Small cell networks, as an important evolution path for next‐generation cellular networks, have drawn much attention. Different from the traditional base stations (BSs) always‐on model, we proposed a BSs on‐off model, where a new, simple expression for the probabilities of active BSs in a heterogeneous network is derived. This model is more suitable for application in practical networks. Based on this, we develop an analytical framework for the performance evaluation of small cell networks, adopting stochastic geometry theory. We derive the system coverage probability; average energy efficiency (AEE) and average uplink power consumption (AUPC) for different association strategies; maximum biased received power (MaBRP); and minimum association distance (MiAD). It is analytically shown that MaBRP is beneficial for coverage but will have some loss in energy saving. On the contrary, MiAD is not advocated from the point of coverage but is more energy efficient. The simulation results show that the use of range expansion in MaBRP helps to save energy but that this is not so in MiAD. Furthermore, we can achieve an optimal AEE by establishing an appropriate density of small cells.

In cognitive radio (CR) networks, cognitive users need continuously monitor spectrum to decrease or avoid interference to primary users, yet attain a reasonable throughput. In this paper, we exploit a scheme to dynamically change the detection time duration to gain maximum throughput. Meanwhile the average de-tection time is kept as small as possible. The advantages of our approaches are approved by the deductions. Also the simulation results enable us to recognize the improved performance of our scheme over those ones with fixed detection time assignment