Antenna Beam Pattern Modulation with Lattice Reduction Aided Detection

This paper introduces a novel transmission design for antenna beam pattern modulation (ABPM) with a low complexity decoding method. The concept of ABPM was first presented with the optimal maximum likelihood (ML) decoding. However, an ML detector may not be viable for practical systems when the constellation size or the number of antennas is large such as in massive multiple input multiple output (MIMO) systems. Linear detectors, on the other hand, have lower complexity but inferior performance. In this paper, we present the antenna pattern selection with a lattice reduction (LR) aided linear detector for ABPM to reduce the detection complexity with the bit error rate (BER) performance approaching that of ML while conserving low complexity. Simulation results show that even with this suboptimal detection, performance gain is achieved by the proposed scheme compared to different spatial modulation techniques using ML detection. In addition, to validate the results, an upper bound expression for BER is provided for ABPM with ML detection

Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

In this paper, we introduce machine learning approaches that are used to prioritize outpatients (OP) according to their current health state, resulting in self-optimizing heterogeneous networks (HetNet) that intelligently adapt according to users' needs. We use a naïve Bayesian classifier to analyze data acquired from OPs' medical records, alongside data from medical Internet of Things (IoT) sensors that provide the current state of the OP. We use this machine learning algorithm to calculate the likelihood of a life-threatening medical condition, in this case an imminent stroke. An OP is assigned high-powered resource blocks (RBs) according to the seriousness of their current health state, enabling them to remain connected and send their critical data to the designated medical facility with minimal delay. Using a mixed integer linear programming formulation (MILP), we present two approaches to optimizing the uplink side of a HetNet in terms of user-RB assignment: a Weighted Sum Rate Maximization (WSRMax) approach and a Proportional Fairness (PF) approach. Using these approaches, we illustrate the utility of the proposed system in terms of providing reliable connectivity to medical IoT sensors, enabling the OPs to maintain the quality and speed of their connection. Moreover, we demonstrate how system response can change according to alterations in the OPs' medical conditions