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This second part of the two-part Special Issue (SI) on massive access for 5G and beyond starts with several papers on massive access techniques, then switches to coverage enhancement approaches, and finishes with a paper on the application of massive access in industrial Internet-of-Things (IoT).

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. In the past few years, it has received considerable attention in academia and industry. This Special Issue (SI) of the IEEE Journal on Selected Areas in Communications (JSAC) on Massive Access for 5G and Beyond

Under the pressure of the growing millennial population and senior citizens are aging, which is one of the top societal priorities in many countries, the provision of healthcare needs to evolve and improve. A roadmap paved by World Health Organization (WHO) in March 2019 called Global Strategy on Digital Health 2020-2024, specified a grand vision of promoting healthy lives and well-beings for everyone

Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home

In the era of the new generation of communication systems, data traffic is expected to continuously strain the capacity of future communication networks. Along with the remarkable growth in data traffic, new applications, such as wearable devices, autonomous systems, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different requirements. This growth

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires

Model-based method and data-based method are two basic approaches for the design of wireless communication systems. Model-based methods suffer from inaccurate modeling assumptions due to excessively complex environment. Recently, data-based methods have achieved remarkable performances in the communication system design without the knowledge of accurate model but encounter some challenges such as,

The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several

Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum likelihood rule, which is NP-hard due to the curse of dimensionality . For practical realizations, sub-optimal decoding algorithms are employed; yet limited theoretical insights prevent one from exploiting the full potential

We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be images of the same person or a vehicle taken from other cameras at different times and locations. Our goal is to maximize the accuracy of the retrieval task under power and bandwidth constraints over the wireless link. Due

Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target

To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional communication resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, a deep learning-based framework for multi-dimensional intelligent multiple access (MD-IMA) scheme is developed for beyond 5G and 6G wireless

In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller

The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL can potentially reduce the information on the local data sets that is leaked via communications. In order to provide formal privacy guarantees, however, it is generally necessary to put in place additional masking mechanisms

As an emerging machine learning technique, federated learning has received significant attention recently due to its promising performance in mitigating privacy risks and costs. While most of the existing work of federated learning focused on designing learning algorithm to improve training performance, the incentive issue for encouraging users’ participation is still under-explored. This paper presents

Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training

Training task in classical machine learning models, such as deep neural networks, is generally implemented at a remote cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since a large amount of data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel

Edge computing (EC) platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to users, are providing ultra-low latency and location-awareness to a rich portfolio of services. As monetary costs are incurred for renting computing resources on edge servers to enable service provisioning, ASP has to cautiously decide where to deploy the application and how

Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can enable machines to automatically boost the performance of the existing/future TCP designs in cellular networks? We answer this question by introducing DeepCC. DeepCC leverages advanced deep reinforcement learning (DRL) techniques to let machines automatically learn how to steer throughput-oriented TCP algorithms

We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1] – [3] . The 5G systems will provide advanced communication

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications

The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Grant-free (GF) access has been one of the enablers for the various use cases in 5th generation (5G) mobile system, especially for time-critical massive machine-type communication (mMTC). However, these use cases have diverse quality of service (QoS) requirements, which can be measured in terms of an access success rate from a GF random access perspective. Consequently, a GF scheme that enables supporting

Technological development expands the computation process of smart devices that adopt the telecare medical information system (TMIS) to fulfill the demands of the healthcare organization. It provides better medical identification to claim the features namely trustworthy, efficient, and resourceful. Moreover, the telecare services automate the remote healthcare monitoring process to ease professional

Emerging virtual reality/augmented reality games and self-driving cars necessitate accurate/responsive/private emotion recognition. Usually, traditional emotion recognition models are deployed at central servers, which results in the lack of abilities in generalization and covering the individual variation of clients. This paper proposes a responsive, localized, and private transfer learning based

Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction

IoT based eHealth system brings a revolution to healthcare industry, with which the old healthcare systems can be updated into smarter and more personalized ones. The practitioners can continue monitoring the physical status of the patients at anytime and anywhere, and develop more precise treatment plans by analyzing the collected data, such as heart rate, blood pressure, blood glucose. Actually,

In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency

In supporting the new 6G standard traffic services-massive ultra-reliable low-latency communications (mURLLC), several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning theory and finite blocklength coding (FBC), have been developed to upper-bound both delay and error-rate for time-sensitive multimedia applications. On the other hand, cell-free (CF) massive

Cross-modal applications that elaborately integrate audio, video, and haptic streams will become the mainstream of the eHealth systems. However, existing stream schedulers usually fail to simultaneously meet the cross-modal transmission requests in terms of low latency, high reliability, high throughput, and low complexity. To circumvent this dilemma, this article proposes a general cross-modal stream

Telemedicine, or the ability granted to doctors to remotely assist patients has been greatly benefited by advances in IoT, network communications, Machine Learning and Edge/Cloud computing. With the impeding arrival of 5G, virtualized infrastructures and cloud-native approaches enable the execution of unprecedented procedures during such patient/doctor interactions, allowing medical professionals to

The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which

The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the

Data are widely collected via the IoT for machine learning tasks in in-home health monitoring applications and mislabeled training data lead to unreliable machine learning models in in-home health monitoring. Researchers have proposed a wide arrangement of algorithms to deal with mislabeled training data, in which one straightforward and effective solution is to directly filter noise from training