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This paper considers a worst-case precoder design problem for multiple-input multiple-output (MIMO) wireless communication systems with imperfect channel knowledge at the transmitter. When the MIMO channel is a full-row rank matrix, which arises generically when the number nT of transmit antennas is greater or equal to the number nR of receive antennas, and when channel state information is known perfectly at the transmitter, the channel can be equalized exactly by employing a precoder equal to the channel pseudo-inverse. However, in actual systems, it is necessary to take into account the channel estimation error. We consider here a worst-case precoder design problem where the goal is to find the precoder minimizing the equalization mean-square error for the least favorable channel located in a ball centered about the estimated channel. Lagrangian optimization is used to convert this min-max problem into a min-min convex minimization problem over a convex domain which can be solved in closed form. The robust precoder and associated least favorable channel have an intuitive interpretation since the least-favorable channel zeroes out the weakest subchannels, and the robust precoder implements a pseudo-inverse only for the remaining subchannels.