A Distributed Energy Efficiency Optimization Scheme for Cooperative Virtual MIMO System

In this work, we propose a distributed energy efficiency (EE) optimization scheme for cooperative virtual multiple input and multiple output (V-MIMO) system. User equipments (UEs) with single antenna form cooperative V-MIMO groups to achieve their target spectral efficiency (SE) and improve their EE in a distributed manner. We decompose the scheme into two sections. Firstly, we deduce closed-form expressions of the optimal power and target SE allocations for each UE on each RB within a V-MIMO group. Then, based on this deduced expressions, a coalition formation game is utilized to choose a proper set of UEs to form V-MIMO groups. A convergent iteration of merge-split operations is adopted according to the Pareto order of UEs' EE. Simulation results show that this proposed scheme increases the EE of UEs, especially when the number of UEs is large and their target SE is high.

[1]  Geoffrey Ye Li,et al.  Energy Efficient Design in Wireless OFDMA , 2008, 2008 IEEE International Conference on Communications.

[2]  Andreas Witzel,et al.  A Generic Approach to Coalition Formation , 2007, IGTR.

[3]  Rohit U. Nabar,et al.  Introduction to Space-Time Wireless Communications , 2003 .

[4]  Guowang Miao,et al.  On Optimal Energy-Efficient Multi-User MIMO , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[5]  Mohsen Guizani,et al.  Double proportional fair user pairing algorithm for uplink virtual MIMO systems , 2008, IEEE Transactions on Wireless Communications.

[6]  Sudharman K. Jayaweera,et al.  V-BLAST-Based Virtual MIMO for Distributed Wireless Sensor Networks , 2007, IEEE Transactions on Communications.

[7]  Andrea J. Goldsmith,et al.  Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[8]  Yueming Cai,et al.  A Cooperative Communication Scheme Based on Coalition Formation Game in Clustered Wireless Sensor Networks , 2012, IEEE Transactions on Wireless Communications.

[9]  Andrea J. Goldsmith,et al.  Degrees of freedom in adaptive modulation: a unified view , 2001, IEEE Trans. Commun..

[10]  Zhu Han,et al.  Coalitional game theory for communication networks , 2009, IEEE Signal Processing Magazine.

[11]  Yueming Cai,et al.  A Coalition Formation Framework for Transmission Scheme Selection in Wireless Sensor Networks , 2011, IEEE Transactions on Vehicular Technology.

[12]  Geoffrey Ye Li,et al.  Energy-efficient link adaptation in frequency-selective channels , 2010, IEEE Transactions on Communications.

[13]  Andrea J. Goldsmith,et al.  Energy-constrained modulation optimization , 2005, IEEE Transactions on Wireless Communications.

[14]  John M. Cioffi,et al.  Optimal Resource Allocation for OFDMA Downlink Systems , 2006, 2006 IEEE International Symposium on Information Theory.

[15]  Walid Saad,et al.  Author manuscript, published in "IEEE Transactions on Wireless Communications (2009) Saad-ITransW-2009" A Distributed Coalition Formation Framework for Fair User Cooperation in Wireless Networks , 2022 .

[16]  Wenbo Wang,et al.  Spatial Multi-User Pairing for Uplink Virtual-MIMO Systems with Linear Receiver , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[17]  Gustavo de Veciana,et al.  A cross-layer approach to energy efficiency for adaptive MIMO systems exploiting spare capacity , 2009, IEEE Transactions on Wireless Communications.

[18]  Cong Xiong,et al.  Energy-efficient wireless communications: tutorial, survey, and open issues , 2011, IEEE Wireless Communications.