Achievable Sum Rate Analysis of ZF Receivers in 3D MIMO Systems

Three-dimensional multiple-input multiple-output (3D MIMO) and large-scale MIMO are two promising technologies for upcoming high data rate wireless communications, since the inter-user interference can be reduced by exploiting antenna vertical gain and degree of freedom, respectively. In this paper, we derive the achievable sum rate of 3D MIMO systems employing zero-forcing (ZF) receivers, accounting for log-normal shadowing fading, path-loss and antenna gain. In particular, we consider the prevalent log-normal model and propose a novel closed-form lower bound on the achievable sum rate exploiting elevation features. Using the lower bound as a starting point, we pursue the "large-system" analysis and derive a closed-form expression when the number of antennas grows large for fixed average transmit power and fixed total transmit power schemes. We further model a high-building with several floors. Due to the floor height, different floors correspond to different elevation angles. Therefore, the asymptotic achievable sum rate performances for each floor and the whole building considering the elevation features are analyzed and the effects of tilt angle and user distribution for both horizontal and vertical dimensions are discussed. Finally, the relationship between the achievable sum rate and the number of users is investigated and the optimal number of users to maximize the sum rate performance is determined.

[1]  A. Goldsmith,et al.  Area spectral efficiency of cellular mobile radio systems , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[2]  Lihua Li,et al.  Sum Rate Analysis of MU-MIMO with a 3D MIMO Base Station Exploiting Elevation Features , 2015 .

[3]  Mandy Eberhart,et al.  Digital Communication Over Fading Channels , 2016 .

[4]  B. Mulgrew,et al.  The effects of user distribution on CDMA antenna array receivers , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

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

[6]  Michail Matthaiou,et al.  Sum Rate Analysis of ZF Receivers in Distributed MIMO Systems , 2013, IEEE Journal on Selected Areas in Communications.

[7]  Rongfang Song,et al.  Distributed Compressive Sensing Based Channel Feedback Scheme for Massive Antenna Arrays with Spatial Correlation , 2014, KSII Trans. Internet Inf. Syst..

[8]  R. Ganesh,et al.  Effect of non-uniform traffic distributions on performance of a cellular CDMA system , 1997, Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications.

[9]  Jun Cai,et al.  MMSE Transmit Optimization for Multiuser Multiple-Input Single-Output Broadcasting Channels in Cognitive Radio Networks , 2013, KSII Trans. Internet Inf. Syst..

[10]  Jin Yang,et al.  Research on the Energy Hole Problem Based on Non-uniform Node Distribution for Wireless Sensor Networks , 2012, KSII Trans. Internet Inf. Syst..

[11]  Mats Viberg,et al.  Throughput Optimization for MISO Interference Channels via Coordinated User-Specific Tilting , 2012, IEEE Communications Letters.

[12]  Yongwha Chung,et al.  A Cost-Effective Pigsty Monitoring System Based on a Video Sensor , 2014, KSII Trans. Internet Inf. Syst..

[13]  Pavel Pechac,et al.  The 3D approximation of antenna radiation patterns , 2003 .

[14]  Luis M. Correia,et al.  A 3D interpolation method for base-station-antenna radiation patterns , 2001 .

[15]  G. Sergiadis,et al.  A novel technique for the approximation of 3-D antenna radiation patterns , 2005, IEEE Transactions on Antennas and Propagation.

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

[17]  Anders Furuskar,et al.  Downtilted Base Station Antennas - A Simulation Model Proposal and Impact on HSPA and LTE Performance , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[18]  Mérouane Debbah,et al.  Optimal 3D cell planning: A random matrix approach , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[19]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[20]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[21]  Fredrik Tufvesson,et al.  Polarized MIMO channels in 3-D: models, measurements and mutual information , 2006, IEEE Journal on Selected Areas in Communications.

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

[23]  Wei Xu,et al.  Outage Probability Analysis of Multiuser MISO Systems Exploiting Joint Spatial Diversity and Multiuser Diversity with Outdated Feedback , 2011, KSII Trans. Internet Inf. Syst..

[24]  Mérouane Debbah,et al.  Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need? , 2013, IEEE Journal on Selected Areas in Communications.