Search results for "Cognitive network"
showing 3 items of 13 documents
Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation
2014
The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions. In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software…
Significance of channel failures on network performance in CRNs with reserved spectrum
2016
It is well understood that in wireless networks, channel failures, which are typically caused by equipment or power failures as well as intrinsic features in radio transmissions, such as fading and shadowing, can easily result in network performance degradation. Therefore, fast recovery from channel failures is an important measure that should be incorporated with those networks. Consequently, in a cognitive radio network (CRN), channel failures can cause significant performance degradation in both primary and secondary networks. Instead, retainability, i.e., the capability of providing continuous connection for users must be guaranteed even if a significant network element is disrupted. In…
On the use of composite indicators for mobile communications network management in smart sustainable cities
2020
Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network&rsquo