Channel Estimation and Sparse Signal Processing in mm- Wave MIMO Systems

The use of compressive sensing (CS) for sparse channel estimation has been recognized as an effective solution to address the issue of high pilot overhead in massive MIMO systems. Therefore, the complexity of signal processing in wireless communication systems, the matrix measurement in the Channel estimates based on Carrier Sense (CS) might be “unfriendly” to channel recovery. Propose an iterative channel estimation technique for millimeter wave (mmWave) MIMO systems based on the Sparse Minimum Mean Square Error (SMMSE) and sparse message passing (SMP) algorithms. Propose a method that iteratively locates and determines the precise values of non-zero elements of the sparse channel matrix by taking use of the sparsity. The locations are found using the Sparse Message Passing (SMP) method, and values are calculated using the SMMSE at each iteration. NOMA is a competitive approach for increasing capacity in Mbps and channel gain in Signal to Noise Ratio over Additive White Gaussian Noise and other fading channels such as Rayleigh and Rician Channels. The Adaptive All Pass Filter (AAPF) is a novel methodology that is proposed for reduction in P APR for efficient NOMA 6G wireless communication to achieve efficient system. This technique meets the requirement that NOMA capacity always outperforms NOMA capacity for each user, making it suitable for use with any scheduling paradigm. The proposed methodology is compared with OFDM to prove NOMA performance is better than OFDM. The various parameters such as reduction in P APR, BER, FER and maximization in Capacity, Signal to Noise Ratio and throughput are calculated and shown as graphical representation.