Deep Reinforcement Learning Based Power Allocation for Minimizing AoI
and Energy Consumption in MIMO-NOMA IoT Systems
- URL: http://arxiv.org/abs/2303.06411v1
- Date: Sat, 11 Mar 2023 14:09:46 GMT
- Title: Deep Reinforcement Learning Based Power Allocation for Minimizing AoI
and Energy Consumption in MIMO-NOMA IoT Systems
- Authors: Hongbiao Zhu, Qiong Wu, Qiang Fan, Pingyi Fan, Jiangzhou Wang, and
Zhengquan Li
- Abstract 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, where the transmission rate is not a constant in the SIC process and the noise is in the.
MIMO-NOMA IoT system based on deep reinforcement learning (DRL)
- Score: 27.19355345123451
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications [0.0]
It is important to build IoT-based Machine Learning systems that are robust against data incompleteness while simultaneously being energy efficient.
ENAMLE is a proactive, energy-aware technique for mitigating the impact of concurrent missing data.
We present extensive experimental studies on two distinct datasets that demonstrate the energy efficiency of ENAMLE.
arXiv Detail & Related papers (2024-03-15T15:01:48Z) - Multiagent Reinforcement Learning with an Attention Mechanism for
Improving Energy Efficiency in LoRa Networks [52.96907334080273]
As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions.
We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa)
Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
arXiv Detail & Related papers (2023-09-16T11:37:23Z) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Federated Learning for Energy-limited Wireless Networks: A Partial Model
Aggregation Approach [79.59560136273917]
limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL)
We first devise a novel FL framework with partial model aggregation (PMA)
The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
arXiv Detail & Related papers (2022-04-20T19:09:52Z) - Reinforcement Learning for Minimizing Age of Information in Real-time
Internet of Things Systems with Realistic Physical Dynamics [158.67956699843168]
This paper studies the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices.
A distributed reinforcement learning approach is proposed to optimize the sampling policy.
Simulations with real data of PM 2.5 pollution show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9%.
arXiv Detail & Related papers (2021-04-04T03:17:26Z) - Massive MIMO As an Extreme Learning Machine [83.12538841141892]
A massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM)
By adding random biases to the received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments.
arXiv Detail & Related papers (2020-07-01T04:15:20Z) - Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable
Edge Computing Systems [87.4519172058185]
An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied.
A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem.
Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
arXiv Detail & Related papers (2020-02-20T04:58:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.