Advanced non-orthogonal and deep learning based communication techniques for future wireless communication systems

The future wireless communication systems demand enhanced security and reliability than the current systems. In this research, we propose a more simple yet efficient physical layer security (PLS) technique for achieving reliable and secure communication in the multiple-input single-output non-orthogonal multiple access (MISO-NOMA) system. This system is capable of providing enhanced confidential communication as well as inter-user interference cancellation without using the successive interference cancellation (SIC) method. As conventional NOMA was already adopted under the name of multi-user superposition transmission (MUST) in release 13 of 3GPP but recently excluded from 3GPP-release 17 due to its performance degradation. In this work, we have analyzed these drawbacks and presented a new kind of NOMA with better performance results in cases where conventional NOMA fails. The proposed algorithm combines the benefit of pre-coder matrices with simultaneous transmission using antenna diversity to enhance the security and reliability of wireless communications with no leakage of information. The proposed new NOMA is specially designed for IoT devices that require limited processing at the receiver. The effectiveness of the developed scheme is verified and proven by extensive numerical simulations.