Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems
Multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA) internet-of-things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support the real-time applications. Age of information (AoI) is an important metric for real-time application, but there is no lite...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
11.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA)
internet-of-things (IoT) systems can improve channel capacity and spectrum
efficiency distinctly to support the real-time applications. Age of information
(AoI) is an important metric for real-time application, but there is no
literature have minimized AoI of the MIMO-NOMA IoT system, which motivates us
to conduct this work. In MIMO-NOMA IoT system, the base station (BS) determines
the sample collection requirements and allocates the transmission power for
each IoT device. Each device determines whether to sample data according to the
sample collection requirements and adopts the allocated power to transmit the
sampled data to the BS over MIMO-NOMA channel. Afterwards, the BS employs
successive interference cancelation (SIC) technique to decode the signal of the
data transmitted by each device. The sample collection requirements and power
allocation would affect AoI and energy consumption of the system. It is
critical to determine the optimal policy including sample collection
requirements and power allocation to minimize the AoI and energy consumption of
MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC
process and the noise is stochastic in the MIMO-NOMA channel. In this paper, we
propose the optimal power allocation to minimize the AoI and energy consumption
of MIMO- NOMA IoT system based on deep reinforcement learning (DRL). Extensive
simulations are carried out to demonstrate the superiority of the optimal power
allocation. |
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DOI: | 10.48550/arxiv.2303.06411 |