A graph theoretic approach to scheduling in cognitive radio networks

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Nexgen Technology provides final year IEEE projects for 2015-2016. It is located in Pondicherry, India and specializes in projects related to scheduling in cognitive radio networks. The document describes several graph-theoretic scheduling problems including: 1) A polynomial-time algorithm for throughput-maximizing scheduling. 2) The NP-Hardness of max-min fair scheduling and an approximation algorithm. 3) The NP-Hardness and inapproximability of proportionally fair scheduling.

NEXGEN TECHNOLOGY
FINAL YEAR IEEE PROJECTS 2015-2016
NEXGEN TECHNOLOGY (0)9751442511, 9791938249
#66, 4st Cross, Venkata Nagar, Near SBI ATM Pondicherry-605 011.
Nexgentech15@gmail.com www.nexgenproject.com
1. A Graph-Theoretic Approach to Scheduling in Cognitive Radio Networks
We focus on throughput-maximizing, max-min fair, and proportionally fair scheduling problems
for centralized cognitive radio networks. First, we propose a polynomial-time algorithm for the
throughput-maximizing scheduling problem. We then elaborate on certain special cases of this
problem and explore their combinatorial properties. Second, we prove that the max-min fair
scheduling problem is NP-Hard in the strong sense. We also prove that the problem cannot be
approximated within any constant factor better than 2 unless P=NP. Additionally, we propose an
approximation algorithm for the max-min fair scheduling problem with approximation ratio
depending on the ratio of the maximum possible data rate to the minimum possible data rate of a
secondary users. We then focus on the combinatorial properties of certain special cases and
investigate their relation with various problems such as the multiple-knapsack, matching,
terminal assignment, and Santa Claus problems. We then prove that the proportionally fair
scheduling problem is NP-Hard in the strong sense and inapproximable within any additive
constant less than log(4/3). Finally, we evaluate the performance of our approximation algorithm
for the max-min fair scheduling problem via simulations. This approach sheds light on the
complexity and combinatorial properties of these scheduling problems, which have high practical
importance in centralized cognitive radio networks.

Rajat Nagpal completed his M.Tech in Artificial Intelligence from the Indian Institute of Science, Bengaluru in 2020. His areas of focus included machine learning, natural language processing, deep learning, and speech processing. He conducted several projects related to text summarization, machine translation, relation extraction, generative adversarial networks, and more. His skills include Python, C, PyTorch, Linux, and he has work experience with Cityflo 350 and DMRC.

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