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

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

Uncoordinated Interference Avoidance Between Terrestrial and Non-Terrestrial Communications

This paper proposes an algorithm that uses geospatial analytics and the muting of physical resources in next-generation base stations (BSs) to avoid interference between cellular (or terrestrial) and satellite communication systems as non-terrestrial systems. The information exchange between satellite and terrestrial links is very limited, but a hybrid edge cloud node with access to satellite trajectories can enable these BSs to take proactive measures. We show simulation results to validate the superiority of our proposed algorithm over a conventional method. Our algorithm runs in polynomial time, making it suitable for real-time interference avoidance.Comment: 5 pages, 4 figures, submitted to IEEE Global Communications Conference 202

Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of number of lookbacks of the beam measurements to study the prediction accuracy. Simulations show that at a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.Comment: 22 pages, 9 figures. Submitted to IEEE Transactions on Communication