A Near Optimal Scheduling Algorithm for Efficient Radio Resource Management in Multi-user MIMO Systems

Multiple input multiple output (MIMO) systems have high potential to achieve maximum channel capacity in wireless communication systems. Multi-user (MU) MIMO network with transmit antennas T x at base station can schedule as many single antenna users in one time slot. This necessitates incorporation...

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Veröffentlicht in:Wireless personal communications Jg. 106; H. 3; S. 1411 - 1427
Hauptverfasser: Naeem, Muddasar, Bashir, Sajid, Ullah, Zaib, Syed, Aqeel A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Antennas
Channel capacity
Communications Engineering
Computer Communication Networks
Computer simulation
Design factors
Engineering
Maximization
MIMO (control systems)
Networks
Optimization
Production scheduling
Radio equipment
Resource management
Schedules
Scheduling
Signal,Image and Speech Processing
Wireless communication systems
Wireless communications
Wireless networks
Channel capacity
Next generation networks
Multiple antenna systems
Multi-user MIMO
Scheduling
Sumrate
Orthogonality
Fairness
ISSN:0929-6212, 1572-834X
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Zusammenfassung:Multiple input multiple output (MIMO) systems have high potential to achieve maximum channel capacity in wireless communication systems. Multi-user (MU) MIMO network with transmit antennas T x at base station can schedule as many single antenna users in one time slot. This necessitates incorporation of a user scheduling strategy. Maximizing sumrate performance without under utilization of channel resources is a primary objective of such scheduling algorithms. Another key design factor for such techniques is to optimize fairness i.e. allowing equal opportunity to all communicating entities. In this paper, we present a simple approach to solve the scheduling problem in MU-MIMO systems while addressing the conflicting objectives of sumrate and fairness. Two variants of the proposed method are also discussed that provide options for maximizing the cumulative sumrate and improving the fairness. Simulations validate the proposed algorithm as a better choice over other conventional methods as it achieves optimal performance with relatively less computational complexity and without compromising fairness. Fairness results are verified by average sumrate method and Jain’s Fariness Index.
Bibliographie:ObjectType-Article-1
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-019-06222-3