Li Zhang

Publications

1 Multi-stage Directional Median Filter

Authors: Zong Chen, Li Zhang

Abstract:

Median filter is widely used to remove impulse noise without blurring sharp edges. However, when noise level increased, or with thin edges, median filter may work poorly. This paper proposes a new filter, which will detect edges along four possible directions, and then replace noise corrupted pixel with estimated noise-free edge median value. Simulations show that the proposed multi-stage directional median filter can provide excellent performance of suppressing impulse noise in all situations.

Keywords: median filter, impulse noise, Multi-stage, Edgepreserving

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Abstracts

3 Hybrid Weighted Multiple Attribute Decision Making Handover Method for Heterogeneous Networks

Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

Abstract:

Small cell deployment in 5G networks is a promising technology to enhance capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn will result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers, and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision according to Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper, we propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method shows better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.

Keywords: weight, Interference, TOPSIS, handover, small cells, MADM, HetNets

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2 Handover for Dense Small Cells Heterogeneous Networks: A Power-Efficient Game Theoretical Approach

Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

Abstract:

In this paper, a non-cooperative game method is formulated where all players compete to transmit at higher power. Every base station represents a player in the game. The game is solved by obtaining the Nash equilibrium (NE) where the game converges to optimality. The proposed method, named Power Efficient Handover Game Theoretic (PEHO-GT) approach, aims to control the handover in dense small cell networks. Players optimize their payoff by adjusting the transmission power to improve the performance in terms of throughput, handover, power consumption and load balancing. To select the desired transmission power for a player, the payoff function considers the gain of increasing the transmission power. Then, the cell selection takes place by deploying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A game theoretical method is implemented for heterogeneous networks to validate the improvement obtained. Results reveal that the proposed method gives a throughput improvement while reducing the power consumption and minimizing the frequent handover.

Keywords: Game theory, Energy Efficiency, handover, small cells, HetNets

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1 Prediction of the Transmittance of Various Bended Angles Lightpipe by Using Neural Network under Different Sky Clearness Condition

Authors: Li Zhang, Yuehong Su

Abstract:

Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: Neural Network, transmittance, bended lightpipe, Photopia

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