Fuzzy C‐Means–Based User Clustering for Channel Interference Minimization in Massive MIMO‐NOMA

ABSTRACT Future wireless connections may rely heavily on multiple‐input multiple‐output (MIMO) devices as a major supporting technology. Nevertheless, it is not clear whether there are performance drops in MIMO circuits as a result of interference. As a relatively recent addition to the multiaccess family, nonorthogonal multiple access (NOMA) is expected to play a significant role in 5G wireless systems. The opportunity for increasing spectral effectiveness and improving wireless connectivity for more people with an amalgamation of NOMA and multiantenna MIMO is substantial. The gain of massive MIMO NOMA over other techniques increases linearly by a factor of M both with the number of users and with the number of antennas at the BS. The suggested approach organizes individuals in a NOMA group into a single group or multiple ones to increase the network's sum‐throughput by taking use of the channel gain disparities among them. Then, we calculate the best power distribution strategy that increases the network's bandwidth by maximizing its sum‐throughput each NOMA clustering for a specific set of NOMA nodes.