Statistical QoS-Driven Power Adaptation Over MIMO-GFDM Based Underwater Wireless Networks

With the explosive developments of underwater acoustic communications, underwater acoustic communications have played a critically important role in many critical applications, such as oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, and tactical surveillance. Due to hash underwater environment, underwater acoustic communications impose new challenges to wireless communications research. Therefore, to guarantee the system performance with minimum resource consumption for underwater acoustic channel, we propose the novel statistical QoS-driven power adaptation over MIMO generalized frequency division multiplexing (GFDM) [1] based underwater wireless networks. The objective of underwater acoustic communications is to transmit large-volume data information at a high-data rate with quality of service (QoS) provisioning. The statistical QoS guarantees [2], in terms of effective capacity and queue-length-bound/delay-bound violation probabilities, have been proposed and demonstrated as the powerful way to characterize delay QoS requirements for wireless traffics. On the other hand, to support the underwater acoustic wireless networks, for the last several years researchers have made a great deal of efforts in investigating various advanced wireless techniques, such as the MIMO-OFDM scheme [3]. However, the complicated application scenarios present new challenges making the OFDM less efficient in various underwater wireless networks. To overcome the aforementioned challenges, in this paper we propose the novel GFDM based scheme over underwater wireless networks.

[1]  Jia Tang,et al.  Cross-layer design of dynamic resource allocation with diverse QoS guarantees for MIMO-OFDM wireless networks , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[2]  Gerhard Fettweis,et al.  GFDM Interference Cancellation for Flexible Cognitive Radio PHY Design , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[3]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[4]  Jia Tang,et al.  Cross-layer resource allocation over wireless relay networks for quality of service provisioning , 2007, IEEE Journal on Selected Areas in Communications.