Massive MIMO versus small-cell systems: Spectral and energy efficiency comparison

In this paper, we study the downlink performance of two important 5G network architectures, i.e. massive multiple-input multiple-output (M-MIMO) and small-cell densification. We propose a comparative modeling for the two systems, where the user and antenna/base station (BS) locations are distributed according to Poisson point processes (PPPs). We then study the SIR distribution and the outage rate of each network. By comparing these results, we observe that for user-average spectral efficiency, small-cell densification is favorable in crowded areas with moderate to high user density and M-MIMO with low user density. However, small-cell systems outperform M-MIMO in all cases when the performance metric is the energy efficiency. The results of this paper are useful for the optimal design of practical 5G networks.

[1]  Z. Néda,et al.  On the size-distribution of Poisson Voronoi cells , 2004, cond-mat/0406116.

[2]  Sumei Sun,et al.  Stochastic Geometry-Based Performance Bounds for Non-Fading and Rayleigh Fading Ad Hoc Networks , 2015, ArXiv.

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

[4]  Kaibin Huang,et al.  Coverage and Economy of Cellular Networks with Many Base Stations , 2012, IEEE Communications Letters.

[5]  Emil Björnson,et al.  Massive MIMO Systems With Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits , 2013, IEEE Transactions on Information Theory.

[6]  David Gesbert,et al.  A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems , 2012, IEEE Journal on Selected Areas in Communications.

[7]  Sumei Sun,et al.  Pecuniary Efficiency of Distributed Antenna Systems , 2015, IEEE Communications Letters.

[8]  Li Ping,et al.  Data-Aided Channel Estimation in Large Antenna Systems , 2014, IEEE Transactions on Signal Processing.

[9]  Sergio Verdu,et al.  Multiuser Detection , 1998 .

[10]  Thomas L. Marzetta,et al.  Pilot Contamination and Precoding in Multi-Cell TDD Systems , 2009, IEEE Transactions on Wireless Communications.

[11]  David Tse,et al.  Linear Multiuser Receivers: Effective Interference, Effective Bandwidth and User Capacity , 1999, IEEE Trans. Inf. Theory.

[12]  Romain Couillet,et al.  Random Matrix Theory Methods for Wireless Communications , 2011 .

[13]  Kerstin Vogler,et al.  Table Of Integrals Series And Products , 2016 .

[14]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[15]  Bongyong Song,et al.  A holistic view on hyper-dense heterogeneous and small cell networks , 2013, IEEE Communications Magazine.

[16]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[17]  Siyuan Zhou Finite Random Matrix Theory Analysis of Multiple Antenna Communication Systems , 2015 .

[18]  Tommy Svensson,et al.  The role of small cells, coordinated multipoint, and massive MIMO in 5G , 2014, IEEE Communications Magazine.

[19]  Thomas L. Marzetta,et al.  Multiple-antenna channel hardening and its implications for rate feedback and scheduling , 2004, IEEE Transactions on Information Theory.

[20]  Sean A. Ramprashad,et al.  Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas , 2011, IEEE Transactions on Wireless Communications.

[21]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

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