Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/716987
Title: Optimal Power Allocation for SCMA Downlink Systems Based on Maximum Capacity
Other Titles: IEEE Transactions on Communications
Authors: Shuai Han|Yiteng Huang|Weixiao Meng|Cheng Li|Nuo Xu|Dageng Chen
subject: Optimal power allocation|convex optimization|maximum capacity|sparse code multiple access
Year: -1-Uns- -1
Abstract: Sparse code multiple access (SCMA) is a novel type of non-orthogonal multiple access technology that combines the concepts of CDMA and OFDMA. The advantages of SCMA include high capacity, low time delay, and high date rate. In this paper, a power allocation algorithm is proposed for SCMA downlink systems where each tone is taken by more than one user to maximize the system’s sum capacity. In SCMA systems, users are divided into different user groups. Thus, our proposed algorithm includes three-level power allocation. Since the power allocation problem is non-convex, the complexity of finding the optimal solutions is prohibitive. The Lagrange dual decomposition method is employed to efficiently solve the non-convex optimization problem. Results show that the optimized algorithm can significantly improve the sum capacity.
URI: http://localhost/handle/Hannan/716987
ISBN: 0090-6778
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

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Title: Optimal Power Allocation for SCMA Downlink Systems Based on Maximum Capacity
Other Titles: IEEE Transactions on Communications
Authors: Shuai Han|Yiteng Huang|Weixiao Meng|Cheng Li|Nuo Xu|Dageng Chen
subject: Optimal power allocation|convex optimization|maximum capacity|sparse code multiple access
Year: -1-Uns- -1
Abstract: Sparse code multiple access (SCMA) is a novel type of non-orthogonal multiple access technology that combines the concepts of CDMA and OFDMA. The advantages of SCMA include high capacity, low time delay, and high date rate. In this paper, a power allocation algorithm is proposed for SCMA downlink systems where each tone is taken by more than one user to maximize the system’s sum capacity. In SCMA systems, users are divided into different user groups. Thus, our proposed algorithm includes three-level power allocation. Since the power allocation problem is non-convex, the complexity of finding the optimal solutions is prohibitive. The Lagrange dual decomposition method is employed to efficiently solve the non-convex optimization problem. Results show that the optimized algorithm can significantly improve the sum capacity.
URI: http://localhost/handle/Hannan/716987
ISBN: 0090-6778
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502712.pdf1.51 MBAdobe PDFThumbnail
Preview File
Title: Optimal Power Allocation for SCMA Downlink Systems Based on Maximum Capacity
Other Titles: IEEE Transactions on Communications
Authors: Shuai Han|Yiteng Huang|Weixiao Meng|Cheng Li|Nuo Xu|Dageng Chen
subject: Optimal power allocation|convex optimization|maximum capacity|sparse code multiple access
Year: -1-Uns- -1
Abstract: Sparse code multiple access (SCMA) is a novel type of non-orthogonal multiple access technology that combines the concepts of CDMA and OFDMA. The advantages of SCMA include high capacity, low time delay, and high date rate. In this paper, a power allocation algorithm is proposed for SCMA downlink systems where each tone is taken by more than one user to maximize the system’s sum capacity. In SCMA systems, users are divided into different user groups. Thus, our proposed algorithm includes three-level power allocation. Since the power allocation problem is non-convex, the complexity of finding the optimal solutions is prohibitive. The Lagrange dual decomposition method is employed to efficiently solve the non-convex optimization problem. Results show that the optimized algorithm can significantly improve the sum capacity.
URI: http://localhost/handle/Hannan/716987
ISBN: 0090-6778
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502712.pdf1.51 MBAdobe PDFThumbnail
Preview File