DETECTION FOR 6G-NOMA BASED MACHINE LEARNING OPTIMIZATION FOR SUCCESSIVE ADAPTIVE MATCHING PURSUIT ANALYSIS
In this recent era, Wireless Communication develops as the most flexible and suitable way of communication among the users contemporary across the globe. In this situation, 6G communication networks will gratify the user requirements of high data speed without any network interjects. Non-orthogonal multiple access (NOMA) had increased in accessing radio procedure in empowering the execution improvements guaranteed in 6G organizes as far as network same as successive adaptive matching pursuit (SAMP) latency, and range efficiency. It delivers the data speed in the range of 10-300 Mbps and a postponement of up to 10-11 Gbps. NOMA upper link, identification dependent on progressive obstruction Analysis (POA) with gadget grouping was suggested. 6G is a generation presently under progress, that's proposed to progress on 5G. 6G promises expressively quicker data rates, higher connection concentration, much lower expectancy, device among another. The max speed of 6G is designed at being as profligate as 35.47 Gbps, which is over 25 times sooner than 5G. This paper proposes a successive adaptive matching pursuit (SAMP) algorithm and the estimation model based on 'LTE-Advanced' wireless channel model. SAMP is designed to MU-MIMO-OFDM (Multi User multi input multi output-orthogonal frequency division multiplexing) of to calculate inter-channel interference. First, SAMP does not need the prior knowledge of the sparsely level. Second, the fixed step size is resolute in order to expand the efficiency of signal reconstruction. Here in this SAMP which proposed technique beats to minimizing the pilot bit error rate (BER) and increase the improved signal-noise ratio (ISNR).