Peer-Reviewed Journal Details
Mandatory Fields
Pham T.;Farrell R.;Tran L.
2019
January
IEEE Transactions on Wireless Communications
Revisiting the MIMO Capacity with Per-Antenna Power Constraint: Fixed-Point Iteration and Alternating Optimization
Published
12 ()
Optional Fields
alternating optimization dirty paper coding fixed-point iteration MIMO minimax duality water-filling
18
1
388
401
© 2002-2012 IEEE. In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are 1) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems and/or 2) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared with current methods. We then extend the proposed solutions to multiuser MIMO systems with dirty paper coding-based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches.
1536-1276
10.1109/TWC.2018.2880436
Grant Details