While 5G is being tested worldwide and anticipated to be rolled out gradually in 2019, researchers around the world are beginning to turn their attention to what 6G might be in 10+ years time, and there are already initiatives in various countries focusing on the research of possible 6G technologies. This article aims to extend the vision of 5G to more ambitious scenarios in a more distant future and speculates on the visionary technologies that could provide the step changes needed for enabling 6G.

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This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicleto-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm reducing the computational overhead significantly.Index Terms-Machine learning, deep learning, deep neural network, V2X, V2V, power control, resource allocation.

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Abstract-Femtocell access points are inexpensive, plug and play home base stations designed to extend radio coverage and increase capacity within indoor environments. Their inherent uncoordinated and overlaid deployment however, means existing radio resource management (RRM) techniques are often ineffectual. Recent advances in dynamic RRM have emphasised the need for more efficient resource management strategies. While centralised resource management offers improved coordination and operator control giving better interference management, it is not scalable for increasing nodes. Distributed management techniques in contrast, do afford scaled deployment, but at higher node densities incur performance degradation in both system throughput and link-quality because of poor coordination. The level of spectrum sharing mandated by macro-femto deployment also impacts on system throughput and is scenario dependant. This paper presents a new hybrid resource management algorithm(HRMA) for down-links in orthogonal frequency division multiple access-based systems, with the model analysed for a range of macro-femto deployment scenarios. HRMA employs a dynamic fractional frequency reuse scheme for macro-cell deployment with frequency reuse defined for femto users depending on their location by making certain frequencies locally available based on macro-femto tier information sharing and efficient localised spectrum utilisation. Quantitative performance results confirm the efficacy of the HRMA strategy for various key system metrics including interference minimisation, outage probability and throughput.

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The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.