• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.487-497
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• 2010

In this paper, we propose a hard handover scheme which adaptively decides important handover parameters such as hysteresis and time-to-trigger values considering the load difference between the target and serving cells. First of all, the hysteresis value can be automatically adjusted according to the load difference, thus it is used to decide the handover trigger time. As a result, through the adaptive hysteresis scheme, handover drop rate is improved. However, this adaptive hysteresis scheme has a problem that the ping-pong effect, which occurs due to the frequent movement of mobile stations at the cell boundary, is increased. Therefore, to solve this problem, we propose a novel adaptive time-to-trigger scheme with the time-to-trigger which is in inverse proportion to the hysteresis value already established by the adaptive hysteresis scheme which adapts to the changing load difference between the target and serving cells. The simulation results show that the proposed adaptive time-to-trigger scheme based on the adaptive hysteresis is better than existing schemes in terms of handover drop rate and ping-pong generation.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.163-170
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• 2007

The next generation communication environment consists of various wireless access networks with distinct features that are configured as an overlay topology. In the network environments, the frequency of hand overs should be minimized and the error propagation should be solved in order to provide high-quality multimedia services to mobile users. Therefore, we propose an performance enhancement approach, based on mSCTP, that provides high quality multimedia services to mobile users by ameliorating the error propagation problem. We utilizes the following four functions: 1) the separation of transmission paths according to the types of frames. 2) retransmission strategy to minimize the loss rate of frames, 3) Foced vertical handover execution by utilizing bicasting, 4) using the stability period in order to reduce the effect of the ping pong phenomenon. The simulation results show that the proposed approach provides seamless multimedia service to mobile users by achieving error resilience.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.