Heterogeneous statistical QoS provisioning over cognitive-radio based 5G mobile wireless networks

Abstract

As one of the critical techniques to support the multimedia services over mobile wireless networks, the statistical quality of service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. In the meantime, the full-duplex spectrum sensing (FD-SS) has been widely recognized as the promising candidate technique for maximizing the spectrum efficiency while provisioning the heterogeneous statistical QoS guarantees over cognitive radiobased 5G mobile wireless networks. However, due to the heterogeneity caused by different scenarios and applications of the multimedia traffics over CRNs, supporting diverse delay-bounded QoS guarantees for cognitive radio networks imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this book chapter, we propose the heterogeneous statistical QoS provisioning schemes by applying the multiple-input-multipleoutput generalized frequency division multiplexing (MIMO-GFDM) techniques to implement the FD-SS-based multimedia services in CRNs. In particular, under the Nakagami-m wireless channels, we derive the MIMO-GFDM-based physical (PHY)-layer model and the self-interference cancelation model. We develop the MIMO-GFDM-based FD-SS schemes and derive the miss-detection and false-alarm probabilities. Under the heterogeneous statistical QoS constraints, we develop the Markov chain model to characterize the aggregate effective capacity under the optimal power allocation policies using our proposed MIMOGFDM architecture over FD-SS CRNs. Also conducted is a set of simulations which validate our proposed schemes, evaluate their performances, and show that our proposed schemes can outperform the other existing solutions under the heterogeneous statistical delay-bounded QoS constraints over cognitive radiobased 5G mobile wireless networks.