Big Data Aided Aggregation Coding Multiple Access for Machine Type Communications

Future fifth generation (5G) wireless networks will be challenged by the huge amount of mobile data traffic, especially from enormous Machine Type Communication(MTC) devices, so in this paper we proposed Aggregation Coding Multiple Access(ACMA) for MTC downlink transmissions, which exploits the inherent correlation among aggregated users in spatial domain to improve the spectrum efficiency. By assuming the data traffic together with the delay requirements of MTC devices can be perfectly predicted based on wireless big data analysis, the transmission order of multicast downlink data traffic from base stations to multiple terminals could be adjusted by proposed aggregation coding technique using much less timeslots. We then evaluated the performance of conditional random search(CRS), standard-row algorithm(SRA) and Genetic Algorithm(GA) to optimize the data traffic transmission order compared to Multimedia Broadcast Multicast Service (MBMS) and simulation results validated the performance of ACMA and the optimization of GA. Our work shed light on dealing with massive MTC data traffic for future wireless communications.