Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

Energy efficiency (EE) has now become one of the essential parameters in the design of 5G and other next‐generation communication networks mainly due to economic and environmental concerns. In this paper, we develop an EE maximisation algorithm for the orthogonal frequency‐division multiplexing (OFDM)–based cognitive radio (CR) systems where the PU activity is highly dynamic. The problem is formulated as a joint optimisation problem in 3 variables: sensing duration, transmission duration, and transmit power on the subcarrier, with statistical constraint on the primary user (PU) interference. The statistical interference constraint frees the CR transmitter from the additional burden of obtaining the instantaneous channel quality feedback from the PU receivers. Furthermore, PU mobility, which is the main consideration of this work, closely portrays a real and practical CR network. We analyse how the mobility of the PUs affect their Quality of Service, particularly in terms of the interference impinged on PUs by the SUs. Moreover, the mobility of the PUs has been modelled, and PU interference constraints are reformulated by considering collisions between the PUs and SUs during their overlap period. Moreover, closed‐form expressions for secondary throughput and EE have been derived. The original problem, which is mathematically intractable due to its nonconvexity, is reformulated in terms of 3 alternately iterating subproblems. Numerical results show that with the proposed optimal algorithm, CR users can achieve significantly higher EE compared with existing algorithms. The suboptimal algorithm guarantees reduced complexity with a performance gap close to zero with the original optimal problem. In this article, an energy‐efficiency maximization problem for orthogonal frequency division multiplexing(OFDM)‐ based Cognitive Radios(CRs) is presented. PU mobility, which is the main consideration of this work, closely portrays a real and practical CR network. The effect of mobility of the PUs on their own Quality of Service is brought into light through this work. A joint optimization problem is formulated to maximize energy efficiency under this scenario and the proposed algorithm achieves significantly higher EE compared with existing algorithms.