Mmwave Massive MIMO HetNet Cell-Based Pilot Contamination Detection Using Meta Heuristic Deep Learning Algorithms

A cutting-edge technology in 5G wireless networks is vast Multiple-Input-Multiple-Output (MIMO), which offers multiple antennas at the destination to form a vast antenna array. Spatial multiplexing of consumers, which requires accurate channel state information (CSI), is how MIMO maximises the spectral efficiency (SE) with the many antennas. Both pilot overhead and pilot pollution have an impact on the SE. This research propose novel technique in Mmwave massive MIMO pilot contamination detection based on HetNet with meta heuristic deep learning algorithms. Here the HetNet based MmWave MIMO cell structure has been deployed and network pilot contamination is detected utilizing convolutional adversarial markov Q- swarm Aquila optimization (CAMQSAO) with orthogonal domain channel transfer functions (ODCTF). The experimental analysis has been carried out in terms of Signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), spectral efficiency.