scholarly journals On the Performance Evaluations of Cooperative Retransmission Scheme for Cell-Edge Users of URLLC in Multi-Carrier Downlink NOMA Systems

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7052
Author(s):  
Won-Jae Ryu ◽  
Jae-Woo Kim ◽  
Soo-Young Shin ◽  
Dong-Seong Kim
Keyword(s):  
Multiple Access ◽  
Prior Information ◽  
Loss Rate ◽  
High Reliability ◽  
Packet Loss Rate ◽  
Performance Evaluations ◽  
Cell Edge ◽  
Low Snr ◽  
Reliability Constraint ◽  
Cell Edge User

Non-orthogonal multiple access (NOMA) has a key feature that the cell-center user (CCU) has prior information about the messages of the cell-edge user(CEU) in the same user-pair. It means that CCU can be used for retransmission when the CEU requests retransmission. As ultra-reliability and low-latency communication (URLLC) requires high-reliability constraints (e.g., 99.999%), using CCU for retransmission can be useful to satisfy the reliability constraint. In this study, to ensure the reliability of CEU, cooperative retransmission (CR) scheme for downlink NOMA systems is proposed. And the CR scheme is evaluated with Block error rate (BLER) considering reliability and with packet loss rate (PLR) in terms of reliability and latency constraints. And the evaluation results showed that the proposed CR scheme can satisfy the target BLER for URLLC low SNR compared to the conventional retransmission scheme, and showed the improved PLR compared to the conventional retransmission scheme in low SNRs.

scholarly journals Stochastic Optimal Control for Buffer-Aided Cooperative Relaying Systems Using Nonorthogonal Multiple Access

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Zhishan Deng ◽  
Qianyun Gong ◽  
Quanzhong Li ◽  
Jiayin Qin
Keyword(s):  
Optimization Algorithm ◽  
Multiple Access ◽  
Asymptotic Optimality ◽  
Low Complexity ◽  
Base Station ◽  
Cooperative Relaying ◽  
Cell Edge ◽  
Data Buffer ◽  
Lyapunov Optimization ◽  
Cell Edge User

In this paper, a multiple-cluster downlink multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) system is considered. In each cluster, there are one central user and one cell-edge user. The central user has a data buffer with finite storage units, which will decode the cell-edge user’s message and store it at the data buffer. To enhance the performance of the cell-edge user, the central user operates as a relay and helps forward the message to the cell-edge user. Our objective is to maximize the long-term average sum rates for the cell-edge users by designing the beamforming vectors and online power control, under the constraints of the data buffer causality, required information rates for central users, and transmit power at the base station and central users. Based on the current buffer state and the channel state information, we propose a low-complexity online Lyapunov optimization algorithm combined with a constrained concave-convex procedure (CCCP) to solve the causal and nonconvex problem. Furthermore, we verify the asymptotic optimality of the proposed online Lyapunov optimization algorithm. Simulation results demonstrate that our proposed scheme performs better than the greedy algorithm and the orthogonal multiple access (OMA) scheme.

Packet loss rate monitoring model of IOT based on differential evolution algorithm

2021 ◽  
pp. 1-12
Author(s):  
Yinghua Feng ◽  
Wei Yang
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Packet Loss ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Packet Loss Rate ◽  
Data Space ◽  
Monitoring Model ◽  
Evolution Algorithm ◽  
The Internet Of Things

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IOT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

Performance Analysis Of Non-Orthogonal Multiple Access Technique Using Simulink

2021 ◽  
Vol 9 (7) ◽  
pp. 921-928
Keyword(s):  
Multiple Access ◽  
Additive White Gaussian Noise ◽  
Multipath Fading ◽  
Rician Fading ◽  
Similar Data ◽  
Cell Edge ◽  
Novel Technology ◽  
Cell Coverage ◽  
Power Domain ◽  
Rician Fading Channel

Non orthogonal multiple access (NOMA) is a novel technology for transmission of the data with similar data-rate for users both at the cell center and the at cell edge. Due to its good cell coverage and large transmission bandwidth enables reliable communication for users located at the cell edge. The signals for the users are transmitted through entire spatial bandwidth by multiplexing in the power domain. This work presents the Simulink implementation of non-orthogonal multiple access over additive white Gaussian noise channel, Rayleigh and Rician fading channel. The NOMA Transmitter and the receiver blocks with SIC are implemented for two user and three user environments. Multipath fading environment was providing using parameter settings available in theblocks. Performance of the models implemented are analyzed with respect to Bit error rate (BER) v/s Eb/Noplots comparing using various modulation schemes over various channels. The results are tabulated and analyzed.

scholarly journals Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kehua Zhao ◽  
Yourong Chen ◽  
Siyi Lu ◽  
Banteng Liu ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Transmission Delay ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Sensing Coverage ◽  
Mobile Sensor Node ◽  
Monitoring Area

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

Transmit beamformer based performance analysis and diversity gains of cell edge user in cooperative MISO-NOMA system

2020 ◽  
Vol 41 ◽  
pp. 101102 ◽  
Author(s):  
Mujtaba Ghous ◽  
Ziaul Haq Abbas ◽  
Ghulam Abbas ◽  
Ahmad Kamal Hassan ◽  
Muhammad Moinuddin
Keyword(s):  
Performance Analysis ◽  
Cell Edge ◽  
Diversity Gains ◽  
Cell Edge User

Cooperative multiple access in cognitive radios to enhance QoS for cell edge primary users: asynchronous algorithm and convergence

2015 ◽  
Vol 61 (4) ◽  
pp. 631-643
Author(s):  
Sang-wook Han ◽  
Hoon Kim ◽  
Youngnam Han ◽  
John M. Cioffi ◽  
Victor C. M. Leung ◽  
...  
Keyword(s):  
Multiple Access ◽  
Cognitive Radios ◽  
Asynchronous Algorithm ◽  
Cell Edge ◽  
Primary Users
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