  We investigate MIMO eigenmode transmission using statistical channel state
information at the transmitter. We consider a general jointly-correlated MIMO
channel model, which does not require separable spatial correlations at the
transmitter and receiver. For this model, we first derive a closed-form tight
upper bound for the ergodic capacity, which reveals a simple and interesting
relationship in terms of the matrix permanent of the eigenmode channel coupling
matrix and embraces many existing results in the literature as special cases.
Based on this closed-form and tractable upper bound expression, we then employ
convex optimization techniques to develop low-complexity power allocation
solutions involving only the channel statistics. Necessary and sufficient
optimality conditions are derived, from which we develop an iterative
water-filling algorithm with guaranteed convergence. Simulations demonstrate
the tightness of the capacity upper bound and the near-optimal performance of
the proposed low-complexity transmitter optimization approach.
