Joint user grouping and resource allocation for uplink virtual MIMO systems

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• 作者：Xiaofeng Lu ; Kun Yang ; Wenna Li ; Shaojun Qiu…
• 关键词：virtual MIMO ; user grouping ; average MSE ; system throughput ; resource allocation ; 022304 ; 虚拟MIMO ; 用户配对 ; 平均MSE ; 系统吞吐量
资源分配 ; SC ; FDMA
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：59
• 期：2
• 页码：1-14
• 全文大小：535 KB
• 参考文献：1.Paulraj A J, Gore D A, Nabar R U, et al. An overview of MIMO communications-a key to gigabit wireless. Proc IEEE, 2004, 92: 198–218CrossRef
  2.Nortel. UL virtual MIMO transmission for E-UTRA. In: 3GPP TSG RAN WG Meeting #42, R1-051162, 2005. 1–10
  3.Sesia S, Toufik I, Baker M. LTE, the UMTS Long Term Evolution: From Theory to Practice. New York: John Wiley & Sons, 2009CrossRef
  4.Motorola. Link simulation results for uplink virtual MIMO. In: 3GPP TSG RAN WG Meeting #54, R1-062074, 2006. 1–3
  5.Wang C X, Hong X M, Ge X H, et al. Cooperative MIMO channel models: a survey. IEEE Commun Mag, 2010, 48: 80–87CrossRef
  6.Zhao H T, Emiliano G P, Wei J B, et al. Capacity and resource allocation of cooperative MIMO in ad hoc networks. Phys Commun, 2011, 4: 98–110CrossRef
  7.Zhao H Z, Ma S, Liu F W, et al. A suboptimal multiuser pairing algorithm with low complexity for virtual MIMO systems. IEEE Trans Veh Technol, 2014, 63: 3481–3486CrossRef
  8.Goldsmith A, Jafar S A, Jindal N, et al. Capacity limits of MIMO channels. IEEE J Sel Area Commun, 2003, 21: 684–702CrossRef
  9.Qualcomm Europe. UL system analysis with SDMA. In: 3GPP TSG RAN WG Meeting #45, R1-062052, 2006. 1–8
  10.Fan J C, Li G Y, Yin Q Y, et al. Joint user pairing and resource allocation for LTE uplink transmission. IEEE Trans Wirel Commun, 2012, 11: 2838–2847
  11.Fan B, Wang W B, Lin Y C, et al. Spatial multi-user pairing for uplink virtual-MIMO systems with linear receiver. In: Proceedings of 2009 IEEE Wireless Communications & Networking Conference, Budapest, 2009. 1807–1811
  12.Chen X, Hu H L, Wang H F, et al. Double proportional fair user pairing algorithm for uplink virtual MIMO systems. IEEE Trans Wirel Commun, 2008, 7: 2425–2429CrossRef
  13.Liang J, Liang Q L. Channel selection in virtual MIMO wireless sensor networks. IEEE Trans Veh Technol, 2009, 58: 2249–2257CrossRef
  14.Nortel. UL virtual MIMO system level performance evaluation for E-UTRA. In: 3GPP TSG RAN1 WG1 Meeting #42, R1-051422, 2005. 1–6
  15.Dhakal S, Joonbeom K. Statistical analysis of user-pairing algorithms in virtual MIMO systems. In: Proceedings of Wireless Telecommunications Symposium, Tampa, 2010. 1–5
  16.Ruder M A, Ding D, Dang U L, et al. Joint user grouping and frequency allocation for multiuser SC-FDMA transmission. Phys Commun, 2013, 8: 91–103CrossRef
  17.Li Y, Wang W B, Zhang X, et al. Combined proportional fair and maximum rate scheduling for virtual MIMO. In: Proceedings of 2008 IEEE 68th Vehicular Technology Conference, Calgary, 2008. 1–4
  18.Wang X T, Wang W B, Zhao Z Y, et al. Fairness adjustable grouping multiuser scheduling for MIMO MAC with MMSE-SIC receiver. In: Proceedings of 2008 IEEE Globecom Workshops, New Orleans, 2008. 1–5
  19.Karimi O B, Toutounchian M A, Liu J C, et al. Lightweight user grouping with flexible degrees of freedom in virtual MIMO. IEEE J Sel Area Commun, 2013, 31: 2004–2012CrossRef
  20.Myung H G, Lim J S, Goodman D J. Single carrier FDMA for uplink wireless transmission. IEEE Trans Veh Technol, 2006, 1: 30–38CrossRef
  21.Wong I C, Oteri O, Mc Coy W. Optimal resource allocation in uplink SC-FDMA systems. IEEE Trans Wirel Commun, 2009, 8: 2161–2165CrossRef
  22.Prasad N, Zhang H H, Zhu H, et al. Multi-user MIMO scheduling in the fourth generation cellular uplink. IEEE Trans Wirel Commun, 2013, 12: 4272–4285CrossRef
  23.Kuhn H. The Hungarian method for the assignment problem. Nav Res Log, 1955, 1: 83–97CrossRef
  24.Kuhn H. Variants of the Hungarian method for the assignment problem. Nav Res Log, 1956, 3: 253–258CrossRef
  25.Rojo O. Further bounds for the smallest singular value and the spectral condition number. Comput Math Appl, 1999, 38: 215–228CrossRef MathSciNet MATH
  26.Yu X Y, Gen M S. Introduction to Evolutionary Algorithms. London: Springer-Verlag, 2010. 193–259MATH
  27.Wei G H, Wang F. Linear Programming. Beijing: Higher Education Press, 1989. 205–225
  28.International Telecommunication Union. Guidelines for evaluation of radio transmission technologies for IMT-2000. In: Recommendation ITU-RM, 1225, 1997. 1–60
  
• 作者单位：Xiaofeng Lu (1)
  Kun Yang (2)
  Wenna Li (1)
  Shaojun Qiu (1)
  Hailin Zhang (1)
  
  1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, China
  2. School of Computer Science and Electrical Engineering, University of Essex, Colchester, CO4 3SQ, UK
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

MIMO has become a core technology of 5G network to largely improve system throughput. Due to the cost and size of the user equipment (UE), the application of MIMO uplink is limited by the difficulty in practical implementation at the user side. Virtual MIMO has been widely investigated to solve this problem for wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average mean squared error (MSE) performance in SC-FDMA uplink systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criteria for evaluation of transmission performance, then establish dynamic user grouping and optimal resource allocation problems for hard and elastic average MSE constraints. The proposed joint resource allocation algorithm is evaluated in SC-FDMA uplink scenarios and the results show that it achieves maximum system throughput with average MSE guaranteed for the hard MSE constraint algorithms and the alterable trade-off between system throughput and average MSE for the elastic MSE constraint algorithms. Keywords virtual MIMO user grouping average MSE system throughput resource allocation