32 research outputs found
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems
This paper presents a deep reinforcement learning (DRL) solution for power
control in wireless communications, describes its embedded implementation with
WiFi transceivers for a WiFi network system, and evaluates the performance with
high-fidelity emulation tests. In a multi-hop wireless network, each mobile
node measures its link quality and signal strength, and controls its transmit
power. As a model-free solution, reinforcement learning allows nodes to adapt
their actions by observing the states and maximize their cumulative rewards
over time. For each node, the state consists of transmit power, link quality
and signal strength; the action adjusts the transmit power; and the reward
combines energy efficiency (throughput normalized by energy consumption) and
penalty of changing the transmit power. As the state space is large, Q-learning
is hard to implement on embedded platforms with limited memory and processing
power. By approximating the Q-values with a DQN, DRL is implemented for the
embedded platform of each node combining an ARM processor and a WiFi
transceiver for 802.11n. Controllable and repeatable emulation tests are
performed by inducing realistic channel effects on RF signals. Performance
comparison with benchmark schemes of fixed and myopic power allocations shows
that power control with DRL provides major improvements to energy efficiency
and throughput in WiFi network systems.Comment: 5 pages, 6 figures, 1 tabl