Fast game-based handoff mechanism with load balancing for LTE/LTE-A heterogeneous networks

Tseng, Chien‐Cheng; Wang, Hwang-Cheng; Ting, Kuo-Chang; Kuo, Fang-Chang

doi:10.1016/j.jnca.2016.12.002

Cited by 10 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods, like ABC algorithm, 27 fast game‐based handoff, 44 VHO in 4G network, 32 fractional DE, and proposed FrSqDE‐based DBN, are taken for the analysis.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

Patil

2021

Int J Communication

View full text Add to dashboard Cite

The innovative characteristics of different heterogeneous wireless networks provide better service. The disparate wireless access networks consist of wireless metropolitan access network (WMAN) and cellular networks. Various techniques are devised for dynamic network selection, but these techniques concentrated on attaining improved performance. To address this issue, a novel method is devised to improve the energy efficiency of disparate heterogeneous wireless networks. Here, the proposed fractional squirrel-dolphin echolocation (FrSqDE) algorithm is devised by integrating squirrel search optimization (SSA) and fractional dolphin echolocation (fractional DE) algorithm. In addition, the fitness function is considered with certain factors that include delay, bit error rate (BER), jitter, packet loss, energy consumption, probability of hot spot, average bit rate (ABR), and received signal strength (RSS). The proposed FrSqDE optimization algorithm is used for training deep belief networks (DBNs) to select optimal weights. Here, the network selection factors are given as input to the proposed FrSqDE-based DBN, wherein optimal decision is made to handle vertical handoff. The proposed FrSqDE-based DBN offered improved performance with a minimal call drop probability of 0.050, minimal delay of 0.013 s, minimal energy consumption of 0.393 J, and maximal throughput of 117.972 kbps.

show abstract

“…The methods, like ABC algorithm, 27 fast game‐based handoff, 44 VHO in 4G network, 32 fractional DE, and proposed FrSqDE‐based DBN, are taken for the analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The methods, like ABC algorithm, 27 fast game-based handoff, 44 VHO in 4G network, 32 fractional DE, and proposed FrSqDE-based DBN, are taken for the analysis. Note: The proposed system offers better performance and that results have been provided as bold.…”

Section: Comparative Methodsmentioning

confidence: 99%

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

Patil

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Wireless Communications and Mobile Computing assisted the mobile users to selecting the proper candidate base station [14]. In [15], combining with the improved competitive auction technique, the quality of uplink and downlink, and load factor, and the fast game-based handover decision method was proposed. The stochastic geometry analysis method was proposed [16].…”

Section: Related Workmentioning

confidence: 99%

LB‐DDQN for Handover Decision in Satellite‐Terrestrial Integrated Networks

Huang

Yin

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.

show abstract

“…This technology promises to support 3 Gbps for fixed communication and also it will provide proper handoff between cells. In future LTE-A might be named as a 5 th generation network [14].…”

Section: Technology Deployed For 4gmentioning

confidence: 99%

Mobile Communication - Past, Present and Future: a Review

Pandi

Nargund²

2018

The Scientific Bulletin of Electrical Engineering Faculty

View full text Add to dashboard Cite

Communication is one of the most important things in human life, day by day the technology is changing, data rate becomes faster and equipment’s are also becoming compact, as the results of change in technology. The human beings have also adapted to the technology as well. In this article the revolution of wireless technologies like 2G, 3G, 4G and 5G (Future generation network) are explained. This article mainly focused on background of wireless communication system, models and technologies adapted for 4G and 5G. The key technologies, spectrum allocation and current projects are discussed and described; this work gives a detailed research to address the projects and developments for future generation wireless technology.

show abstract

Fast game-based handoff mechanism with load balancing for LTE/LTE-A heterogeneous networks

Cited by 10 publications

References 19 publications

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

LB‐DDQN for Handover Decision in Satellite‐Terrestrial Integrated Networks

Mobile Communication - Past, Present and Future: a Review

Contact Info

Product

Resources

About