A Stochastic Optimization Framework for Adaptive Spectrum Access and Power Allocation in Licensed-Assisted Access Networks

Licensed-assisted access (LAA) has been becoming a promising technology to the supplementary utilization of the unlicensed spectrum. However, due to the densification of small base stations (SBSs) and the dynamic variation of the number of Wi-Fi nodes in the overlapping areas, the licensed channel interference and the unlicensed channel collision could seriously influence the quality of service and the energy consumption. In this paper, jointly considering time-variant multi-wireless-channel conditions and random numbers of Wi-Fi nodes, we address an adaptive spectrum access and power allocation problem that enables the minimization of the system power consumption under a certain queue stability constraint in the LAA-enabled SBSs and Wi-Fi networks. The complex stochastic optimization problem has been decomposed as a modified Hungarian algorithm and a difference of two convex algorithm in the framework of Lyapunov optimization. We also characterize the performance bounds of the proposed algorithm with a tradeoff of
$[O(1/V), O(V)]$
between power consumption and delay theoretically. The numerical results verify the tradeoff and show that our scheme can reduce the power consumption over the existing scheme by up to 73.3% under the same traffic delay.