5G 이동통신 셀 설계를 위한 타부 탐색과 유전 알고리즘의 성능
Performance comparison of Tabu search and genetic algorithm for cell planning of5G cellular network
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The fifth generation(5G) of wireless networks will connect not only smart phone but also unimaginable things. Therefore, 5G cellular network is facing the soaring traffic demand of numerous user devices. To solve this problem, a huge amount of 5G base stations will need to be installed. The base station positioning problem is an NP-hard problem that does not know how long it will take to solve the problem. Because, it can not find an answer other than to check the number of all cases. In this paper, to solve the NP hard problem, we compare the tabu search and the genetic algorithm using real maps for optimal cell planning. We also perform Monte Carlo simulations to study the performance of the Tabu search and Genetic algorithm for 5G cell planning. As a results, Tabu search required 2.95 times less computation time than Genetic algorithm and showed accuracy difference of 2dBm.
Abstract
The fifth generation(5G) of wireless networks will connect not only smart phone but also unimaginable things. Therefore, 5G cellular network is facing the soaring traffic demand of numerous user devices. To solve this problem, a huge amount of 5G base stations will need to be installed. The base station positioning problem is an NP-hard problem that does not know how long it will take to solve the problem. Because, it can not find an answer other than to check the number of all cases. In this paper, to solve the NP hard problem, we compare the tabu search and the genetic algorithm using real maps for optimal cell planning. We also perform Monte Carlo simulations to study the performance of the Tabu search and Genetic algorithm for 5G cell planning. As a results, Tabu search required 2.95 times less computation time than Genetic algorithm and showed accuracy difference of 2dBm.
참고문헌 (15)
- Cisco, "Cisco Visual Networking Index:Forecast and Methodology", ~ (2016)
- Cisco, "Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update", ~ (2016)
- 김문홍, "5G 이동통신기술 발전방향", 정보와 통신 열린강좌 32.9(별책 1 호), 46~54 (2015)
- "5G White Paper"[URL]
- Bai, Tianyang, "Millimeter wave cellular channel models for system evaluation", Computing, Networking and Communications (ICNC), 2014 International Conference on (2014), IEEE
- Taufique, Azar, "Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities", IEEE Access, 5, pp.4821~4845 (2017)
- Desale, Sachin, "Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey", Int. J. Comp. Eng. Res. Trends, 2(5), pp.296~304 (2015)
- "Heuristic"[URL]
- Maple Carsten, "Parallel genetic algorithms for third generation mobile network planning", Parallel Computing in Electrical Engineering, PARELEC 2004, International Conference on (2004), IEEE
- St-Hilaire, Marc, "A tabu search algorithm for the global planning problem of third generation mobile networks", Computers &Electrical Engineering, 34(6), pp.470~487 (2008)
- Kirkpatrick, Scott, "Optimization by simulated annealing", science, 220(4598), pp.671~680 (1983)
- M. St-Hilaire, "Comparison of different meta-heuristics to solve the global planning problem of UMTS networks Computer Networks", The International Journal of Computer and Telecommunications Networking, 55(12), pp.2705~2716 (2011)
- 3GPP, "Technical specification group radio access network; channel model for frequency spectrum above 6 GHz. TR 38.900, 3rd Generation Partnership Project(3GPP)", ~ (2016)
- 이승학, "GPU를 이용한 SDR 시스템 용 LTE MIMO 기지국 기능 구현", (사)디지털산업정보학회 논문지, 8(4), pp.91~98 (2012), (사)디지털산업정보학회
- 박종근, "GPU를 이용한 TDD LTE MU-MIMO 시스템에서의 재전송 구현", (사)디지털산업정보학회 논문지, 13(2), pp.35~42 (2017), (사)디지털산업정보학회[DOI]