Spatial Reuse for Practical Scenarios: Iterative Power Adjustment from Distributed Contour Estimation and Propagation

The number of wireless networks that coexist in space is increasing steeply. To allow coexistence while avoiding interference, it becomes important to properly characterize the propagation contour where the received power of wireless transmitters reaches a certain threshold. Detailed channel modeling taking into account the specificities of typical urban scenarios is however a very complex task. Thanks to the widespread use of wireless access technology, it becomes feasible to use network nodes to estimate and communicate these propagation contours. In this paper, we propose a lightweight practical scheme for local contour estimation of a given transmitter. The local estimate is efficiently propagated to all other secondary transmitters that can then meet interference constraints optimally, i.e., without having to consider large safety margins that limit spatial reuse gains. This optimality is obtained through iterative power control based on true propagation contour distances and local pathloss estimates. The overhead of the estimation and communication phases is simulated to be close to linear in the number of nodes, so that the solution scales well. The scheme can be used for optimal power control in practical wireless networks, or for the deployment of secondary networks in areas with primary transmitters that should be protected.

[1]  Brian M. Sadler,et al.  Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy , 2006, ArXiv.

[2]  John S. Seybold,et al.  Introduction to RF Propagation: Seybold/Introduction to RF Propagation , 2005 .

[3]  Martin Mauve,et al.  A survey on position-based routing in mobile ad hoc networks , 2001, IEEE Netw..

[4]  B. Ripley Statistical inference for spatial processes , 1990 .

[5]  Debasis Mitra,et al.  An Asynchronous Distributed Algorithm for Power Control in Cellular Radio Systems , 1994 .

[6]  Anant Sahai,et al.  Fundamental tradeoffs in robust spectrum sensing for opportunistic frequency reuse , 2006 .

[7]  Songwu Lu,et al.  A scalable solution to minimum cost forwarding in large sensor networks , 2001, Proceedings Tenth International Conference on Computer Communications and Networks (Cat. No.01EX495).

[8]  Chong-kwon Kim,et al.  Multicast tree construction and flooding in wireless ad hoc networks , 2000, MSWIM '00.

[9]  Hisato Iwai,et al.  Path loss measurement in 5 GHz macro cellular systems and consideration of extending existing path loss prediction methods , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[10]  J. Seybold Introduction to RF Propagation , 2005 .

[11]  W.C.Y. Lee,et al.  Estimate of local average power of a mobile radio signal , 1985, IEEE Transactions on Vehicular Technology.

[12]  Sai Shankar Nandagopalan,et al.  IEEE 802.22: An Introduction to the First Wireless Standard based on Cognitive Radios , 2006, J. Commun..

[13]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.