Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks

Load balancing (LB) is a key challenge in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a non-trivial training process. The state-of-the-art learning-aided LB methods require retraining when the network environment (particularly the user number) changes, significantly limiting their practicability. In this paper a novel deep neural network (DNN) structure, named adaptive target-condition neural network (A-TCNN), is proposed to tackle the LB issue for a varying number of users, without the need for retraining. Unlike the existing LB methods conducting AP selection for all users together, the new method performs AP selection for a single target user, upon the condition of other users. Also, A-TCNN involves an adaptive mechanism which maps any smaller number of users to a preset number by splitting the users’ data rate requirements, without affecting the AP selection result for the target user. Once trained, A-TCNN can be used for any user numbers not exceeding the maximum user number that the network can support. Results show that apart from the adaptiveness to a varying user number, A-TCNN provides a higher network throughput (up to 45%) than the conventional DNN in most cases, especially for a larger scale of network. In terms of computational complexity, A-TCNN can achieve a sub-millisecond level runtime, which is 2 orders of magnitude lower than fuzzy logic and 3 orders of magnitude lower than game theory.