Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Low complexity resource allocation with opportunistic feedback over downlink OFDMA networks

Abstract: Optimal tone allocation in downlink OFDMA networks is a non-convex NP-hard problem that requires extensive feedback for channel information. In this paper, two constant complexity limited-feedback algorithms are proposed to achieve near-optimal performance. First, using opportunistic feedback, the proposed schemes are shown to reduce feedback overhead by requiring only users likely to be allocated resources to feedback. There are differences between the two proposed schemes for implementation of the feedback protocol. One scheme requires less feedback but is contention-based, while the other scheme is sequential and thus avoids possible collisions leading to slightly higher performance, but needs more feedback. Second, complexity is reduced for resource allocation by solving the optimization problem in a distributed manner, rather than centrally at the base station. As shown both analytically and through numerical results, these distributed algorithms reduce the required feedback overhead significantly, and achieve constant computational complexity with little performance loss compared to the optimal solution.

Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach

Abstract: The purpose of this paper is to identify at-risk online students earlier, more often, and with greater accuracy using time-series clustering. The case study showed that the proposed approach could generate models with higher accuracy and feasibility than the traditional frequency aggregation approaches. The best performing model can start to capture at-risk students from week 10. In addition, the four phases in student’s learning process detected holiday effect and illustrate at-risk students’ behaviors before and after a long holiday break. The findings also enable online instructors to develop corresponding instructional interventions via course design or student–teacher communications.