1 research outputs found
Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals
Deploying massive number of antennas at the base station side can boost the
cellular system performance dramatically. Meanwhile, it however involves
significant additional radio-frequency (RF) front-end complexity, hardware cost
and power consumption. To address this issue, the
beamspace-multiple-input-multiple-output (beamspace-MIMO) based approach is
considered as a promising solution. In this paper, we first show that the
traditional beamspace-MIMO suffers from spatial power leakage and imperfect
channel statistics estimation. A beam combination module is hence proposed,
which consists of a small number (compared with the number of antenna elements)
of low-resolution (possibly one-bit) digital (discrete) phase shifters after
the beamspace transformation to further compress the beamspace signal
dimensionality, such that the number of RF chains can be reduced beyond
beamspace transformation and beam selection. The optimum discrete beam
combination weights for the uplink are obtained based on the branch-and-bound
(BB) approach. The key to the BB-based solution is to solve the embodied
sub-problem, whose solution is derived in a closed-form. Based on the solution,
a sequential greedy beam combination scheme with linear-complexity (w.r.t. the
number of beams in the beamspace) is proposed. Link-level simulation results
based on realistic channel models and long-term-evolution (LTE) parameters are
presented which show that the proposed schemes can reduce the number of RF
chains by up to with a one-bit digital phase-shifter-network.Comment: Submitted to IEEE Trans. Signal Proces