A novel layered graph model for topology formation and routing in dynamic spectrum access networks

Abstract

This paper studies a fundamental problem in dynamic spectrum access (DSA) networks: given a set of detected spectrum bands that can be temporarily used by each node in a DSA network, how to form a topology by selecting spectrum bands for each radio interface of each node, called topology formation in this paper. We propose a novel layered graph to model the temporarily available spectrum bands, called spectrum opportunities (SOPs) in this paper, and use this layered graph model to develop effective and efficient routing and interface assignment algorithms to form near-optimal topologies for DSA networks. We have evaluated the performance of our layered graph approach and compared it to a sequential interface assignment algorithm. The numerical results show that the layered graph approach significantly outperforms the sequential interface assignment.

Routing in cognitive radio networks: Challenges and solutions

“…The authors of [12,13] propose a comprehensive framework to address channel assignment and routing jointly in semi-static multi-hop CRNs. In these works, the PU dynamics are assumed to be low enough such that the channel assignment and the routing among SUs can be statically designed.…”

Maximum Lifetime Communication Topologies of Secondary User Nodes in Cognitive Radio Ad hoc Networks

“…8, No. 3;2015 The solutions based on the full spectrum knowledge assume each SU node to be completely aware of all the available PU channels in the network and choose optimal routes with respect to either minimum number of hops per SU-SU path (Xin et al, 2005), maximum conflict-free assignment (Zhou et al, 2009) of PU channels or minimum number of channel switches per SU-SU path (Xin et al, 2008); there is bound to be switching of channels when none of the PU channels available for the end nodes of an SU-SU link are the same as the preferred PU channels for one or both the end nodes to which they stay tuned by default for transmission and reception. Such full spectrum knowledge-based solutions take a centralized approach like we took in this paper; however, the full spectrum knowledge for the current time instant alone cannot be used to arrive at benchmarks for the routing metric, if one intends to use the LORA strategy of staying on with a route as long as it exists.…”

“…Thus, even if the SU nodes and PU nodes are static, communication topologies (like paths and trees) that connect the SU nodes may have to be frequently reconfigured based on the PU channels' availability in the SU-SU neighborhoods. The routing solutions for CRAHNs proposed so far have focused mainly on discovering optimal routes with respect to traditional metrics like hop count (Xin et al, 2005), end-to-end delay, with the channel switch delay considered an integral component of end-to-end delays (Cheng et al, 2007a;Cheng et al, 2007b;Ma et al, 2008), throughput (Shi & Hou, 2008), energy consumption (Pyo & Hasegawa, 2007) and etc. Not much work has been done on determining stable paths or trees whose lifetime is longer.…”

Cognitive Radio-Based Vehicular Ad Hoc and Sensor Networks

“…An energy efficient and fair routing algorithm for vehicular sensor networks has been proposed by us already [33]. CR-VASNET inherits network layer issues from traditional WSNs [34, 35] and mobile ad hoc networks (MAENT) [36–38] such as infrastructure-less, unstable topology, multihop networking, energy efficiency data-centric routing, attribute-based routing, and localization, and rather than the mentioned challenges, the proposed routing protocols for CR-VASNET must consider dynamic spectrum access issues like channel access delay, interference, operating frequency, and bandwidth too. Furthermore, CR-VASNET as a multihop network, which the RSS nodes send data packets to the next hope, spectrum decision may change the next hope status.…”