Nt1310 Unit 3 Channel Assignment

Computer Communications 36 (2013) 411–420

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Computer Communications journal homepage: www.elsevier.com/locate/comcom

Effective channel assignments in cognitive radio networks
Jie Wu a, Ying Dai a,⇑, Yanchao Zhao a,b a b

Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, United States
State Key Lab of Novel Software, Department of Computer Science and Technology, Nanjing University, PR China

a r t i c l e

i n f o

Article history:
Received 5 January 2012
Received in revised form 20 October 2012
Accepted 23 October 2012
Available online 5 November 2012
Keywords:
Channel assignment
Cognitive radio networks
Dynamic spectrum
The channel assignment (CA) problem is one of the most important issues in CRNs, with the objective of satisfying the interference constraints, and maximizing the number of nodes with channels assigned. In this paper, our goal is to design highly-efficient and localized protocols for CA. In addition, we want to maximize node connectivity after CA, which is important for packet delivery. To this end, we design two basic algorithms and an advanced algorithm framework. Within this framework, we can change the edge priority in CA to meet different requirements. Simulation results show that the proposed framework is fast (two rounds of communication among nodes, regardless of network size) and outperforms an existing method.
Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction
Today we are facing a dilemma of rapidly increasing demand of wideband wireless access, and shrinking out of unallocated spectrum. Studies indicate that, at any given time and location, there exists a large portion of under-utilized licensed spectrum [1].
In this way, the assignment algorithm will converge faster without affecting the network performance. The edgetrimming strategy must follow these design objectives:
 Cut as many non-essential edges as possible, so that the algorithms will converge faster.
 Make sure the performance of networks does not degrade after channel assignment.
To achieve a better tradeoff between these two objectives, the most crucial part is how to evaluate the importance of edges.
Our method is marking the edges as essential or non-essential, according to Definition 4.

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We can develop more strategies to perform edge-trimming, like the conflict possibility and effective degrees. Due to space limitations, we will not extend these topics here.

we compare the three conflict resolutions of our third algorithm.
Moreover, we implement the approach proposed in our discussion part, and make some comparisons.

6.2. Dealing with dense networks

7.1. Simulation settings & methodology

In some cases, we may face situations of dense networks where the number of neighbors of each node is much larger than normal