Impact of Channel State Misreporting on Multi-user Massive MIMO Scheduling Performance

The robustness of system throughput with scheduling is a critical issue. In this paper, we analyze the sensitivity of multi-user scheduling performance to channel misreporting in systems with massive antennas. The main result is that for the round-robin scheduler combined with max-min power control, the channel magnitude misreporting is harmful to the scheduling performance and has a different impact from the purely physical layer analysis. Specifically, for the homogeneous users that have equal average signal-to-noise ratios (SNRs), underreporting is harmful, while overreporting is beneficial to others. In under-reporting, the asymptotic rate loss on others is derived, which is tight when the number of antennas is huge. One interesting observation in our research is that the rate loss “periodically” increases and decreases as the number of misreporters grows. For the heterogeneous users that have various SNRs, both underreporting and overreporting can degrade the scheduler performance. We observe that strong misreporting changes the user grouping decision and hence greatly decreases some users' rates regardless of others gaining rate improvements, while with carefully designed weak misreporting, the scheduling decision keeps fixed and the rate loss on others is shown to grow nearly linearly with the number of misreporters.

[1]  Limin Xiao,et al.  Analog beam tracking in linear antenna arrays: Convergence, optimality, and performance , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[2]  Cheng Li,et al.  Multi-User Scheduling of the Full-Duplex Enabled Two-Way Relay Systems , 2017, IEEE Transactions on Wireless Communications.

[3]  Michail Matthaiou,et al.  Tightness of Jensen’s Bounds and Applications to MIMO Communications , 2017, IEEE Transactions on Communications.

[4]  Erik G. Larsson,et al.  No Downlink Pilots Are Needed in TDD Massive MIMO , 2016, IEEE Transactions on Wireless Communications.

[5]  Xing Zhang,et al.  Angle-of-arrival based beamforming for FDD massive MIMO , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[6]  Kang G. Shin,et al.  Vulnerability and Protection of Channel State Information in Multiuser MIMO Networks , 2014, CCS.

[7]  Yih-Chun Hu,et al.  A Study on False Channel Condition Reporting Attacks in Wireless Networks , 2014, IEEE Transactions on Mobile Computing.

[8]  Jie Xiong,et al.  SecureArray: improving wifi security with fine-grained physical-layer information , 2013, MobiCom.

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

[10]  Bruno Clerckx,et al.  Recent trend of multiuser MIMO in LTE-advanced , 2013, IEEE Communications Magazine.

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

[12]  Hao Chen,et al.  Exploiting and Defending Opportunistic Scheduling in Cellular Data Networks , 2010, IEEE Transactions on Mobile Computing.

[13]  A. Lee Swindlehurst,et al.  Poisoned feedback: The impact of malicious users in closed-loop multiuser mimo systems , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[15]  Joonho Lee,et al.  A scheduling algorithm combined with zero-forcing beamforming for a multiuser MIMO wireless system , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[16]  박성우 (A)Scheduling algorithm combined with zero-forcing beamforming for a multiuser MIMO wireless system , 2005 .