Filter bank multicarrier (FBMC) prevents degradation of transmission efficiency caused by the guard interval (GI). However, the signal-to-interference-plus-noise ratio (SINR) deteriorates owing to inter-symbol interference (ISI) and inter-carrier interference (ICI) in multipath fading channels. On the other hand, orthogonal frequency-division multiplexing (OFDM) can mitigate the effects of ISI by inserting a GI, thereby maintaining an excellent performance. However, the transmission efficiency decreases owing to GI. This study leverages the distinctive properties of both FBMC and OFDM and proposes a hybrid approach that can achieve robust transmission efficiency even in the face of a varying delay spread. In particular, we theoretically calculate the transmission efficiency of FBMC and OFDM based on the instantaneous channel response, choose a transmission method that offers superior efficiency, and ensure high transmission efficiency regardless of the wireless channel conditions. Furthermore, we validate the effectiveness of our proposed method through computer simulations and compare its performance with those of the FBMC-only and OFDM-only approaches.

Network virtualization allows the provision of various network services and enables flexible control by dynamically changing the path according to the service etc. While conventional network tomography uses path information to estimate the internal network status, such as each link delay, dynamic path changes make it difficult to determine the path that a packet will take. For networks with undeterministic routing, this study proposes a method for estimating the status of each link using a neural network that does not require path information as an input. Instead, it estimates the status of each link using only end-to-end measurements. The neural network is trained using various patterns of individual link statuses as teaching signals on a simulated network where the path changes dynamically. We evaluated the effectiveness of our method through simulations. The results show that the proposed method can identify degraded links with a true positive rate of 98% and false positive rate of 8%.