Joint Scheduling and Power Allocation with Per-User Rate Constraints for Uplink MU-MIMO OFDMA Systems

This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple access (OFDMA) systems. The objective is to minimize the number of occupied resource blocks (RBs) subject to per-user rate constraints. The prob...

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Published in:IEEE Vehicular Technology Conference pp. 1 - 5
Main Authors: Zhang, Lin, Han, Shengqian, Yang, Chenyang
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2023
Subjects:
Artificial neural networks
deep learning
Learning systems
MU-MIMO OFDMA
OFDM
power allocation
Programming
Scheduling
Simulation
Vehicular and wireless technologies
ISSN:2577-2465
Online Access:Get full text
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Abstract This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple access (OFDMA) systems. The objective is to minimize the number of occupied resource blocks (RBs) subject to per-user rate constraints. The problem is a mixed integer and non-convex programming problem. We first propose a hierarchical algorithm to find a solution, where in the outer layer the number of RBs are reduced in a greedy manner while in the inner layer the power allocation and scheduling of users are optimized to determine which RB should be unoccupied. The inner problem is non-convex high-complexity problem. To reduce the complexity, we further employ a deep neural network to learn the solution of the inner problem. Simulation results show that compared to two baseline methods, the proposed method can effectively reduce the occupied RBs with much lower complexity.
AbstractList This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple access (OFDMA) systems. The objective is to minimize the number of occupied resource blocks (RBs) subject to per-user rate constraints. The problem is a mixed integer and non-convex programming problem. We first propose a hierarchical algorithm to find a solution, where in the outer layer the number of RBs are reduced in a greedy manner while in the inner layer the power allocation and scheduling of users are optimized to determine which RB should be unoccupied. The inner problem is non-convex high-complexity problem. To reduce the complexity, we further employ a deep neural network to learn the solution of the inner problem. Simulation results show that compared to two baseline methods, the proposed method can effectively reduce the occupied RBs with much lower complexity.
Author Han, Shengqian
Zhang, Lin
Yang, Chenyang
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  givenname: Chenyang
  surname: Yang
  fullname: Yang, Chenyang
  email: cyyang@buaa.edu.cn
  organization: Beihang University,Beijing,China
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Snippet This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple...
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SubjectTerms Artificial neural networks
deep learning
Learning systems
MU-MIMO OFDMA
OFDM
power allocation
Programming
Scheduling
Simulation
Vehicular and wireless technologies
Title Joint Scheduling and Power Allocation with Per-User Rate Constraints for Uplink MU-MIMO OFDMA Systems
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