Abstract:
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have to address smart jammers that use smart radio devices to choose the jamming policy wit...Show MoreMetadata
Abstract:
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have to address smart jammers that use smart radio devices to choose the jamming policy with the goal of interrupting the ongoing transmissions. In this paper, we propose a reinforcement learning based power control strategy for the downlink mmWave massive MIMO systems. More specifically, we present a fast policy hill-climbing based power control algorithm for a base station to choose the transmit power over multiple antennas. Based on the signal-to-interference-plus-noise ratio (SINR) of the signals and the jamming strength, we evaluate the impact of the number of transmit antennas on the communication performance. Simulation results verify that the proposed schemes can increase the average SINR, sum data rate and the utility of the mmWave massive MIMO against smart jamming compared with the benchmark strategy.
Published in: 2018 IEEE Global Communications Conference (GLOBECOM)
Date of Conference: 09-13 December 2018
Date Added to IEEE Xplore: 21 February 2019
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Power Control ,
- Massive Multiple-input Multiple-output ,
- Learning-based Power Control ,
- Simulation Results ,
- Base Station ,
- Multiple-input Multiple-output ,
- Ratio Of Signal ,
- Ongoing Transmission ,
- Signal-to-interference-plus-noise Ratio ,
- Massive Multiple-input Multiple-output Systems ,
- Power Control Scheme ,
- System State ,
- Optimal Control ,
- Additive Noise ,
- Inverter ,
- Time Slot ,
- Azimuth Angle ,
- Antenna Array ,
- Channel Model ,
- Fast Algorithm ,
- Markov Decision Process ,
- Reinforcement Learning Algorithm ,
- Mixed Strategy ,
- Optimal Control Strategy ,
- Complex Gain ,
- Channel Matrix ,
- Q-learning Algorithm
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Power Control ,
- Massive Multiple-input Multiple-output ,
- Learning-based Power Control ,
- Simulation Results ,
- Base Station ,
- Multiple-input Multiple-output ,
- Ratio Of Signal ,
- Ongoing Transmission ,
- Signal-to-interference-plus-noise Ratio ,
- Massive Multiple-input Multiple-output Systems ,
- Power Control Scheme ,
- System State ,
- Optimal Control ,
- Additive Noise ,
- Inverter ,
- Time Slot ,
- Azimuth Angle ,
- Antenna Array ,
- Channel Model ,
- Fast Algorithm ,
- Markov Decision Process ,
- Reinforcement Learning Algorithm ,
- Mixed Strategy ,
- Optimal Control Strategy ,
- Complex Gain ,
- Channel Matrix ,
- Q-learning Algorithm
