Energy-Efficient Resource Optimization for Cognitive Multi-cell Network in 5G

In order to improve the energy efficiency of 5G communication system, this paper proposed a novel resource allocation algorithm for the cognitive multi-cell network in 5G. In the proposed algorithm, the alternate optimization framework is adopted to optimize the subcarrier assignment and power allocation, where the maximal signal-interference-plus-noise ratio criterion is applied to complete the subcarrier assignment and the successive convex approximation is utilized to solve the power allocation after transforming the problem into an equivalent one. Due to the significant spectral leakage of traditional orthogonal frequency division multiplexing (OFDM), some other modulation schemes as 5G candidates are also analyzed, e.g., filter-bank based multi-carrier (FBMC), universal filtered multi-carrier (UFMC), and generalized frequency division multiplexing (GFDM). The simulation results indicate that the proposed algorithm can achieve higher energy efficiency than the interference limited algorithm and the modulation scheme which has less spectral leakage can achieve higher energy efficiency.