Boundary sensitive Lindbladians and relaxation dynamics

It is well known that non-Hermitian systems can be extremely sensitive to boundary conditions owing to non-Hermitian skin effect (NHSE). Analogously, we investigate two boundary-sensitive $U(1)$ symmetric Lindbladians: one carries current in the steady state, and the other does not. The numerical results indicate significant change of the Liouvillian spectrum, eigenmodes and relaxation time for both Lindbladians when the boundary conditions are altered. This phenomenon is found to be triggered by the Liouvillian skin effect (LSE), specifically the localization of eigenmodes, which stems from the NHSE of the non-Hermitian effective Hamiltonian. In addition, these two Lindbladians manifest different LSE, ultimately resulting in distinct relaxation behaviors.Comment: 19pages, 27 figure

A New Heterogeneous Hybrid MIMO Receive Structure of Rapidly Eliminating DOA Ambiguity

Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays eventuate a huge amount of circuit costs and complexity to direction of arrival(DOA) estimation, which is hard to satisfy the needs of high precision and low cost in future green wireless communication. To address this challenge, a novel heterogeneous hybrid MIMO receiver is proposed in this paper and a high performance DOA estimator called heterogeneous cross-minimum distance (HCMD) is developed based on the structure. The antenna arrays are first divided into multiple groups, and each group adopts a different hybrid structure. The virtual antenna arrays of these groups are then used for DOA estimation to generate multiple candidate angle sets, where each set contains a unique true solution and multiple pseudo-solutions. Finally, the cross-distance minimization method is applied to the multiple candidate angle sets to select the corresponding true solution for each group, and the final DOA estimation is given by combining the multiple true solutions. Simulation results show that as the number of antennas tends to large-scale, the proposed method can rapidly find the true solution for each group and achieve excellent estimation performance