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Welcome to the September 2021 issue of IEEE Geoscience and Remote Sensing Magazine ( GRSM )! We have a number of feature articles in this issue, covering topics ranging from a tutorial on synthetic aperture radar (SAR) beamforming, to a review of the Earth-observation constellation, to several articles on techniques for processing various types of remote sensing data. These will be followed by a report

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About this time, I expected to be returning from the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) that was to be held in Brussels, Belgium, with many stories about the trip and thinking about discussions I had with colleagues. However, once again, we found ourselves attending the conference online and, in many cases, recording presentations that originally were intended to be

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This article presents the multimodal BigEarthNet (BigEarthNet-MM) benchmark archive consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support deep learning (DL) studies in multimodal, multilabel remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is annotated with multilabels provided by the CORINE Land Cover (CLC) map of 2018 based

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I Welcome you to the third issue of the IEEE Communications Surveys and Tutorials in 2021. This issue includes 18 articles covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications,” “Internet Technologies,” “IoT and M2M,” “Network and Service Management and Green Communications,” “Network Security,” “Network Virtualization

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate

The LoRa radio technology is one of the most prominent choices in the Internet of Things Low-Power Wide Area Networks (LPWANs) industry due to its versatile and robust technical characteristics along with its ability to achieve long communication ranges combined with low energy consumption and reduced cost. One of the main issues in LoRa networks is how many end-devices can be reporting efficiently

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine

Distributed computing has become a common approach for large-scale computation tasks due to benefits such as high reliability, scalability, computation speed, and cost-effectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final

Towards future intelligent vehicular network, the machine learning as the promising artificial intelligence tool is widely researched to intelligentize communication and networking functions. In this paper, we provide a comprehensive survey on various machine learning techniques applied to both communication and network parts in vehicular network. To benefit reading, we first give a preliminary on

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In this letter, we propose a deep learning-based spreading sequence design and active user detection (AUD) to support massive machine-type communications (mMTC) where a large number of devices access the base station using non-orthogonal spreading sequences. To design the whole communications system minimizing AUD error, we employ an end-to-end deep neural network (DNN) where the spreading network

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This letter analyzes the impact of packet retransmissions on the performance of wireless random access protocols. In particular, we focus on the effects of two controllable parameters, namely the rate at which retransmissions occur and the number of allowed retransmissions. In a scenario where packets are generated according to a Poisson process and new packets substitute old ones upon arrival, we

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works,

In this letter, we address the problem of channel classification. Specifically, we consider five channel models also accounting for the incoming fifth generation mobile communication network (5G). The classification problem is formulated in terms of a multiple hypothesis testing problem and solved through a heuristic decision logic relying on sequential binary comparisons. This choice is dictated by

The spatial covariance matrix, as the core of subspace direction of arrival (DOA) estimation, is unavailable in the hybrid analog-digital structure, which is widely used in large-scale transceivers to reduce the number of radio frequency chains. In previous studies, the beam sweeping algorithm (BSA) has been used for effectively reconstructing the spatial covariance matrix. However, the BSA is not

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest

Intelligent reflecting surface (IRS) is a revolutionary technology for achieving spectral- and energy-efficient wireless networks. By adjusting the phase shifts at the IRS, a high passive beamforming gain can be achieved, which is particularly appealing for improving the spectral efficiency of wireless powered communication networks (WPCNs). Inspired by this, we study IRS-aided WPCNs, where the IRS

Due to its high mobility and low cost, unmanned aerial vehicle mounted base station (UAV-BS) can be deployed in a fast and cost-efficient manner for providing wireless services in areas where traditional terrestrial infrastructures cannot be laid for technical and economic reasons. In this letter, we investigate the problem of joint three-dimensional (3D) deployment and power allocation for maximizing

In this letter, we investigate the reconfigurable intelligent surface (RIS) assisted multi-cell downlink multi-input single-output system under the assumption of only statistical channel state information at the base stations and RIS. Firstly, a closed-form ergodic weighted sum rate approximation is obtained. Then, based on this approximation, we propose an approximate optimal joint beamforming vectors

The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication. With the growing complexity of CSI, CSI feedback in massive MIMO system has become a bottleneck problem. Recently, numerous deep learning-based CSI feedback approaches demonstrate their efficiency and potential. However, most existing methods improve accuracy at the cost of computational

This paper proposes a new finite precision iterative decoder for low-density parity-check (LDPC) codes. The proposed decoder, named Sign-Preserving Min-Sum (SP-MS), significantly improves the decoding performance compared to the classical Offset Min-Sum (OMS) decoder when messages are quantized on $q=2$ , 3, or 4 bits. The particularity of the SP-MS decoder is that messages cannot take the 0 value

This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control

In this paper, we investigate the performance of a full-duplex (FD) relay system where multi-antennas are exploited at source and destination. Unlike previous works, the impacts of imperfect channel state information (I-CSI), transceiver hardware noise (THN), and residual self-interference (RSI) are taken into account. We mathematically derive the exact closed-form expressions of the outage probability

Energy detection is of crucial importance in cognitive radio networks. However, its performance is poor when the channel fading is severe, which causes interference to the primary users. In order to tackle this issue, an intelligent reflecting surface (IRS)-enhanced energy detection for spectrum sensing is proposed. Both the cases with and without the direct link between the primary user and the secondary

In this letter, we formulate computationally efficient precoder and detector design problems for symbol-level generalized precoding-aided spatial modulation (GPQSM) systems with phase-shift-keying signaling. We first present a novel low-complexity decoupled detector. Subsequently, we formulate two new precoders using constructive interference (CI) techniques to enhance the performance of the proposed

In this letter, we investigate the UAV swarm communication in the presence of jammers, where cluster-based network architecture is adopted to enable efficient backhaul links. We aim to maximize the sum rate of the cluster head (CH) UAV by optimizing UAVs’ trajectories and clustering jointly subject to UAV’s various flying abilities. However, the formulated problem is non-convex and thus intractable

Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose a federated LIDAR aided beam selection method for V2I