HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO

In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to optimize the beamforming matrices. In frequency division duplex (FDD) systems, full channel reciprocity does not hold, and CSI acquisition generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed the end-to-end design of pilot transmission, feedback, and CSI estimation via deep learning. In this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots. The proposed method is based on a novel deep learning architecture -- HyperRNN -- that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance, and that it reduces requirements in terms of pilot lengths.Comment: To be presented at SPAWC 202

On Function-on-Scalar Quantile Regression

Existing work on functional response regression has focused predominantly on mean regression. However, in many applications, predictors may not strongly influence the conditional mean of functional responses, but other characteristics of their conditional distribution. In this paper, we study function-on-scalar quantile regression, or functional quantile regression (FQR), which can provide a comprehensive understanding of how scalar predictors influence the conditional distribution of functional responses. We introduce a scalable, distributed strategy to perform FQR that can account for intrafunctional dependence structures in the functional responses. This general distributed strategy first performs separate quantile regression to compute $M$-estimators at each sampling location, and then carries out estimation and inference for the entire coefficient functions by properly exploiting the uncertainty quantifications and dependence structures of $M$-estimators. We derive a uniform Bahadur representation and a strong Gaussian approximation result for the $M$-estimators on the discrete sampling grid, which are of independent interest and provide theoretical justification for this distributed strategy. Some large sample properties of the proposed coefficient function estimators are described. Interestingly, our rate calculations show a phase transition phenomenon that has been previously observed in functional mean regression. We conduct simulations to assess the finite sample performance of the proposed methods, and present an application to a mass spectrometry proteomics dataset, in which the use of FQR to delineate the relationship between functional responses and predictors is strongly warranted

Communication, sensing, computing and energy harvesting in smart cities

A smart city provides diverse services based on real-time data obtained from different devices deployed in urban areas. These devices are largely battery-powered and widely placed. Therefore, providing continuous energy to these devices and ensuring their efficient sensing and communications are critical for the wide deployment of smart cities. To achieve frequent and effective data exchange, advanced enabling information and communication technology (ICT) infrastructure is in urgent demand. An ideal network in future smart cities should be capable of sensing the physical environment and intelligently mapping the digital world. Therefore, in this paper, we propose design guidelines on how to integrate communications with sensing, computing and/or energy harvesting in the context of smart cities, aiming to offer research insights on developing integrated communications, sensing, computing and energy harvesting (ICSCE) for promoting the development ICT infrastructure in smart cities. To put these four pillars of smart cities together and to take advantage of ever-increasing artificial intelligence (AI) technologies, the authors propose a promising AI-enabled ICSCE architecture by leveraging the digital twin network. The proposed architecture models the physical deep neural network-aided ICSCE system in a virtual space, where offline training is performed by using the collected real-time data from the environment and physical devices

Physical Layer Security of Spatially Modulated Sparse-Code Multiple Access in Aeronautical Ad-Hoc Networking

For improving the throughput while simultaneously enhancing the security in aeronautical ad-hoc networking (AANET), a channel quality indicator (CQI)-mapped spatially modulated sparse code multiple access (SM-SCMA) scheme is proposed in this paper. On one hand, we exploit the joint benefits of spatial modulation and SCMA for boosting the data rate. On the other hand, a physical-layer secret key is generated by varying the SM-SCMA mapping patterns based on the instantaneous CQI in the desired link. This guarantees the security of AANETs, since this secret key is not exchanged between the source aeroplane and its destination. Due to the line-of-sight (LoS) propagation in the AANET, other aeroplanes or eavesdroppers may detect the signals delivered in the desired link. However, they are unable to translate the detected signals into the original confidential information, even if multiple copies of the signals are recoined over multiple hops of the AANET, because they have no knowledge of the CQI-based SM-SCMA mapping pattern. The performance of the CQI-mapped SM-SCMA is evaluated in terms of both its bit error rate and its ergodic secrecy rate, which substantiates that the proposed scheme secures the confidential information exchange in the multi-hop AANET

Fault Troubleshooting Using Bayesian Network and Multicriteria Decision Analysis

Fault troubleshooting aims to diagnose and repair faults at the highest efficacy and a minimum cost. The efficacy depends on multiple criteria like fault probability, cost, time, and risk of a repair action. This paper proposes a novel fault troubleshooting approach by combining Bayesian network with multicriteria decision analysis (MCDA). Automobile engine start-up failure is used as a case study. Bayesian network is employed to establish fault diagnostic model for reasoning and calculating standard values of uncertain criteria like fault probability. MCDA is adopted to integrate the influence of the four criteria and calculate utility value of the actions in each troubleshooting step. The approach enables a cost-saving, high efficient, and low risky troubleshooting

Artificial-Noise-Aided CQI-Mapped Generalized Spatial Modulation

The artificial noise (AN) aided channel quality indicator (CQI) mapped generalized spatial modulation (GSM) philosophy is proposed for improving the security of legitimate links. Given the randomness of the legitimate CQI, eavesdroppers cannot successfully decode the confidential information. We analyse both the secrecy rate as well as the eavesdropper's error rate and reveal that the proposed scheme outperforms its CQI-mapped modulation counterpart. Theoretically, the secrecy rate approaches the achievable data rate of the legitimate link, which is equal to the GSM capacity

Three-dimensional, isotropic imaging of mouse brain using multi-view deconvolution light sheet microscopy

We present a three-dimensional (3D) isotropic imaging of mouse brain using light-sheet fluorescent microscopy (LSFM) in conjunction with a multi-view imaging computation. Unlike common single view LSFM is used for mouse brain imaging, the brain tissue is 3D imaged under eight views in our study, by a home-built selective plane illumination microscopy (SPIM). An output image containing complete structural information as well as significantly improved resolution (∼4 times) are then computed based on these eight views of data, using a bead-guided multi-view registration and deconvolution. With superior imaging quality, the astrocyte and pyramidal neurons together with their subcellular nerve fibers can be clearly visualized and segmented. With further including other computational methods, this study can be potentially scaled up to map the connectome of whole mouse brain with a simple light-sheet microscope

repytah: An Open-Source Python Package for Building Aligned Hierarchies for Sequential Data

We introduce repytah, a Python package that constructs the aligned hierarchies representation that contains all possible structure-based hierarchical decompositions for a finite length piece of sequential data aligned on a common time axis. In particular, this representation–introduced by Kinnaird (2016) with music-based data (like musical recordings or scores) as the primary motivation–is intended for sequential data where repetitions have particular meaning (such as a verse, chorus, motif, or theme). Although the original motivation for the aligned hierarchies representation was finding structure for music-based data streams, there is nothing inherent in the construction of these representations that limits repytah to only being used on sequential data that is music-based. The repytah package builds these aligned hierarchies by first extracting repeated structures (of all meaningful lengths) from the self-dissimilarity matrix (SDM) for a piece of sequential data. Intentionally repytah uses the SDM as the starting point for constructing the aligned hierarchies, as an SDM cannot be reversed-engineered back to the original signal and allows for researchers to collaborate with signals that are protected either by copyright or under privacy considerations. This package is a Python translation of the original MATLAB code by Kinnaird (2014) with additional documentation, and the code has been updated to leverage efficiencies in Python