User-centric Networks Selection with Adaptive Data Compression for Smart Health

The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users’ objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques

DeepWiN: Deep Graph Reinforcement Learning for User-Centric Radio Access Networks Automation

The future cellular networks are expected to support an increasing number of users with heterogeneous applications, requiring varying network resources. Therefore, the 6G and beyond cellular networks need to be elastic, and user-centric. User-centric Radio Access Networks (UCRAN), with virtual cells (S-zones), can provide on-demand connectivity, coverage and quality of service to different user applications while optimizing the network for energy efficiency, area spectral efficiency, reliability and user service rate. However, with high variability in the network, due to user mobility and fading, the selection of S-zone sizes which optimize the network performance for multiple types of users simultaneously becomes a challenge. Therefore, to automate the selection of S-zone sizes dynamically, we propose deep graph reinforcement learning (DGRL), a Soft actor-critic model integrated with Graph neural network. DGRL infers from DeepWiN, a graphical representation of UCRAN that encodes the non-euclidean topology of the network along with its euclidean features, effectively encapsulating the wireless domain knowledge of the network configuration. Our experiments show that the deep graph reinforcement learning can learn to optimize S-zone sizes with 15% fewer training episodes in comparison to the legacy neural-network-based reinforcement learning, hence demonstrating the advantage of network topology-awareness for artificial intelligence

Backhaul-aware Robust 3D Drone Placement in 5G+ Wireless Networks

Using drones as flying base stations is a promising approach to enhance the network coverage and area capacity by moving supply towards demand when required. However deployment of such base stations can face some restrictions that need to be considered. One of the limitations in drone base stations (drone-BSs) deployment is the availability of reliable wireless backhaul link. This paper investigates how different types of wireless backhaul offering various data rates would affect the number of served users. Two approaches, namely, network-centric and user-centric, are introduced and the optimal 3D backhaul-aware placement of a drone-BS is found for each approach. To this end, the total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks, respectively. Moreover, as it is preferred to decrease drone-BS movements to save more on battery and increase flight time and to reduce the channel variations, the robustness of the network is examined as how sensitive it is with respect to the users displacements.Comment: in Proc. IEEE ICC2017 Workshops, FlexNets201

Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing