Abstract
Connectivity is rapidly becoming a core feature of modern vehicles to enable the provision of intelligent services that promote safer transport networks, real-time traffic infotainment systems and remote asset monitoring. As such, a reliable communications back-bone is required to connect vehicles that deliver real-time data to smart services deployed at cloud or edge architecture tiers. Hence, reliable uplink connectivity becomes a necessity. Next-generation vehicles will be equipped with multiple wireless interfaces, and require robust mechanisms for reliable and efficient management of such communication interfaces. In this context, the contribution of this article is a learning based approach for interface selection known as the Multi-Armed Bandit Adaptive Similarity-based Regressor (MABASR). MABASR takes advantage of the underlying linear relationship between channel quality parameters and uplink data rate to realise a robust interface selection policy. It is shown how this approach outperforms algorithms developed in prior work, achieving up to two orders of magnitude lower standard deviation of the obtained reward when trained on different data sets. Thus, higher reliability and less dependency on the structure of the training data are achieved. The approach is tested in mobile, static, and artificial static scenarios where severe network congestion is simulated. All data sets used for the evaluation are made publicly available.
Highlights
The ModLinUCB would be using its policy to learn the model of every cellular connection by sampling one sample at a time
MEASUREMENT AND SIMULATION METHODOLOGY In order to investigate what kind of patterns exist between channel quality parameters and uplink rate, data which has already been acquired for the evaluation of the ModLinUCB [1] was utilised
Multi-Armed Bandit Adaptive Similarity-based Regressor (MABASR) The outcomes of above analysis can be exploited by creating an interface selection policy that will maximize the linear correlation between input and output while learning new samples, i.e. data that does not comply with the policy’s goal can be ignored
Summary
A STABLE, reliable end-to-end connection delivered via Vehicle-to-Infrastructure (V2I) communications backbone is needed to support advanced services for nextgeneration vehicles. One approach to achieve this is to provide an agent that learns the current state of the environment by performing actions (i.e. selecting a different interface) based on a specified policy to obtain rewards. For the selection of the most favorable network, a multi-attribute decision making algorithm is suggested in [5] The issue with these approaches is that they require a lot of parameters (e.g. packet delay, packet jitter, etc.) in order to take a decision on which interface to use. B. MULTI-ARMED BANDIT ALGORITHMS The potential applications of multi-armed bandit algorithms in the field of wireless networks have been discussed in [12] where an overview of the use of this type of algorithm is presented, in the context of channel selection in a dynamic environment. The aforementioned algorithm is not suitable for this interface selection problem
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