Grant-free random access is an effective solution to enable massive access for future Internet of Vehicles (IoV) scenarios based on massive machine-type communication (mMTC). Considering the uplink transmission of grant-free based vehicular networks, vehicular devices sporadically access the base station, the joint active device detection (ADD) and channel estimation (CE) problem can be addressed by compressive sensing (CS) recovery algorithms due to the sparsity of transmitted signals. However, traditional CS-based algorithms present high complexity and low recovery accuracy. In this manuscript, we propose a novel alternating direction method of multipliers (ADMM) algorithm with low complexity to solve this problem by minimizing the$\ell _{2,1}$norm. Furthermore, we design a deep unfolded network with learnable parameters based on the proposed ADMM, which can simultaneously improve convergence rate and recovery accuracy. The experimental results demonstrate that the proposed unfolded network performs better performance than other traditional algorithms in terms of ADD and CE.

Capturing the directions of light by light field cameras powers next-generation immersive multimedia applications. A critical problem in taking advantage of the rich visual information in light field images is depth estimation. Conventional light field depth estimation methods build a cost volume that measures the photo-consistency of pixels refocused to a range of depths, and the highest consistency indicates the correct depth. This strategy works well in most regions but usually generates blurry edges in the estimated depth map due to occlusions. Recent work shows that integrating occlusion models to light field depth estimation can largely reduce blurry edges. However, existing occlusion handling methods rely on complex edge-aided processing and post-refinement, and this reliance limits the resultant depth accuracy and impacts on the computational performance. In this paper, we propose a novel occlusion-aware vote cost (OAVC) which is able to accurately preserve edges in the depth map. Instead of using photo-consistency as an indicator of the correct depth, we construct a novel cost from a new perspective that counts the number of refocused pixels whose deviations from the central-view pixel are less than a small threshold, and utilizes that number to select the correct depth. The pixels from occluders are thus excluded in determining the correct depth. Without the use of any explicit occlusion handling methods, the proposed method can inherently preserve edges and produces high-quality depth estimates. Experimental results show that the proposed OAVC outperforms state-of-the-art light field depth estimation methods in terms of depth estimation accuracy and computational complexity.

Soil moisture is a crucial parameter of hydrological processes as it affects the exchange of water and heat at the land/atmosphere interface. Regional hydrological applications (floods and modeling of small basins) and agricultural applications (irrigation and agricultural land mapping) require daily soil moisture (SM) values having a spatial resolution of at least 1-km. This requirement is currently unmet by existing satellite missions. Notably, SM has variability over three dimensions. As such, accurate prediction of satellite SM requires multiple bidirectional spectra-spatiotemporal analyses. However, current state-of-the-art SM downscaling models can not yet fulfill this requirement. This paper proposes a new bi-directional LSTM model dubbed the three-dimensional bi-directional LSTM (3D-Bi-LSTM), which downscales the Soil Moisture Active Passive (SMAP) global daily 9-km SM to daily 1-km SM. In the proposed downscaling model, the region-specific soil moisture indices (SMIs) are first extracted using a covariance-adaptive convolutional neural network (CNN) to support the extraction of important distinctive information from multispectral data. Next, the CNN output is provided to the 3D-Bi-LSTM to perform the bi-directional analysis of spatial correlation within a feature and spectral correlation between features over multiple time instants. Experimental results demonstrate the proposed model outperforms state-of-the-art networks. An ablation study, transferability assessment, and feature importance study further demonstrate the proposed 3D-Bi-LSTM’s efficiency.

Rainfall event forecasting is prominently done using climate models (CMs) to produce multiple forecasts for the same rainfall event. The best forecast is complicated to find and hence has not yet been explored in the CMs. Recent advances in deep learning methods have provided an exceptional ability to investigate intricate weather patterns from big climate data. In this article, a hybrid climate learning model (HCLM) is proposed that utilizes both the CM and the deep learning models for improving the rainfall forecast. More specifically, a probabilistic multilayer perceptron (PMLP) network evaluates multiple forecasts from the CM-generated forecasts and selects the best one. The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast. The experimental results from various climate zones in Australia show that the HCLM outperforms existing state-of-the-art climate and deep learning models.

In contrast to traditional centralized estimation methods, this letter proposes a distributed dynamic state estimation method for microgrids incorporating distributed energy resources. Specifically, distributed filter structure is designed in an interconnected way, where packet losses obviously occur between them. Then the error function is written in a compact form using the matrix property of the Kronecker product. Afterwards, it can be transformed into a linear matrix inequality. Finally, the local and neighboring gains for the distributed estimator are effectively computed after solving the convex optimization problem. Simulation result shows that the proposed method can well estimate the system state within 10 iterations.

This paper explores the optimal filtering problem for microgrid state estimation considering packet losses in the internet of things (IoT) networks. The considered IoT networks collect microgrid information from smart sensors and send control messages to actuators where sensors and actuators are linked over a lossy network. Explicitly, the distribution power system incorporating renewable distributed energy resources such as wind turbine is represented as a state-space model where IoT element such as smart sensors are deployed to obtain measurements. This sensing information is transmitted to the fusion center through a lossy IoT communication network where measurements are lost. This paper proposes an optimal estimator based on the mean squared error between the actual states and its estimate. Afterwards, a state feedback controller is designed based on the semidefinite programming approach under the condition of packet dropouts in the IoT network. The efficacy of the proposed approaches are demonstrated by presenting an environment-friendly microgrid model incorporating distributed energy resources.

Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible prediction structures is unlimited, and it is also observed that the coding bit-rate can be reduced by increasing the number of bi-directionally encoded views in the prediction structure. However, none work has been conducted to analyze the relationship of the prediction structure with its coding performance. In light of this observation, we first design a new LF-MVC prediction structure by extending the inter-view prediction into a two-directional parallel structure. Analytical models for source coding rate and encoding time are developed to analyze their relationships with the prediction structure, and are proven to be well-matched to our experimental results. Experimental evaluation of two LF video sequences demonstrates that the proposed LF-MVC prediction structure can achieve a factor of 26% bit-rate reduction against the conventional MVC prediction structure for an LF video with 5×5 views, and a further 34% bit-rate reduction for an LF video with a larger 10×10 views. Compared with the state-of-the-art MVC-based LF video coding prediction structures in the literature, LF-MVC can achieve the best coding performance, and with its high encoding efficiency, is well suited for deployment in practical LF-based 3D systems.

Due to the overwhelming complexity of multihop transmission and intermessage interference, only a limited amount of research has been carried out in the implementation of wireless network coding (WNC) for generalized multihop two-way relay channels (MH-TRCs), let alone the generalization of multihop WNC (MH-WNC) schemes. Our recent paper has showed that the MH-WNC scheme with fixed two transmission time intervals (TTIs) was unable to always outperform conventional non-NC schemes in outage performance for the MH-TRC with an arbitrary number of nodes. In view of this fact, a generalized MH-WNC scheme with multiple TTIs is designed for the L-node K-message MH-TRC in this paper. Closed-form expressions for the upper bound of the outage probability for two prominent relaying network coding strategies (i.e., analog network coding and compute-and-forward network coding) are derived. Moreover, by investigating the relationships between the outage probability and the numbers of nodes, messages, and TTIs, we obtain an optimal MH-WNC scheme that can achieve the best outage probability and always outperform non-NC in the MH-TRC with an arbitrary number of nodes.

Wireless network coding for multi-hop two-way relay channels (MH-TRC) has been proven to achieve a significantly improved network throughput than non- network coding (Non-NC) schemes. Our previous work showed that the multi-hop compute-and-forward (MH- CPF) scheme for the MH-TRC with fixed two transmission time intervals was unable to outperform Non-NC in the MH-TRC with an arbitrary number of nodes. In view of this fact, we propose anI-time- interval (I-TI) MH-CPF scheme (Irepresents the number of transmission time intervals) for the MH- TRC with arbitrary numbers of nodes and messages. By converting the transmission pattern to a corresponding characteristic matrix, the outage probability of theI-TI MH-CPF scheme is derived. We investigate the relationships between the numbers of nodes, messages and TIs with the outage probability. It is proven thatL-1-TI MH-CPF can outperform 2-TI MH-CPF when the number of messages is large, while the MH-CPF scheme with a larger number of TIs has better outage performance than 2-TI MH-CPF in the MH-TRC with a small number of nodes.

We investigate the performance of analog network coding (ANC) for multi-hop networks in this paper. With the amplify-and-forward (AF) protocol, relays broadcast the sum of two colliding signals to neighboring nodes, while the source node can subtract its own signal from the colliding signal to obtain the received information. We first give the transmission scheme expressions for the n-node m-frame two-way multi-hop network. For this scheme, we derive the end-to-end signal-to-noise ratio (SNR) expression. Without loss of generality, the closed-form expression of the outage probability for the generalized multi-hop network is evaluated. Numerical results demonstrate that the outage performance with ANC is better than the traditional scheme without ANC.