An Analysis of the Tradeoff Between the Energy and Spectrum Efficiencies in an Uplink Massive MIMO-OFDM System

This brief mainly investigates energy efficiency (EE) and spectrum efficiency (SE) for the uplink massive multiple-input-multiple-output orthogonal frequency-division multiplexing system in a single-cell environment. An approximate SE expression is first derived by employing the maximum ratio combination or zero-forcing detection at the base station. Then, the theoretical tradeoff between EE and SE is established after introducing a realistic power consumption model in consideration of both the radiated power and the circuit power. Based on the tradeoff, the optimal EE with respect to SE is derived using the convex optimization theory. Results show that the optimal EE increases by deploying a suitable number of antennas, multiplexing a reasonable number of users, expanding the system bandwidth, or shrinking the cell radius. Partly different from the EE, the SE corresponding to the optimal EE can be improved by increasing the number of antennas, multiplexing a rational number of users, narrowing the system bandwidth, or shrinking the cell radius.

[1]  Erik G. Larsson,et al.  Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems , 2011, IEEE Transactions on Communications.

[2]  Xiaohu You,et al.  Energy- and Spectral-Efficiency Tradeoff for Distributed Antenna Systems with Proportional Fairness , 2013, IEEE Journal on Selected Areas in Communications.

[3]  Vijay K. Bhargava,et al.  Green Cellular Networks: A Survey, Some Research Issues and Challenges , 2011, IEEE Communications Surveys & Tutorials.

[4]  Thomas L. Marzetta,et al.  Performance of Conjugate and Zero-Forcing Beamforming in Large-Scale Antenna Systems , 2013, IEEE Journal on Selected Areas in Communications.

[5]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[6]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[7]  R. Couillet,et al.  Random Matrix Methods for Wireless Communications: Estimation , 2011 .

[8]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[9]  Geoffrey Ye Li,et al.  Energy-Efficient Configuration of Spatial and Frequency Resources in MIMO-OFDMA Systems , 2013, IEEE Transactions on Communications.

[10]  Ji-Woong Choi,et al.  Energy Efficient Hardware Architecture of LU Triangularization for MIMO Receiver , 2010, IEEE Transactions on Circuits and Systems II: Express Briefs.

[11]  Jaeseok Kim,et al.  Low-Complexity Symbol Detector for MIMO-OFDM-Based Wireless LANs , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[12]  Babak Daneshrad,et al.  Energy-Constrained Link Adaptation for MIMO OFDM Wireless Communication Systems , 2010, IEEE Transactions on Wireless Communications.

[13]  Chong-Yung Chi,et al.  On the Impact of Quantized Channel Feedback in Guaranteeing Secrecy with Artificial Noise: The Noise Leakage Problem , 2009, IEEE Transactions on Wireless Communications.

[14]  Wei-Ping Zhu,et al.  Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems , 2013, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Gaston H. Gonnet,et al.  On the LambertW function , 1996, Adv. Comput. Math..

[16]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.