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Brain-aware wireless networks: Learning and resource management


Human-centric applications such as virtual reality and immersive gaming will be central to the future wireless networks. Common features of such services include: a) their dependence on the human user's behavior and state and b) their need for more network resources compared to conventional cellular applications. To successfully deploy such applications over wireless and cellular systems, the network must be made cognizant of the human in the loop. In this paper, a concrete measure for the delay perception of human users in a wireless network is defined. Then, a new learning method called probability distribution identification is introduced to find a probabilistic model for this delay perception. A novel approach based on Lyapunov optimization is proposed for allocating radio resources to human users while considering their brain's delay perception model. Simulation results show that a brain-aware approach can yield savings of up to 37% in power compared to the system that only considers quality of service metrics.
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hal-02877285 , version 1 (22-06-2020)



Ali Zadeh K., Walid Saad, Mérouane Debbah. Brain-aware wireless networks: Learning and resource management. 2017 51st Asilomar Conference on Signals, Systems, and Computers, Oct 2017, Pacific Grove, United States. pp.1784-1788, ⟨10.1109/acssc.2017.8335668⟩. ⟨hal-02877285⟩
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