Heterogeneous Statistical QoS-Driven Resource Allocation Over MIMO-OFDMA Based 5G Cognitive Radio Networks
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abstract
2017 IEEE. With the explosive development of the next era for mobile wireless networks, there has been a lot of studies in the promising techniques for multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G-promising techniques, multiple input multiple output-orthogonal frequency-division multiple access (MIMO-OFDMA) based cognitive radio schemes are proposed to significantly improve the system capacity while mitigate the interference for future dynamic spectrum access networks. However, due to the heterogeneity caused by different links of simultaneous traffics over the wireless relay, supporting diverse delay-bounded QoS guarantees for MIMO-OFDMA based cognitive radio networks (CRNs) imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this paper we propose the heterogeneous QoS- driven resource allocation scheme by applying the MIMO-OFDMA based relaying scheme over CRNs. In particular, under the Nakagami-m fading model, we establish the MIMO-OFDMA based system model. Then, given the heterogeneous statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal power-allocation policies for the MIMO- OFDMA based CRNs. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of effective capacity to efficiently implement the heterogeneous statistical QoS over MIMO-OFDMA based CRNs.
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2017 IEEE Wireless Communications and Networking Conference (WCNC)
