Efficient radio resource management and routing mechanisms for opportunistic spectrum access networks.

摘要

Opportunistic spectrum access (OSA) promises to significantly improve the utilization of the RF spectrum. Under OSA, an unlicensed secondary user (SU) is allowed to detect and access under-utilized portions of the licensed spectrum, provided that such operation does not interfere with the communication of the licensed primary users (PUs). Cognitive radio (CR) is a key enabling technology of OSA. In this dissertation, we propose several radio resource management and routing mechanisms that optimize the discovery and utilization of spectrum opportunities in a cognitive radio network (CRN). First, we propose a sequential channel sensing and probing mechanism that enables a resource-constrained SU to efficiently identify the optimal transmission opportunity from a pool of potentially usable channels. This mechanism maximizes the SUs expected throughput by accounting for the tradeoff between the reward and overhead of scanning additional channels. The optimal channel sensing and probing process is modeled as a maximum-rate-of-return problem in optimal stopping theory. Operational parameters, such as the sensing and probing times, are optimized by exploiting the special structure of the problem. Second, we study the problem of coordinated spectrum access in CRNs, with the objective of maximizing the CRNs throughput. By exploiting the geographic relationship between an SU and its surrounding PUs, we propose the novel concept of microscopic spectrum opportunity, in which active SUs and PUs are allowed to operate in the same region, subject to power constraints. Under this framework, we formulate the coordinated channel access problem as a joint power/rate control and channel assignment optimization problem. Centralized and distributed approximate algorithms are proposed to solve this problem efficiently. Compared with its macroscopic counterpart, we show that the microscopic spectrum opportunity framework offers significant throughput gains (e.g., over 100% in our simulations). Finally, at the network layer, we study the problem of truthful least-priced-path (LPP) routing for profit-driven CRNs. The purpose of this problem is to design a route selection and pricing mechanism that guarantees truthful spectrum cost reporting from profit-driven SUs and that finds the cheapest route for end users. The problem is investigated with and without capacity constraints at individual nodes. In both cases, polynomial-time algorithms are developed to solve the LPP problem. Extensive simulations are conducted to verify the validity of the proposed mechanisms.