Self-Deployment for Future-Indoor Wi-Fi Networks: An Artificial Intelligence Approach

The upsurge in data traffic pushed Wi-Fi operators to adopt wireless extenders to improve indoor coverage. Existing deployment approaches, however, focused on coordinated scenarios (managed by the same operator) with single-hop communication. In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughputs of the extender are optimized. We envision networks that inform the user about the optimal location of extenders based on channel conditions. To that end, we propose an Artificial Intelligence (AI) Case Based Reasoning (CBR) framework to enable autonomous self-deployment that allows the network to learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms that balance exploration and exploitation of the search space. In essence, AI stores previous information and actions in a Knowledge Base (KB) which can be reused in future situations, through reasoning, or guide the generation of new actions. Wi-Fi standard compliant ns-3 simulations evaluated the proposed self-deployment AI approach and compared its performance against existing conventional coverage maximization approaches under practical uncoordinated scenarios. Throughput fairness and ubiquitous Quality of Service (QoS) satisfaction are achieved which provide the impetus of applying the AI-driven self-deployment in practice.