Project: Machine-Learning based Hierarchical Modulation based Cooperative Non-Orthogonal

Key information:

Abstract: 

To fulfil the requirements of the next generation of wireless communications, Non-orthogonal Multiple Access (NOMA) is often considered a primary contender for user multiplexing over the previous traditional methods. In exchange for this improved spectral efficiency brought by the technology, there are many new problems that require attention. This research looks to combine this with Hierarchical Modulation (HM) to support the drawbacks of each system. Both NOMA and HM provide different alternatives to supporting cooperative relays and through their combination, can allow for greater throughput than used separately.

One of the greatest challenges is how to effectively balance the resources available to the transmitter when performing NOMA Downlink to a cluster of users and the various ways that can be achieved. Game Theory approaches in communication scenarios have become increasingly popular as a method of performing user pairing and clustering, as well as power allocation to the different NOMA users which are being considered in this line of work. The challenges arise when implementing cooperative networks that allow data to be relayed from user to user and being able to create a network where user fairness is achieved across all devices, despite their different requirements.