Sparse Channel Estimation Using Orthogonal Matching Pursuit (OMP) Algorithm for Next Generation Wireless Communications

Increasing mobile broadband services is one of the primary initial drivers of 5G, driven by the insatiable demand for ever-faster and more immersive mobile experiences. More and more people are using their mobile devices to access, share, and watch high-definition multimedia, which has led to a rise in the demand for mobile data. The ever-expanding capabilities of mobile devices, such as those with higher-resolution cameras, 4K video, always-on cloud computing, virtual/augmented reality, etc., are increasing the need for more robust network infrastructure. Although it still needs a lot of testing, millimeter wave (mm Wave) technology, also known as high-frequency spectrum bands over 24 GHz, is becoming an important part of 5G. Large bandwidths (hundreds of megahertz) are available at these high frequencies, and the prospect of huge capacity gains and exceptionally high data rates is very enticing. To reduce power consumption and effectively use the available bandwidth, this can be done by selecting the sparsest channel. For the purpose of locating the sparsest channel, the Orthogonal Matching Pursuit (OMP) algorithm is used. The algorithm's goal is to perform better in noisy environments with less computing complexity. The estimate of the channel is viewed as a sparse approximation problem. The dominating channel coefficients are sequentially identified by the iterative OMP technique. The process is repeated until there is no longer any residue. Results that accurately reflect the power, level of sparsity, and actual Channel response will support the goal.