Optimizing Throughput and Latency of Static 5G Multicast Networks using Boltzmann Machines

The 5G networks transmit data highly directionally and at very high rates. As a result, the data is not broadcast to all the receivers in the network simultaneously and there exist time delays in receivers servicing. The sender's goal is to find an optimal beam divergence angle for the antenna such that the data multicast rate is maximised while minimising alignment delay. There is a need for finding optimal receiver servicing solutions efficiently with high frequency especially for edge devices. Here we model the above problem as a constrained optimization problem and solve it using a Boltzmann Machine. The corresponding objective function has two terms: (i) the data transmission delay and (ii) the alignment delay. In addition to this, a penalty is imposed on the sender to ensure that all receivers receive the data exactly once and all the sets of receivers are serviced in a geometrically meaningful manner. The objective function is mapped to the functional form of the energy function of a Boltzmann Machine. The output comprises a prescription of which sets of receivers are to be serviced and an optimum value of beam divergence angle. This work captures the output visualisation, and the effect of relevant constraints appropriately for the first time. Efficient hardware-accelerated implementations of Boltzmann Machines using in-memory computing principles and emerging memories like memristors greatly increase the significance of such mappings. Such mappings of latency minimization problems are expected to be crucial for developing 5G mobile multicast networks in an IoT (Internet-of- Things) setting.