Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems

Unmanned aerial vehicle (UAV) is steadily growing as a promising technology for next-generation communication systems due to their appealing features such as wide coverage with high altitude, on-demand low-cost deployment, and fast responses. UAV communications are fundamentally different from the conventional terrestrial and satellite communications owing to the high mobility and the unique channel characteristics of air-ground links. However, obtaining effective channel state information (CSI) is challenging because of the dynamic propagation environment and variable transmission delay. In this paper, a deep learning (DL)-based CSI prediction framework is proposed to address channel aging problem by extracting the most discriminative features from the UAV wireless signals. Specifically, we develop a procedure of multiple Gaussian Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural networks (DNNs). To evaluate the proposed approach, real data measurements from an UAV communicating with base-stations within a commercial cellular network are obtained and used for training and validation. Numerical results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs

UAV-Assisted 5G Network Architecture with Slicing and Virtualization

Next generation networks promise not only extremely high data rates and low latency, but also ubiquitous coverage and massive IoT. One of the major challenges is the guaranteed service provision even in cases of network failure (e.g. infrastructure damage, remote areas, flash crowd areas etc.). Flying nodes that will act as aerial base stations or relays could back-up the network fast and prevent any service interruption or even enhance network performance. This paper discusses the architecture and possible applications of flying modes in the frame of a 5G network supporting network slicing and lightweight virtualization. It also provides aerial LTE measurement results to support the feasibility check for using UAVs in two possible scenarios, i.e. network capacity enhancement and increasing network coverage

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

The definition of multiple slicing types in 5G has created a wide field for service innovation in communications. However, the advantages that network slicing has to offer remain to be fully exploited by today’s applications and users. An important area that can potentially benefit from 5G slicing is emergency communications for First Responders. The latter consists of heterogeneous teams, imposing different requirements on the connectivity network. In this paper, the RESPOND-A platform is presented, which provides First Responders with network-enabled tools on top of 5G on-scene planning, with enhanced service slicing capabilities tailored to emergency communications. Furthermore, a mapping of emergency services and communications to specific slice types is proposed to identify the current challenges in the field. Additionally, the proposed tentative mechanism is evaluated in terms of energy efficiency. Finally, the approach is summarized by discussing future steps in the convergence of 5G network slicing in various areas of emergency vertical applications