IEEE 802.11ax-2019 will replace both IEEE 802.11n-2009 and IEEE 802.11ac-2013 as the next high-throughput Wireless Local Area Network (WLAN) amendment. In this paper, we review the expected future WLAN scenarios and use-cases that justify the push for a new PHY/MAC IEEE 802.11 amendment. After that, we overview a set of new technical features that may be included in the IEEE 802.11ax-2019 amendment and describe both their advantages and drawbacks. Finally, we discuss some of the network-level functionalities that are required to fully improve the user experience in next-generation WLANs and note their relation with other on-going IEEE 802.11 amendments.

Machine-to-Machine (M2M) communications are positioned to be one of the fastest growing technology segments in the next decade. Sensor and actuator networks connect communication machines and devices so that they automatically transmit information, serving the growing demand for environmental data acquisition. The IEEE 802.11ah Task Group (TGah) is working on a new standard to address the particular requirements of M2M networks: a large number of power-constrained stations, a long transmission range, small and infrequent data messages, low data rates and a non-critical delay. This paper explores the key features of this new standard, especially those related to the reduction of energy consumption in the medium access control layer. Given these requirements, a performance assessment of IEEE 802.11ah in four common M2M scenarios such as agriculture monitoring, smart metering, industrial automation and animal monitoring is presented. networks because of the growing but still reduced number of devices and light traffic requirements. Simultaneously, the 3rd Generation Partnership Project (3GPP) is working towards supporting M2M applications on 4G broadband mobile networks, such as UMTS and LTE, with the goal of natively embedding M2M communications in the upcoming 5G systems.

Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and intersymbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies over the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multiantenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.

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One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and got a lot of attention within the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place, now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions and congesting the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs' constraints, characteristics of prediction methods and monitored data. Finally, we conclude the paper with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs' growth.

Abstract-Next-generation WLANs will support the use of wider channels, which is known as channel bonding, to achieve higher throughput. However, because both the channel center frequency and the channel width are autonomously selected by each WLAN, the use of wider channels may also increase the competition with other WLANs operating in the same area for the available channel resources. In this paper, we analyse the interactions between a group of neighboring WLANs that use channel bonding and evaluate the impact of those interactions on the achievable throughput. A Continuous Time Markov Network (CTMN) model that is able to capture the coupled dynamics of a group of overlapping WLANs is introduced and validated. The results show that the use of channel bonding can provide significant performance gains even in scenarios with a high density of WLANs, though it may also cause unfair situations in which some WLANs receive most of the transmission opportunities while others starve.

Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one considers the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs. We show that substantial improvements on network performance can be achieved regarding throughput and fairness.