Performance Analysis of Uplink Massive MIMO System Over Rician Fading Channel

Massive multiple input multiple output (MIMO) is considered as one of the promising technology to significantly improve the spectral efficiency of fifth generation (5G) networks. In this paper, we analyze the performance of uplink massive MIMO systems over a Rician fading channel and imperfect channel state information (CSI) at a base station (BS). Major Rician fading channel parameters including path-loss, shadowing and multipath fading are considered. Minimum mean square error (MMSE) based channel estimation is done at the BS. Assuming a zero-forcing (ZF) detector, a closed-form expression for the uplink achievable rate is derived and expressed as a function of system and propagation parameters. The impact of the system and propagation parameters on the achievable rate are investigated. Numerical results show that, when the Rician K-factor grows, the uplink achievable sum rate improves. Specifically, when both the number of BS antenna and the Rician K-factor become very large, channel estimation becomes more robust and the interference can be average out and thus, uplink sum rate improves sianificantlv,

[1]  Mérouane Debbah,et al.  Asymptotic analysis of multicell massive MIMO over Rician fading channels , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Michail Matthaiou,et al.  Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means , 2014, IEEE Journal of Selected Topics in Signal Processing.

[3]  Shi Jin,et al.  Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels , 2017, IEEE Journal on Selected Areas in Communications.

[4]  Daya K. Nagar,et al.  Expectations of Functions of Complex Wishart Matrix , 2011 .

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

[6]  Qiang Sun,et al.  Uplink spectral efficiency analysis of multi-cell multi-user massive MIMO over correlated Ricean channel , 2017, Science China Information Sciences.

[7]  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.

[8]  Steven D. Blostein,et al.  MIMO Zero-Forcing Detection Analysis for Correlated and Estimated Rician Fading , 2012, IEEE Transactions on Vehicular Technology.

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

[10]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[11]  Geoffrey Ye Li,et al.  LOS-based Conjugate Beamforming and Power-Scaling Law in Massive-MIMO Systems , 2014, ArXiv.

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

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

[14]  Geoffrey Ye Li,et al.  An Overview of Massive MIMO: Benefits and Challenges , 2014, IEEE Journal of Selected Topics in Signal Processing.

[15]  Nihar Jindal,et al.  Beamforming with Finite Rate Feedback for LOS MIMO Downlink Channels , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.