Energy Consumption Optimization for Multihop Cognitive Cellular Networks

Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC²N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 - ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.