Random Space–Time Line Code With Proportional Fairness Scheduling

As the demand for and services of the Internet of Things (IoT) increase, the dense deployment of a massive number of devices is expected in future IoT networks. Accordingly, an appropriate scheduling method will be required to fairly support the overwhelmingly large number of devices or users operating in practical environments. In this paper, we consider a single-cell downlink network in which one transmitter, such as an access point and base station, supports the massive number of users by using a space-time line code (STLC) scheme. Here, to resolve a prohibitively large amount of channel state information (CSI) feedback from the users to the transmitter, we propose a new STLC scheme called random STLC that uses a random vector for the STLC encoding. Furthermore, a proportional fairness (PF) scheduler is modified with a priority factor that is proportional to the devised alternative signal-to-interference-plus-noise ratio. By a comparison with an optimal STLC system that uses full CSI with a PF scheduler, we justify the benefit of the proposed random STLC with a PF scheduler using partial CSI for massive-user networks. Numerical results obtained from a rigorous simulation confirm that the proposed scheme can provide a comparable to achievable rate and fairness with massive number of users together with affordable feedback overhead in massive networks.