Statistical Interference Modeling and Coexistence Strategies in Cognitive Wireless Networks
| dc.contributor.author | Babaei, Alireza | |
| dc.creator | Babaei, Alireza | |
| dc.date | 2009-05-01 | |
| dc.date.accessioned | 2009-07-25T22:32:14Z | |
| dc.date.available | NO_RESTRICTION | |
| dc.date.available | 2009-07-25T22:32:14Z | |
| dc.date.issued | 2009-07-25T22:32:14Z | |
| dc.description.abstract | Cognitive radio is a novel approach for better utilization of the scarce, already packed but highly underutilized radio spectrum. To this end, environment-aware unlicensed secondary wireless devices are envisioned to share the spectrum with the primary licensed network, provided that their operation does not impose unmanageable interference on the primary nodes. To achieve this coexistence goal, interference modeling is of great significance. Interference, in general, has a stochastic nature not only due to randomness in the propagation channel, but also due to the random geographic dispersion of nodes. A statistical representation for interference, in which the power levels of the secondary nodes influence the parameters of the model, is, thus, of considerable interest in analysis and design of cognitive wireless network. Stochastic geometry and spatial point processes are used for modeling the coexisting primary and secondary networks. In particular, we model these networks using spatial bivariate Poisson processes. We obtain statistical properties of the distances in these processes and use them for modeling the interference from secondary network on the primary nodes. We first consider an approximate Gaussian model for interference assuming that Central Limit Theorem (C.L.T) can be applied. We, then, show that a more accurate model for interference is the sum of a Normal and a Log-normal random variables. The power levels of secondary nodes can be adjusted to obtain desirable values for the parameters in both of these models. Having this characterization of interference, we propose power control strategies for the secondary network which assure the satisfaction of interference constraint at the primary nodes. We show that these strategies are very easy to implement with little coordination requirement. Nodes either need to know where they are located in the sequence of nodes ordered according to their Euclidean distance to a primary node or need no location information, based on which strategy is being used. Given that secondary nodes have imposed power control strategies to coexist with the primary nodes, we find the lower bound of achievable throughput for the secondary nodes. We use the statistical properties of distances between secondary nodes and find an upper bound for the interference of secondary network on an arbitrary secondary node and thereby a lower bound for its throughput. We show that the approach is applicable to finding the throughput in a general power-constrained random network. | |
| dc.identifier.uri | https://hdl.handle.net/1920/4596 | |
| dc.language.iso | en_US | |
| dc.subject | Cognitive wireless network | |
| dc.subject | Power control | |
| dc.subject | Interference modeling | |
| dc.subject | Bivariate Poisson process | |
| dc.title | Statistical Interference Modeling and Coexistence Strategies in Cognitive Wireless Networks | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy in Electrical and Computer Engineering |