Online Learning Based Transmission Scheduling for Delay-Sensitive Data Over a Fading Channel With Imperfect Channel State Information

This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then, the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. Numerical results indicate that the proposed online learning-based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay, and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning-based scheduling for practical systems.