Increasing energy efficiency of Massive-MIMO network via base stations switching using reinforcement learning and radio environment maps

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

“…However, it can be expected that similar sets of UE positions would result in a similar number of antennas that should be active. Such a phenomenon was observed during our previous studies, exploiting the problem of BSs switching [25]. Therein, we have taken advantage of these similarities between states (REM entries) to speed up the process of learning.…”

“…2. The multi-stage system-level simulator used in this manuscript includes, e.g., channel estimation using Sounding Reference Signals, user scheduling with the proportional fair rule and ZF precoding, as presented in detail in [25]. We are considering a medium range BS, of transmit power equal to 38 dBm [41].…”

“…A popular model of MMIMO BS power consumption can be found in [2]. Similarly as in our previous work, we will consider three components having major impact on power consumption [25]:…”

“…The most commonly used is the one, where EE is given by the ratio of average network throughput, and average network power consumption. We proposed a slight modification to this definition [25]:…”

“…Although state of the art REMs were designed to store value-location tuples, we propose to map a set of all connected UE positions onto the number of active antennas. A similar representation has been used in the context of base station switching in [25]. We propose to learn how many antennas are needed through interaction with the MMIMO network, i.e., using RL.…”