User Selection for NOMA-Based MIMO With Physical-Layer Network Coding in Internet of Things Applications

Yilmaz, Saadet Simay; Özbek, Berna; İlgüy, Mert; Okyere, Bismark; Musavian, Leila; González, Jonathan

doi:10.1109/jiot.2021.3079157

Cited by 12 publications

(2 citation statements)

References 34 publications

(28 reference statements)

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“…In [ 23 ], Yilmaz et al proposed a user selection algorithm for the MIMO-NOMA IoT system to improve the sum data rate, and adopted the physical layer network coding (PNC) to improve the spectral efficiency. In [ 24 ], Shi et al considered the downlink of the MIMO-NOMA IoT networks and studied the outage probability and goodput of the system with the Kronecker model.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

Wu,

Zhang,

Zhu

et al. 2023

Sensors

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Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

Wu,

Zhang,

Zhu

et al. 2023

Sensors

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“…MU-mMIMO with a PNC scheme in the uplink transmission has been presented in [3], where the BER performance of mMIMO joined with PNC performed better than mMIMO without PNC. The non-orthogonal multiple access (NOMA) uplink-based MIMO system with PNC was proposed to improve the spectral efficiency and reduce the delay to fulfill the requirements of future IoT applications [22]. To the best of our knowledge, no research has been presented on the use of joint PNC and mMIMO over the mmWave channel.…”

Section: Introductionmentioning

confidence: 99%

Transmission of Physical Layer Network Coding Based on Massive MIMO over Millimeter Wave Channel

A. Mahmood,

A. Kadhim,

S. Al-raweshidy

2024

Iraqi Journal of ICT

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Massive Multiple Input Multiple Output system and physical layer network coding (PNC) are two important technologies that are considered in new generation networks. In this paper we investigate the effect of increasing the number of antenna elements at user nodes and relay node in the presence of PNC. Two-way wireless relay transmission is considered in conjunction with massive number of antennas at the relay node assuming millimeter wave (mmWave) band channel environment. End-to-end transmission is considered using 3-D QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) channel model. The performance measures used are the bit error rate (BER) and throughput. Up to 100% increase in throughput of PNC coded system is obtained. The BER performance is improved only when the number of relay node antennas is much higher than that of the user node. Combining mMIMO with PNC gives better throughput without affecting the BER at high SNR. This is achieved with reasonable number of antennas at the user nodes.

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Performance analysis of physical-layer network coding with QPSK modulation in wireless IoT networks

Yeom

Jin

et al. 2022

ICT Express

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User Selection for NOMA-Based MIMO With Physical-Layer Network Coding in Internet of Things Applications

Cited by 12 publications

References 34 publications

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems

Transmission of Physical Layer Network Coding Based on Massive MIMO over Millimeter Wave Channel

Performance analysis of physical-layer network coding with QPSK modulation in wireless IoT networks

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