Interference Utilization Precoding in Multi-Cluster IoT Networks

In Internet-of-Things, downlink multi-device interference has long been considered as a harmful element deteriorating system performance, and thus the principle of the classic interference-mitigation based precoding is to suppress the multi-device interference by exploiting the spatial orthogonality. In recent years, a judicious interference utilization precoding has been developed, which is capable of exploiting multi-device interference as a beneficial element for improving device’s reception performance, thus reducing downlink communication latency. In this review paper, we aim to review the emerging interference utilization precoding techniques. We first briefly introduce the concept of constructive interference, and then we present two generic downlink interference-utilization optimizations, which utilizes the multi-device interference for enhancing system performance. Afterwards, the application of interference utilization precoding is discussed in multi-cluster scenario. Finally, some open challenges and future research topics are envisaged.

Pairwise-Rotated EOFs of Global SST

Empirical orthogonal function (EOF) analysis of global sea surface temperature yields modes in which interannual variability associated with ENSO and the lower-frequency variability associated with the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are confounded with one another and with the signature of global warming. The confounded EOFs exhibit overlapping centers of action with polarities of the perturbations juxtaposed such that the respective modes are mutually orthogonal in the global domain. When physical modes with different time scales appear in the same pair of EOFs, the principal component (PC) time series tend to be positively correlated in one frequency band and negatively correlated in another. Mode mixing may be a reflection of sampling variability or it may reflect the lack of spatial orthogonality of the physical modes themselves. Using sequences of pairwise orthogonal rotations of selected PCs, it is possible, without recourse to filtering, to recover a global warming mode with a bland spatial pattern and a nearly linear upward trend, along with dynamical modes, each with its own characteristic time scale, that resemble ENSO, the PDO, and the AMO. Novel elements of this analysis include a rationale for choosing the optimal angle for pairwise rotation and a simple algorithm for eliminating mode mixing between the dynamical modes and the global warming mode by transferring the linear trends from the former to the latter.