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      주파수 수요와 주파수 재할당 비용을 고려한 무선통신 네트워크 재설계 = Redesigning Radio Networks Considering Frequency Demands and Frequency Reassignment Cost

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      https://www.riss.kr/link?id=A82586030

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      다국어 초록 (Multilingual Abstract)

      In this Paper, we present a frequency reassignment problem (FRP) arising from the reconfiguration of radio networks such as adding new base stations (BSs) and changing the number of frequencies assigned to BSs. For this problem, we develop an integer programming (IP) model that minimizes the sum of frequency reassignment cost and the cost for unsatisfied frequency demands, while avoiding interference among frequencies. To obtain tight lower bounds, we develop some valid inequalities and devise an objective function relaxation scheme. Also, we develop a simple but efficient heuristic procedure to solve large size problems. Computational results show that the developed valid inequalities are effective for improving lower bounds. Also, the proposed tabu search heuristic finds tight upper bounds with average optimality gap of 2.3%.
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      In this Paper, we present a frequency reassignment problem (FRP) arising from the reconfiguration of radio networks such as adding new base stations (BSs) and changing the number of frequencies assigned to BSs. For this problem, we develop an integer ...

      In this Paper, we present a frequency reassignment problem (FRP) arising from the reconfiguration of radio networks such as adding new base stations (BSs) and changing the number of frequencies assigned to BSs. For this problem, we develop an integer programming (IP) model that minimizes the sum of frequency reassignment cost and the cost for unsatisfied frequency demands, while avoiding interference among frequencies. To obtain tight lower bounds, we develop some valid inequalities and devise an objective function relaxation scheme. Also, we develop a simple but efficient heuristic procedure to solve large size problems. Computational results show that the developed valid inequalities are effective for improving lower bounds. Also, the proposed tabu search heuristic finds tight upper bounds with average optimality gap of 2.3%.

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      목차 (Table of Contents)

      • 1. Introduction
      • 2. Integer Programming Model
      • 3. Lower Bounds
      • 4. Solution Procedure
      • 5. Computational Results
      • 1. Introduction
      • 2. Integer Programming Model
      • 3. Lower Bounds
      • 4. Solution Procedure
      • 5. Computational Results
      • 6. Conclusions
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