Dynamic fair node spectrum allocation for ad hoc networks using random matrices

Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.

[1]  G. Reinelt The traveling salesman: computational solutions for TSP applications , 1994 .

[2]  Jae-Hak Chung,et al.  A non-interfering cognitive radio system for spectrum sharing , 2009, 2009 9th International Symposium on Communications and Information Technology.

[3]  Ariel D. Procaccia,et al.  No agent left behind: dynamic fair division of multiple resources , 2013, AAMAS.

[4]  Toby Walsh,et al.  Online Cake Cutting , 2010, ADT.

[5]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[6]  Reza Berangi,et al.  Mobility assisted spectrum aware routing protocol for cognitive radio ad hoc networks , 2013, Journal of Zhejiang University SCIENCE C.

[7]  Luciano Bononi,et al.  Cooperative spectrum management in cognitive Vehicular Ad Hoc Networks , 2011, 2011 IEEE Vehicular Networking Conference (VNC).

[8]  Béla Bollobás,et al.  Random Graphs , 1985 .

[9]  Ali Gorcin,et al.  A SIGNAL IDENTIFICATION APPLICATION FOR COGNITIVE RADIO , 2007 .

[10]  John A. Camara Electrical Engineering Reference Manual, for the Electrical and Computer PE Exam, 7th ed. , 2006 .

[11]  Marco Moretti,et al.  Robust frequency synchronization for OFDM-based cognitive radio systems , 2008, IEEE Transactions on Wireless Communications.

[12]  Taieb Znati,et al.  A path availability model for wireless ad-hoc networks , 1999, WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466).

[13]  David B. Chester,et al.  Quantifying the relative merits of genetic and swarm algorithms for network optimization in cognitive radio networks , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[14]  Si Chen,et al.  Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks , 2012 .

[15]  H. V. Trees Detection, Estimation, And Modulation Theory , 2001 .

[16]  Wenye Wang,et al.  Modeling and Analysis of Connectivity in Mobile Ad Hoc Networks with Misbehaving Nodes , 2006, 2006 IEEE International Conference on Communications.

[17]  Thomas L. Marzetta,et al.  Detection, Estimation, and Modulation Theory , 1976 .

[18]  Sanada Yukitoshi,et al.  Spectrum Sensing Algorithms via Finite Random Matrix Theory , 2012 .

[19]  Scott Shenker,et al.  Analysis and simulation of a fair queueing algorithm , 1989, SIGCOMM 1989.

[20]  Philip Wolfe,et al.  Contributions to the theory of games , 1953 .

[21]  L. S. Shapley,et al.  17. A Value for n-Person Games , 1953 .

[22]  Benjamin Hindman,et al.  Dominant Resource Fairness: Fair Allocation of Multiple Resource Types , 2011, NSDI.

[23]  Ramin Hekmat,et al.  Ad-hoc networks - fundamental properties and network topologies , 2006 .

[24]  Maarten van Steen,et al.  Network-level synchronization in decentralized social ad-hoc networks , 2010, 5th International Conference on Pervasive Computing and Applications.

[25]  Si Chen,et al.  On optimizing vehicular dynamic spectrum access networks: Automation and learning in mobile wireless environments , 2011, 2011 IEEE Vehicular Networking Conference (VNC).

[26]  A. Edelman,et al.  Random matrix theory , 2005, Acta Numerica.

[27]  Robert N. McDonough,et al.  Detection of signals in noise , 1971 .

[28]  Youping Zhao,et al.  Radio environment map-based cognitive Doppler spread compensation algorithms for high-speed rail broadband mobile communications , 2012, EURASIP Journal on Wireless Communications and Networking.