Cognitive Spectrum Access for Underwater Acoustic Communications

While very successful in traditional radio communications, the usage of TDMA and CSMA schemes for underwater acoustic communications is severely limited in efficiency and scalability, primarily due to the very large propagation delays. FDMA seems a viable alternative in that the propagation delay does not impact significantly its efficiency. However, in underwater communications, the capacity achievable on a particular channel depends strongly both on its frequency and on the communication distance, unlike in traditional radio transmissions where FDMA channels usually have comparable performance. Therefore, fixed channel allocation schemes traditionally used for radio FDMA do not perform well in underwater communications. In this paper, we investigate the application of the principles of cognitive radio and dynamic spectrum access to underwater communications. In particular, we propose a channel allocation scheme which exploits user location knowledge in order to maximize the minimum channel capacity among those achieved by the users. This provides maximum fairness and makes a more efficient use of the available spectrum resources. Performance evaluation carried out by means of simulation shows that our approach can achieve a great improvement in fairness among users, with respect to fixed allocation schemes, while at the same time scaling much better and thus allowing effective communications over larger distances.

[1]  M.J. Ryan,et al.  A Propagation-delay-tolerant Collision Avoidance Protocol for Underwater Acoustic Sensor Networks , 2006, OCEANS 2006 - Asia Pacific.

[2]  Milica Stojanovic,et al.  On the relationship between capacity and distance in an underwater acoustic communication channel , 2006, Underwater Networks.

[3]  Jose Manuel dos Santos Coelho Underwater Acoustic Networks: Evaluation of the Impact of Media Access Control on Latency, in a Delay Constrained Network , 2005 .

[4]  Robert J. Urick,et al.  Principles of underwater sound , 1975 .

[5]  Richard M. Karp,et al.  A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.

[6]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[7]  Milica Stojanovic,et al.  Recent advances in high-speed underwater acoustic communications , 1996 .

[8]  Marco Miozzo,et al.  ns2-MIRACLE: a modular framework for multi-technology and cross-layer support in network simulator 2 , 2007, Valuetools 2007.

[9]  V. Rodoplu,et al.  UWAN-MAC: An Energy-Efficient MAC Protocol for Underwater Acoustic Wireless Sensor Networks , 2007, IEEE Journal of Oceanic Engineering.

[10]  Milica Stojanovic,et al.  A MAC protocol for ad-hoc underwater acoustic sensor networks , 2006, Underwater Networks.

[11]  P. Casari,et al.  A Comparison of Multiple Access Techniques in Clustered Underwater Acoustic Networks , 2007, OCEANS 2007 - Europe.

[12]  Richard M. Karp,et al.  A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.

[14]  Lawrence G. Roberts,et al.  ALOHA packet system with and without slots and capture , 1975, CCRV.

[15]  M. Stojanovic,et al.  An adaptive algorithm for differentially coherent detection in the presence of intersymbol interference , 2005, IEEE Journal on Selected Areas in Communications.

[16]  Joseph A. Rice US navy seaweb development , 2007, WuWNet '07.

[17]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[18]  M. Stojanovic,et al.  Underwater acoustic networks , 2000, IEEE Journal of Oceanic Engineering.