ABSTRACT
Daily life today requires the use of mobile phones as a mode of wireless communication and to support cloud-based streaming facilities. One of the ways to attain seamless mobile communication is optimizing the resources that are allocated and that are being used, for instance by using sufficient numbers of transmitters and receivers for the demand. The algorithms and techniques of machine learning have been used to allocate resources in an optimized and scalable manner. Massive multiple-input/multiple-output (MIMO) is a setup of large numbers of antenna arrays that make possible the simultaneous transmission of large numbers of data streams. Beamforming is a variation on massive MIMO that has the ability to fit in the antenna array’s radiation pattern, and adaptive cell association allocates resources depending on the quantity of the associated user equipment. Load balancing is another important technique that is used to avoid traffic in the base station by distributing some of the workloads from the overloaded stations to the less-loaded base transceiver stations. This chapter gives a detailed description of current efforts to optimize resource allocation.
