Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are to 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Blockage Prediction for Mobile UE in RIS-assisted Wireless Networks: A Deep Learning Approach

Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme