On routing in Cognitive Radio Networks

Abstract

Cognitive Radio Networks (CRNs) are expected to address and to resolve important technological and operational challenges of future networks, such as Dynamic Spectrum Access (DSA), routing in heterogeneous networks and provision of Quality of Experience (QoE) for different applications. Accordingly, a number of new terminal and network functionalities are required to solve the technical problems and to provide efficient management of CRNs. The paper advances a new solution for routing in CRNs, providing communication between Secondary Users (SUs) occupying different so-called Spectrum Opportunities (SOPs), i.e., portions of spectrum not occupied by Primary Users (PUs). This entails solving a complex process composed by two fundamental elements. These elements are multi-constraint routing and multi-dimensional adaptation for multiple cognitive radio dimensions like space, frequency, power and time. The goal of the paper is to develop optimization algorithms necessary to provide end-to-end (e2e) routing paths for communication between SUs with associated QoE demands.

Communication mechanisms for cognitive radio networks

“…A knowledge database is maintained at every SN to represent all relevant CRN information [3]. Ultimately, data representation allows for the operational parameters of each network member device to be described in every CR-dimension, e.g., frequency (depicted as device spectrum opportunities (DSOPs), given that every SU needs a portion of an available spectrum opportunity (SOP) to operate in, regardless of the used modulation [5]), power (transmit output power) and geographic point mapped to a set of virtual space coordinates (x,y) referred to as virtual identifier (VID) Φ. This is selected with regard to the actual geographical location of the hosts in the physical CRN coverage area.…”

“…A newly arriving CRD that wishes to join a CRN must therefore first contact the responsible SN to request a virtual operational zone (if available) within the imposed topology structure. Naturally, this requires the presence of a free DSOP within an available SOP in the particular SN coverage area [5]. Typically, SOPs are partitioned in three categories with reference to the activity and holding times of the licensed users: Static, Dynamic and Opportunistic [2], [6].…”

“…Typically, SOPs are partitioned in three categories with reference to the activity and holding times of the licensed users: Static, Dynamic and Opportunistic [2], [6]. However, since the holding time of an e2e path must be long enough to allow for it to be traversed [5], only static/dynamic SOPs are considered in the suggested CRN communication framework. Meaning, the disruptive nature of opportunistic SOPs is currently disregarded.…”

“…A SN computed and optimized routing path allows for e2e goals of the source to be considered. SN imposes different constraints on the routing path, denoting ad-hoc packet routing with aggregated throughput obtained by employing suitable Multi-Objective, MultiConstrained optimization algorithms [5]. In the case of distributed greedy routing, the destination is reached by moving from one neighbor to another without any regards to optimization constraints.…”

“…Given the initial required dimensioning of cluster sizes with regard to the environmental constraints of the CRN geographical area, direct communication between devices in neighboring clusters is considered, excluding so the need of intermediate relays. Hence, an e2e path can be computed where one hop per cluster is sufficient and each hop is determined according to optimization preferences, like throughput, cost, and delay [5]. The optimization problem of selecting an e2e routing path subject to multiple constraints can be of type Multi-Constrained Path (MCP) or Multi-Constrained Optimal Path (MCOP), where popular routing algorithms are Self-Adaptive Multiple Constraints Routing Algorithms (SAMCRA) and Particle Swarm Optimization [7], [8].…”