemes are investigated to further optimize the BER performance.

Improved Stack Algorithm for MIMO Systems

2013 ◽  
Vol 333-335 ◽  
pp. 666-669
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han
Keyword(s):  
Bit Error Rate ◽  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
System Capacity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Multiple input multiple output (MIMO) systems could increase wireless communication system capacity enormously. The best optimal detection algorithm for MIMO systems was maximum likelihood (ML) detection algorithm, which could provide the best bit error rate (BER) performance for MIMO systems. However, the computational complexity of ML detection algorithm grew exponentially with the number of transmit antennas and the order of modulation, which resulted in difficult using for practice. A modified MIMO signal detection algorithm which combined ML detection with stack algorithm was presented in this paper. After performing QR decomposition of the channel matrix, the ML detection with length L was done firstly. The partial accumulated metrics were calculated and sorted, which produced an ordered set secondly. Based on the ordered set, stack algorithm was performed to search for the symbol with the minimum accumulated metrics. The proposed algorithm reduced the probability of look back in stack algorithm.

scholarly journals Performance Analysis of Precoded MIMO PLC System Based on Two-Sided Jacobi SVD

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Hasna Kilani ◽  
Mohamed Tlich ◽  
Rabah Attia
Keyword(s):  
Closed Loop ◽  
Multiple Input Multiple Output ◽  
Singular Value ◽  
Power Line Communication ◽  
Error Vector Magnitude ◽  
Zero Forcing ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Value Decomposition ◽  
Vector Magnitude

This paper evaluates the performance of closed loop multiple input multiple output power line communication (CL MIMO PLC) system based on enhanced zero-forcing (ZF) equalizer. In this work, the two-sided Jacobi (TSJ) algorithm has been investigated for the computation of singular value decomposition of the channel matrix. Quantized parameters are feedback from the receiver to the transmitter for precoding process. Numerous simplifications are introduced for the reduction of the algorithm complexity. The performance of the CL MIMO PLC is evaluated in terms of bit error rate (BER), constellation error vector magnitude (EVM), and mean square error (MSE) between the constructed SVD matrices and Matlab computed ones.

scholarly journals An Analytical Multimodulus Algorithm for Blind Demodulation in a Time-Varying MIMO Channel Context

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Steredenn Daumont ◽  
Daniel Le Guennec
Keyword(s):  
Bit Error Rate ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Time Varying ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Separation Algorithm ◽  
Communication Signals ◽  
Multiple Input ◽  
Input Multiple Output

This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AMMA and adaptive-ASCMA, respectively, are compared with the adaptive (Analytical Constant Modulus Algorithm) ACMA and the MMA (Multi-Modulus Algorithm). The adaptive-AMMA and adaptive-ASCMA achieve a lower residual intersymbol interference and bit error rate than those of the adaptive-ACMA and MMA.

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