The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks.

No abstract

No abstract

SUMMARY Random testing (RT), which simply selects test cases at random from the whole input domain, has been widely applied to test software and assess the software reliability. However, it is controversial whether RT is an effective method to detect software failures. Adaptive random testing (ART) is an enhancement of RT in terms of failure‐detection effectiveness. Its basic intuition is to evenly spread random test cases all over the input domain. There are various notions to achieve the goal of even spread, and each notion can be implemented by different algorithms. For example, ‘by exclusion’ and ‘by partitioning’ are two different notions to evenly spread test cases. Restricted random testing (RRT) is a typical algorithm for the notion of ‘by exclusion’, whereas the notion of ‘by partitioning’ can be implemented by either the technique of bisection (ART‐B) or the technique of random partitioning (ART‐RP). In this paper, we propose a generic approach that can be used to implement different notions. In the new approach, test cases are simply selected based on test profiles that are in turn designed according to certain notions. In this study, we design several test profiles for the notions of ‘by exclusion’ and ‘by partitioning’, and then use these profiles to illustrate our new approach. Our experimental results show that compared with the original RRT, ART‐B, and ART‐RP algorithms, our new approach normally brings at least a higher failure‐detection capability or a lower computational overhead. Copyright © 2011 John Wiley & Sons, Ltd.

No abstract