4G Heterogeneous Networks Base Station Planning Using Evolutionary Multi-objective Algorithm

Author(s):  
Xueyi Liang ◽  
Hailin Liu ◽  
Qiang Wang
Author(s):  
Shumin Wang ◽  
Honggui Deng ◽  
Rujing Xiong ◽  
Gang Liu ◽  
Yang Liu ◽  
...  

AbstractThe emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies.


2021 ◽  
Author(s):  
Mhd Amen Summakieh ◽  
Chee Keong Tan ◽  
Yin Hoe Ng ◽  
Ayman A. El-Saleh

Abstract Heterogeneous networks (HetNets) are a promising communication paradigm to satisfy the diverse requirements of Long Term Evolution-Advanced (LTE-A). Associating users with different base station tiers using the conventional technique based on the highest received SINR is not viable in HetNets due to its rigid association, which only aims at throughput maximization. Many e orts have been made to tackle the optimization problem of user association with a single objective such as throughput, fairness or energy efficiency. In this paper, we propose a novel multi-objective user association technique using particle swarm optimization (PSO) with the aim of jointly maximizing the throughput and the network balance index (NBI). By incorporating weight factors into the proposed scheme, the system operator has the flexibility to configure the priority levels of throughput and NBI. Numerical results demonstrate that our proposed multi-objective user association technique achieves better performance in terms of fitness values compared to the single-objective user association schemes.


Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5307 ◽  
Author(s):  
Shuang Zhang ◽  
Guixia Kang

To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.


2010 ◽  
Vol 29-32 ◽  
pp. 2496-2502
Author(s):  
Min Wang ◽  
Jun Tang

The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.


2017 ◽  
Vol E100.B (7) ◽  
pp. 1145-1151 ◽  
Author(s):  
Runze WU ◽  
Jiajia ZHU ◽  
Liangrui TANG ◽  
Chen XU ◽  
Xin WU

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