8 research outputs found
Robust Transceiver Design for IRS-Assisted Cascaded MIMO Systems
{Robust transceiver design against unresolvable system uncertainties is of
crucial importance for reliable communication. We consider a MIMO multi-hop
system, where the source, the relay, and the destination are equipped with
multiple antennas. Further, an intelligent reconfigurable surface (IRS) is
established to cancel the RSI as much as possible. The considered
decode-and-forward (DF) hybrid relay can operate in either half-duplex or
full-duplex mode, and the mode changes adaptively depending on the RSI
strength. We investigate a robust transceiver design problem, which maximizes
the throughput rate corresponding to the worst-case RSI under a
self-interference channel uncertainty bound constraint. To the best of our
knowledge, this is the first work that uses the IRS for RSI cancellation in
MIMO full-duplex DF relay systems. The yielded problem turns out to be a
non-convex optimization problem, where the non-convex objective is optimized
over the cone of semidefinite matrices. We propose a closed-from lower bound
for the IRS worst case RSI cancellation. Eventually, we show an important
result that, for the worst case scenario, IRS can be helpful only if the number
of IRS elements are at least as large as the size of the interference channel.
Moreover, a novel method based on majorization theory is proposed to find the
best response of the transmitters and relay against worst case RSI.
Furthermore, we propose a multi-level water-filling algorithm to obtain a
locally optimal solution iteratively. Finally, we obtain insights on the
optimal antenna allocation at the relay input-frontend and output-frontend, for
relay reception and transmission, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:1912.1283
Robust optimization for amplify-and-forward mimo relaying from a worst-case perspective
In this paper, we consider robust optimization of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay precoders in presence of deterministic imperfect channel state information (CSI), when the CSI uncertainty lies in a norm bounded region. Two widely used performance metrics, mutual information (MI) and mean square error (MSE), are adopted as design objectives. According to the philosophy of worst-case robustness, the robust optimization problems with respect to maximizing the worst-case MI and minimizing the worst-case MSE are formulated as maximin and minimax problems, respectively. Due to the fact that these two problems do not have a concave-convex or convex-concave structure, we cannot rely on the conventional saddle point theory to find the robust solutions. Nevertheless, by exploiting majorization theory, we show that the formulated maximin and minimax problems both admit saddle points. We further analytically characterize the saddle points, and provide closed-form solutions to robust relay precoder designs. Interestingly, we find that, under both MI and MSE metrics, the robust relay optimization leads to a channel-diagonalizing structure, meaning that eigenmode transmission is optimal from the worst-case robustness perspective. The proposed robust designs can improve the spectral efficiency and reliability of AF MIMO relaying against CSI uncertainties at the similar cost of computational complexity as the existing non-robust schemes. © 1991-2012 IEEE