Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models.