Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs

doi:10.3969/j.issn.1673-5188.2018.02.003

ZTE Communications ›› 2018, Vol. 16 ›› Issue (2): 9-15.DOI: 10.3969/j.issn.1673-5188.2018.02.003

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Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs

JIN Yaqi, XU Xiaodong, TAO Xiaofeng

Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-02-28 Online:2018-06-25 Published:2019-12-12
About author:JIN Yaqi (yqjin@bupt.edu.cn) received the B.S. degree in communication engineering from Harbin Institute of Technology (HIT), China in 2015. She is currently pursuing the M.S. degree in information and communication engineering with Beijing University of Posts and Telecommunications (BUPT), China. Her research interests cover wireless communication, QoS guaranteed techniques, resource management including resource allocation and load balancing. She has published several papers at international conferences.|XU Xiaodong (xuxiaodong@bupt.edu.cn) received his B.S. degree in information and communication engineering and master’s degree in communication and information system from Shandong University, China in 2001 and 2004 respectively. He received his Ph.D. degree of circuit and system from Beijing University of Posts and Telecommunications (BUPT), China in 2007. He is currently a professor of BUPT. He has coauthored seven books and more than 120 journal and conference papers. He is also the inventor or co-inventor of 37 granted patents. His research interests cover network architecture, moving network, coordinated multi-point and mobile network virtualization. His reasearch is supported by Beijing Nova Programme on Mobile Networking.|TAO Xiaofeng (taoxf@bupt.edu.cn) received the B.S. degree in electrical engineering from Xi’an Jiaotong University, China in 1993, and the M.S.E.E. and Ph.D. degrees in telecommunication engineering from Beijing University of Posts and Telecommunications (BUPT), China in 1999 and 2002, respectively. He was a visiting professor with Stanford University, USA from 2010 to 2011, was the Chief Architect with the Chinese National FUTURE Fourth-Generation (4G) TDD Working Group from 2003 to 2006, and established the 4G TDD CoMP Trial Network in 2006. He is currently a professor with BUPT and a fellow of the Institution of Engineering and Technology. He is the inventor or co-inventor of 50 patents and the author or co-author of 120 papers in 4G and beyond 4G. He is currently involved in fifth-generation networking technology and mobile network security.
Supported by:
This paper is supported by the National Science and Technology Major Project under Grant No. 2016ZX03001009-003 and the Nature and Science Foundation of China under Grants Nos. 61471068 and 111 Project of China B16006.

Abstract

Abstract:

To meet the booming development of diversified services and new applications in the future, the fifth-generation mobile communication system (5G) has arisen. Resources are increasingly scarce in the dynamic time-varying of 5G networks. Allocating resources effectively and ensuring quality of service (QoS) requirements of multi-services come to be a research focus. In this paper, we utilize effective capacity to build a utility function with multi-QoS metrics, including rate, delay bound and packet loss ratio. Taking advantage of opportunity cost (OC), we also propose a multi-QoS guaranteed resource allocation algorithm for multi-services to consider the future condition of system. In the algorithm, according to different business characteristics and the theory of OC, we propose different selection conditions for QoS users and best effort (BE) users to choose more reasonable resources. Finally, simulation results show that our proposed algorithm achieves superior system utility and relatively better fairness in multi-service scenarios.

Key words: utility function, effective capacity, opportunity cost, QoS guaranteed, resource allocation

JIN Yaqi, XU Xiaodong, TAO Xiaofeng. Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs[J]. ZTE Communications, 2018, 16(2): 9-15.

Figures/Tables 7

Figure 1. The system model of wireless network for multi-QoS mobile services.

Figure 2. The utility function trend of four classes.

Table 1 Simulation setting: system parameters

System parameters
Number of BSs	7
Number of Subchannels	50
Maximum power of BSs	46 dBm
Carrier Frequency	2 GHz
Bandwidth	10 MHz
Cell average radius	500 m
Pathloss model	$PL = 128.1 + 37.6 lo g 10 d, d (km)$
Shadowing standard deviation	8 dB
Shadowing correlation distance	50 m
Fast fading	Rayleigh fading
Noise density	-174 dBm/Hz
Average arriving rate	150 kbit/s

Table 1 Simulation setting: system parameters

System parameters
Number of BSs	7
Number of Subchannels	50
Maximum power of BSs	46 dBm
Carrier Frequency	2 GHz
Bandwidth	10 MHz
Cell average radius	500 m
Pathloss model	$PL = 128.1 + 37.6 lo g 10 d, d (km)$
Shadowing standard deviation	8 dB
Shadowing correlation distance	50 m
Fast fading	Rayleigh fading
Noise density	-174 dBm/Hz
Average arriving rate	150 kbit/s

Table 2 Simulation setting: QoS parameters

Traffic Type	Effective bandwidth (kbit/s)	Delay bound (ms)	Packet loss ratio
VoIP	150	[20, 50]	10^-2
IPTV	200	[50, 100]	10^-3
HTTP	--	[100, 200]	10^-6
TCP	--	[300, 500]	10^-6

Figure 3. Sum utility versus the number of users.

Figure 4. System throughput versus the number of users.

Figure 5. Fairness versus number of users.

References 15

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Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs

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