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Abstract-The Internet of Things (IoT) promises ubiquitous connectivity of everything everywhere, which represents the biggest technology trend in the years to come. It is expected that by 2020 over 25 billion devices will be connected to cellular networks; far beyond the number of devices in current wireless networks. Machine-to-Machine (M2M) communications aims at providing the communication infrastructure for enabling IoT by facilitating the billions of multi-role devices to communicate with each other and with the underlying data transport infrastructure without, or with little, human intervention. Providing this infrastructure will require a dramatic shift from the current protocols mostly designed for human-to-human (H2H) applications. This article reviews recent 3GPP solutions for enabling massive cellular IoT and investigates the random access strategies for M2M communications, which shows that cellular networks must evolve to handle the new ways in which devices will connect and communicate with the system. A massive non-orthogonal multiple access (NOMA) technique is then presented as a promising solution to support a massive number of IoT devices in cellular networks, where we also identify its practical challenges and future research directions.

This paper reviews the state of the art channel coding techniques for ultra-reliable low latency communication (URLLC). The stringent requirements of URLLC services, such as ultra-high reliability and low latency, have made it the most challenging feature of the fifth generation (5G) of mobile networks. The problem is even more challenging for the services beyond the 5G promise, such as tele-surgery and factory automation, which require latencies less than 1ms and packet error rates as low as 10 −9 . This paper provides an overview on channel coding techniques for URLLC and compares them in terms of performance and complexity. Several important research directions are identified and discussed in more detail.

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Chronic stress is well recognized to decrease the number of GFAP⁺ astrocytes within the prefrontal cortex (PFC). Recent research, however, has suggested that our understanding of how stress alters astrocytes may be incomplete. Specifically, chronic stress has been shown to induce a unique form of microglial remodelling, but it is not yet clear whether astrocytes also undergo similar structural modifications. Such alterations may be significant given the role of astrocytes in modulating synaptic function. Accordingly, in the current study we have examined changes in astrocyte morphology following exposure to chronic stress in adult rats, using three-dimensional digital reconstructions of astrocytes. Our analysis indicated that chronic stress produced profound atrophy of astrocyte process length, branching and volume. We additionally examined changes in astrocyte-specific S100β, which are both a putative astrocyte marker and a protein whose expression is associated with astrocyte distress. While we found that S100β levels were increased by stress, this increase was not correlated with atrophy. We further established that while chronic stress was associated with a decrease in astrocyte numbers when GFAP labelling was used as a marker, we could find no evidence of a decrease in the total number of cells, based on Nissl staining, or in the number of S100β⁺ cells. This finding suggests that chronic stress may not actually reduce astrocyte numbers and may instead selectively decrease GFAP expression. The results of the current study are significant as they indicate stress-induced astrocyte-mediated disturbances may not be due to a loss of cells but rather due to significant remodeling of the astrocyte network.