Mobile Broadband Scaling and Enhancement for Fast Moving Trains

Internet is an important part of our life, whether traveling or at home. The broadband services available at home are reliable and are usually at constant speed. The people traveling especially in fast moving trains are at higher mobility and may be moving in areas of less connectivity, and providing a reliable service to them is a challenging task. One possible solution to this is to provide communication through an on-board Wi-Fi, which takes services from a central Wi-Fi situated in the middle of the train, which is connected to cellular radio service long-term evolution for railways. The network consists of LTE-R which is dedicated for railway communication only, a public mobile network, which supports LTE-R in the areas of no coverage and high traffic conditions and a public safety network in emergency conditions. The work is verified with the help of simulations on MATLAB, considering different traffic scenarios. The BSs placed at a distance of 2.5 Km and antenna height used is 45 m are equipped with 3G and 4G interfaces, a universal mobile telecommunications services (UMTS) and radio access network (RAN). The UMTS interface is used for voice services and handover when spectrum available in the next cell is less

A Deep Recurrent Q Network Towards Self-adapting Distributed Microservices Architecture (in press)

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resources over-provisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms including: i) deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The DRQN implementation in this paper manages to outperform the above mentioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training times. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms