COMPLEXITY REDUCED CHANNEL ESTIMATION IN WIMAX ENVIRONMENT FOR MIMO–OFDM SYSTEM

Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) are considered to be major methods for the ensuing high performance in next generation mobile communications. The undesirable effects on the transmitted signals need to be addressed and eliminated to improve the capacity of the systems. These effects depend on the physical properties of the channel. Hence, there is a need to provide perfect estimate of the channel to counteract these effects and thereby delivering precise base-band processes at the receiving end of the system such as signal demodulation and decoding. In this paper, the channel between multiple antenna elements are investigated and analysed for optimum technique with less complexity and less power requirement to estimate the characteristics of the channel. The bit error rate (BER) and normalised mean square error (NMSE) of the channels in MIMO-OFDM systems are examined for different channel tracking techniques. The simulation results are measured to investigate the working of the system model with different algorithms over Worldwide Interoperability for Microwave Access channel. An efficient QRD method is suggested in this paper based on the available system resources and specifications

Optimized deep knowledge-based no-reference image quality index for denoised MRI images

The quality of the Magnetic Resonance Imaging (MRI) image influences the disease diagnosis and consequent treatment. However, noise distortion severely impacts these images and tends to interfere with diagnosis during the data acquisition/transmission. This contribution proposes a novel No-reference Image Quality Index (NIQI) method for the intelligent estimation of MRI images and to evaluate its efficacy compared to well-established approaches. A novel Optimized Deep Knowledge-based NIQI (ODK-NIQI) method is developed and tested rigorously. The ODK-NIQI method combines shuffle shepherd optimization and improved deep mish-activated ConvNet approach. The implementation of the projected approach is conducted in MATLAB software. The results demonstrate that the proposed method achieves the best performance and the highest consistency of objectives for both the noisy and denoised MRI brain images investigated. Additionally, the proposed method shows significant improvement over the traditional NIQI techniques using standard performance metrics comprising the Spearman's Rank Correlation Coefficient (SROCC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Pearson Linear Correlation Coefficient (PLCC). Overall, the proposed ODK-based NIQI strategy performs well in denoising MRI images