Performance Analysis of Efficient Power Allocation Techniques with Different Modulation for 4X4 V-BLAST MIMO-OFDM System

Objectives: To propose an optimal power allocation method for high-speed V-BLAST MIMO-OFDM system for better performance in terms of SNR and BER than the traditional methods. Materials and Methods: To study and evaluate the performance of two linear channel estimators, the Least Square Error (LS) and Linear Minimum Mean Square Error (LMMSE) are used for 4 × 4 VBLAST Multiple input, multiple output-orthogonal frequency division multiplexing (MIMOOFDM) system with different modulation-4 QAM,16QAM and 64 QAM. For power allocation, optimal power is determined by the Greedy Power Allocation and the Cuckoo Search algorithm depending on the condition of the carriers measured from the channel estimation. Findings: Based on the detection method, Vertical Bell Labs Layered Space-Time (V-BLAST) MIMO-OFDM is found to be the high-speed system. Here, the proposed optimal power allocation method for high-speed V-BLAST MIMO-OFDM system showed a better performance in terms of Signal to Noise Ratio (SNR) and BER than the traditional methods. This thesis work has been evaluated based on different channel estimation with different modulation techniques for V-BLAST MMSE MIMO-OFDM System. The test program has been implemented in the latest technology system in Matlab R2013a. The experimental results show that our proposed method achieves better performance in terms of Bit Error Rate (BER) and SNR compared with the existing methods used for improving the performance of the MIMOOFDM. Linear MMSE proved efficient channel estimation technique over LS. MMSE -OSIC achieves very good performance with respect to other detectors in VBLAST MIMO-OFDM environment for 4× 4 MIMO-OFDM systems. Application: This result would be useful to make efficient Adaptive MIMO-OFDM system. It would be also useful in adaptive modulation and power allocation concept for making better wireless communication system. This performance analysis can perform for different channel models.