Towards Optimal Energy Harvesting Receiver Design in MIMO Systems

In this paper, we investigate a multiple-input multiple-output (MIMO) system with simultaneous information detection (ID) and energy harvesting (EH) receiver. This point-to-point system operates in the vicinity of active interfering nodes. The receiver performs power splitting where a portion of received signal undergoes analog energy harvesting circuitry. Further, the information content of the other portion is extracted after performing digital beamforming. In this MIMO system, information carrier eigen-modes are not necessarily the eigen-modes with the strongest energy level. Hence, it is beneficial to perform independent beamforming at the receiver of MIMO-P2P channel. Here, we utilize a hybrid analog/digital beamforming for the purpose of simultaneous ID and EH in such scenarios. This design, provides extra design degrees-of-freedom in eigen-mode selection for ID and EH purposes independently. Worst-case performance of this receiver structure is discussed. Finally, its benefits is compared to the classical receiver structure and the gains are highlighted

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