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The joint linear precoder and decoder minimum mean squared error (MMSE) design represents a low complexity yet powerful solution for spatial multiplexing MIMO systems. Its performance can be further boosted through optimally selecting the number of spatial streams to be used according to the available channel state information (CSI), so-called spatial-mode selection. The performance of both the latter MMSE design and the related spatial-mode selection criteria, however, critically depends on the availability of timely CSI at both transmitter and receiver. In practice, the latter assumption can be severely challenged, due to channel time variations that lead to imperfect CSI at the transmitter. State-of-the-art designs mistakenly use this imperfect CSI to design the linear precoder and rely on the receiver to reduce the induced degradation. We have alternatively proposed a robust Bayesian joint linear precoder and decoder solution that takes into account the uncertainty on the true channel, given the channel mean feedback at the transmitter. In this paper, we further improve the performance of our aforementioned robust design using a new spatial-mode selection criterion based on channel mean feedback. We also illustrate, via Monte-Carlo analysis, the robustness of the resulting improved design to channel time variations, which outperforms the state-of-the-art approach.