Lower Bound on the Sum-rate of Decremental Beam Selection Algorithm for Beamspace MIMO Systems

In general, the zero-forcing (ZF) precoding suffers from a severe receive signal-to-noise ratio (SNR) degradation in the high interference regime. However, recent evidences from realistic measurements demonstrated that millimeter wave (mmWave) systems are mainly noise-limited as high gain antennas behave as spatial filters to the interference signal. This makes ZF precoding equally attractive as that of other linear precoding counterparts. Considering ZF precoding, this paper aims to derive a lower bound on the sum-rate achieved by a decremental beam selection (BS) algorithm in a beamspace MIMO (B-MIMO) system operating at mmWave frequencies. This bound relates Frobenious norms of precoding matrices of full and reduced dimensional (i.e. after BS) B-MIMO systems through a deterministic square-hyperbolic function. Note that, both ZF precoding and decremental BS are not new concepts. However, the derived sum-rate bound provides a new insight to the topic. Given a particular full dimensional B-MIMO channel, the presented bound can be used to understand limits of BS algorithms.

[1]  David James Love,et al.  Capacity Limits of Multiple Antenna Multicasting Using Antenna Subset Selection , 2008, IEEE Transactions on Signal Processing.

[2]  F. Hoog,et al.  Subset selection for matrices , 2007 .

[3]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[4]  Moe Z. Win,et al.  Capacity of MIMO systems with antenna selection , 2001, IEEE Transactions on Wireless Communications.

[5]  Petros Drineas,et al.  CUR matrix decompositions for improved data analysis , 2009, Proceedings of the National Academy of Sciences.

[6]  Akbar M. Sayeed,et al.  Beamspace MIMO for high-dimensional multiuser communication at millimeter-wave frequencies , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[7]  Linglong Dai,et al.  Near-Optimal Beam Selection for Beamspace MmWave Massive MIMO Systems , 2016, IEEE Communications Letters.

[8]  K. V. Srinivas,et al.  A Beam Selection Algorithm for Millimeter-Wave Multi-User MIMO Systems , 2018, IEEE Communications Letters.

[9]  Christos Masouros,et al.  Low RF-Complexity Millimeter-Wave Beamspace-MIMO Systems by Beam Selection , 2015, IEEE Transactions on Communications.

[10]  Josef A. Nossek,et al.  A Comparison of Hybrid Beamforming and Digital Beamforming With Low-Resolution ADCs for Multiple Users and Imperfect CSI , 2017, IEEE Journal of Selected Topics in Signal Processing.

[11]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..

[12]  Matthias Pätzold,et al.  Sparse multipath channels: Modelling, analysis, and simulation , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[13]  Akbar M. Sayeed,et al.  Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements , 2013, IEEE Transactions on Antennas and Propagation.

[14]  Reiner S. Thomä,et al.  Measurements Based Interference Analysis at Millimeter Wave Frequencies in an Indoor Scenario , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).