Magnetization properties of magnetic nanowire arrays are studied on various ferromagnetic materials grown in anodic alumina ͑alumite͒ and track etched polycarbonate ͑PCTE͒ membranes by pulsed electrodeposition. Magnetization curves were measured as functions of wire material, field orientation, and wire length. The coercivity (H c ) and remanent squareness ͑S͒ of the various wire arrays were derived from hysteresis loops as a function of angle ͑͒ between the field and wire axis. For PCTE membranes, H c () curves for CoNiFe, NiFe, and Co nanowire arrays all show an otherwise-bell-type variation, while they change shapes from the otherwise bell to bell type for Ni nanowire arrays as the wire diameter decreases to 30 nm. These characteristics can be understood based on different magnetization reversal mechanisms of small wires. The effect of magnetostatic interaction among wires on the magnetic properties was examined by changing the wire lengths in alumite membranes. It is found that the interaction reduces H c and S values significantly and may cause the overall easy axis change from parallel to perpendicular to the wire axis. However, the interaction is much weaker than expected from an independent precession theory. The strong coupling among the wire may also induce a change of magnetization reversal mechanism.

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Channel estimation for the downlink of frequency division duplex (FDD) massive MIMO systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users.The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived.The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.