Transient analysis of post-interruption dynamics in cognitive radio networks: An Onsager´s regression approach

The transient dynamics of cognitive radio after being interrupted by primary users are studied. The Onsager´s regression in non-equilibrium statistical mechanics is applied. The correlation function in the equilibrium state is used to study the time evolution of the deviation from the equilibrium state, according to the principle of Onsager´s regression. Analytic approximations have been derived for the correlation function. Motivated by the insight from the approximation of the correlation function, a simple scheduling algorithm is proposed to recover the equilibrium as quickly as possible. Numerical simulations are carried out for a Jackson network model. The validity of the Onsager´s regression is verified. The derived approximation of the correlation function is demonstrated to match the true value at the beginning of the relaxation. The proposed scheduling algorithm is shown to outperform a random scheduling scheme.