As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP LTE and is envisioned to be an essential component of 5G mobile networks. The key feature of NOMA is to serve multiple users at the same time/frequency/ code, but with different power levels, which yields a significant spectral efficiency gain over conventional orthogonal MA. The article provides a systematic treatment of this newly emerging technology, from its combination with MIMO technologies to cooperative NOMA, as well as the interplay between NOMA and cognitive radio. This article also reviews the state of the art in the standardization activities concerning the implementation of NOMA in LTE and 5G networks.

We propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables oVML without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters (e.g., the retransmission limit, block size, block arrival rate, and the frame sizes) so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes.An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020 shiva2020blockchained. However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future reserach directions.

In order to improve the spectral efficiency, non-orthogonal multiple access (NOMA) with superposition coding is employed in a coordinated system where downlink signals to users near to base stations (BSs) and cell-edge users are transmitted simultaneously. To support a cell-edge user in a NOMA channel, two coordinated BSs use Alamouti code. In order to see the performance, we derive expressions for the transmission rates to near and cell-edge users. It is shown that the proposed coordinated superposition coding (CSC) scheme can provide a reasonable transmission rate to a cell-edge user without degrading the rates to users near to BSs.

The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

Multicast beamforming with superposition coding (SC) is studied for multiresolution broadcast where both data streams of high priority (HP) and low priority (LP) are to be transmitted for a user close to a base station (BS), while only data stream of LP is to be transmitted to a user not close to the BS (e.g., a cell-edge user). Using SC, a minimum total transmission power beamforming problem has been formulated to find beamforming vectors and powers for both users. For given normalized beamforming vectors, a closed-form expression for the optimal power allocation is derived from which an iterative algorithm is considered to find beamforming vectors. The proposed multicast beamforming with SC is applied to nonorthogonal multiple access (NOMA) systems to support multiple users as a two-stage beamforming method.

The Internet of Things (IoT) is a rapidly emerging field of technologies that delivers numerous cutting-edge solutions in various domains including the critical infrastructures. Thanks to the IoT, the conventional power system network can be transformed into an effective and smarter energy grid. In this article, we review the architecture and functionalities of IoT-enabled smart energy grid systems. Specifically, we focus on different IoT technologies including sensing, communication, computing technologies, and their standards in relation to smart energy grid. This article also presents a comprehensive overview of existing studies on IoT applications to the smart grid system. Based on recent surveys and literature, we observe that the security vulnerabilities related to IoT technologies have been attributed as one of the major concerns of IoT-enabled energy systems. Therefore, we review the existing threat and attack models for IoT-enabled energy systems and summarize mitigation techniques for those security vulnerabilities. Finally, we highlight how advanced technologies (e.g., blockchain, machine learning, and artificial intelligence) can complement IoT-enabled energy systems to be more resilient and secure and overcome the existing difficulties so that they become more effective, robust, and reliable in operation. Precisely, this article will help understand the framework for IoT-enabled smart energy system, associated security vulnerabilities, and prospects of advanced technologies to improve the effectiveness of smart energy systems.