Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach

Abstract

This letter investigates the problem of providing gigabit wireless access with reliable communication in 5G millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) networks. In contrast to the classical network design based on average metrics, a distributed risk-sensitive reinforcement learning-based framework is proposed to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km2, the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.

arXiv commentIEEE Communications Letters. Topic is Ultra-Reliable Communication in 5G mmWave Networks

References in corpus (5)

• Ultra-Reliable and Low Latency Communication in mmWave-Enabled Massive MIMO Networks
• Hierarchical Interference Mitigation for Massive MIMO Cellular Networks
• Joint load balancing and interference mitigation in 5G HETNETS
• Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty
• Wireless Access for Ultra-Reliable Low-Latency Communication (URLLC): Principles and Building Blocks

Cited by in corpus (5)

• Taming the latency in multi-user VR 360${}^{\circ }$: A QoE-aware deep learning-aided multicast framework
• Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks
• Energy-Efficient Power Control of Train-ground mmWave Communication for High Speed Trains
• Robust and Reliable Stochastic Resource Allocation via Tail Waterfilling
• Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks