Sum Rate Analysis for Massive MIMO-NOMA Uplink System With Group-Level Successive Interference Cancellation

He, Xueyun; Huang, Ziqi; Wang, Hong; Song, Rongfang

doi:10.1109/lwc.2023.3266026

Cited by 7 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 34 , 35 ], the authors adopted

estimated by the deep neural network or minimum mean square error method in the SIC process and demonstrated its efficiency. In addition, the BS also knows

; thus, the BS can calculate the power of the received signal transmitted by device m as

…”

Section: System Model and Problem Formulationmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…In [ 34 , 35 ], the authors adopted

estimated by the deep neural network or minimum mean square error method in the SIC process and demonstrated its efficiency. In addition, the BS also knows

; thus, the BS can calculate the power of the received signal transmitted by device m as

…”

Section: System Model and Problem Formulationmentioning

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

View full text Add to dashboard Cite

show abstract

Maximizing energy efficiency for covert communication in NOMA systems with reradiating on antenna

Zeng,

Jia,

et al. 2024

Physical Communication

View full text Add to dashboard Cite

Energy‐Efficiency Maximization in Backscatter Communication‐Based Non‐Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches

Lin,

Wang,

Wang

et al. 2024

IET Signal Processing

View full text Add to dashboard Cite

This paper investigates a backscatter communication (BackCom) based non‐orthogonal multiple access (NOMA) system in a multiple‐input and single‐output (MISO) scenario, where two decoding methods are deployed, including the sum‐capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high‐performance and low‐complexity solutions, respectively. Simulation results indicate that the combination of the sum‐capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity.

show abstract

Sum Rate Analysis for Massive MIMO-NOMA Uplink System With Group-Level Successive Interference Cancellation

Cited by 7 publications

References 14 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

Maximizing energy efficiency for covert communication in NOMA systems with reradiating on antenna

Energy‐Efficiency Maximization in Backscatter Communication‐Based Non‐Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches

Contact Info

Product

Resources

About