The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum-and QML-assisted B5G networks are identified and discussed.

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A series of 2,4-diaryl-2,3,4,5-tetrahydro- (36-40) and 2,4-diaryl-2,3-dihydro-1,5-benzothiazepines (25-35) have been synthesized from the corresponding chalcones 1-24. Both the benzothiazepines and chalcones were evaluated as DPPH free-radical scavengers and as inhibitors of cholinesterases, urease, and alpha-glucosidase. Compounds 2, 5, 6, 7, 10, 13, 18, 21, 36a, 37a, 37b, and 39a showed significant cholinesterase inhibiting activities. Among the 15 dihydro-1,5-benzothiazepines, 26, 32, and 35 exhibited significant radical-scavenging activities; and six tetrahydro-1,5-benzothiazepines (35, 36a, 36b, 37a, 37b, and 39a) were found to be inhibitors of AChE and BChE. Compounds 22, 25, 26, 33, 35, 36a, 37b, and 39a inhibited urease, and 25 and 27-31 were found to be potent inhibitors of alpha-glucosidase.

The ability to detect and distinguish interactions in the workplace can shed light over productivity, team work and on employees' use of space. Questionnaires and direct observations have often been used as mechanisms to identify office based interactions, however, these are either very time consuming, yield coarse grained information or do not scale to large numbers of people. Technology has been recently employed to cut costs and improve output, however precise interaction dynamics gathering often requires individuals to wear custom hardware. In this paper, we present an extensive evaluation of Bluetooth Low Energy (BLE) as a technology to monitor people proximity in the workplace. We examine the key parameters that affect the accuracy of the detected contacts and their impact on power consumption. We study how this system can be implemented on popular wearable devices (i.e., Android Wear and Tizen) and the resulting limitations. Through a real world deployment in a commercial organisation with 25 participants we evaluate the performances of a BLE-based proximity detection technique. Our results show the suitability of BLE for workplace interaction detection and give guidance to vendors and Operating System (OS) developers on the impact of the restrictions regarding the use of BLE on commodity wearables.

Vehicular communications essentially support automotive applications for safety and infotainment. For this reason, industry leaders envision an enhanced role of vehicular communications in the fifth generation of mobile communications technology. Over the years, the number of vehicle-mounted sensors has increased steadily, which potentially leads to more volume of critical data communications in a short time. Also, emerging applications such as remote/autonomous driving and infotainment such as high-definition movie streaming require data-rates on the order of multiple Gbit/s. Such high data-rates require a large system bandwidth, but very limited bandwidth is available in the sub-6 GHz cellular bands. This has sparked research interest in the millimeter wave (mmWave) band (10 GHz-300 GHz), where a large bandwidth is available to support the high data-rate and low-latency communications envisioned for emerging vehicular applications. However, leveraging mmWave communications requires a thorough understanding of the relevant vehicular propagation channels, which are significantly different from those investigated below 6 GHz. Despite their significance, very few investigations of mmWave vehicular channels are reported in the literature. This work highlights the key attributes of mmWave vehicular communication channels and surveys the recent literature on channel characterization efforts in order to provide a gap analysis and propose possible directions for future research.

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In this paper, we develop a three-dimensional geometrical channel model for mobile-to-mobile (M2M) communication environment. The proposed model combines most of the existing two-dimensional (2D) and three-dimensional (3D) geometrical channel models for M2M and fixed-to-mobile (F2M) communication channels into one generalized model. Using the proposed model, closed-form expressions for the joint probability density function (PDF) of angle-of-arrival (AoA) and time-ofarrival (ToA) in azimuth and elevation planes are derived. These results are then verified by simulations and available field measurements. Furthermore, comprehensive comparisons of the proposed channel model are carried out with the existing 2D and 3D channel models in the literature, which show that many notable geometrical channel models in M2M and F2M communication environments become the special cases of our proposed generalized 3D geometrical channel model by adjusting corresponding channel parameters. Consequently, the proposed 3D channel model can be taken as a useful tool for modeling mobile ad hoc networks (MANET), Vehicular Ad hoc Networks (VANET) and Intelligent Transportation Systems (ITS).