多接收机自由扩散分子通信信道建模

doi:10.11959/j.issn.1000-436x.2021151

Abstract

Abstract:

A coexistence scenario with a point source, a pair of absorbing and transparent receiver was considered, an interference factor was introduced in the proposed channel model based on the receiving molecular probability in the transparent receiver considering the influences of the absorbing receiver on the transparent one.Furthermore, the channel model of point source and transparent receiver had been proposed by using Levenberg-Marquardt algorithm combined with artificial neural network to study and predict channel model parameters.The simulation results not only verify the effectiveness of the proposed channel model, but also show that the peak time of any point in the environment is directly proportional to the square of the distance from the point source to the receiver, and inversely proportional to the molecular diffusion coefficient, and the peak time is not affected by the absorbing receiver in the environment.

Key words: molecular communication, free diffusion, multi receiver, channel model

CLC Number:

TN92

Zhuo SUN, Xu BAO, Jie LIN, Wence ZHANG. Channel modeling of molecular communication via free diffusion with multiple receiver[J]. Journal on Communications, 2021, 42(7): 107-116.

Figures/Tables 15

References 28

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参数	取值
点源S发射的总分子数	20 000
扩散系数D/(μm²·s^-1)	100
吸收机R₁的半径r₁/μm	2
透明机R₂ 的体积V₂/μm³	1
仿真步长Δt /s	0.001
仿真总时长/s	2
吸收机的吸收概率P_react	1
仿真次数	111

d₂/μm	α	k₁	k₂	R-square	SSE	RMSE
3	60°	7.015 7	0.038 2	0.999 6	4.018 6×10^-6	6.403 7×10^-6
4	150°	2.868 8	0.023 8	0.999 9	2.781 1×10^-7	1.684 6×10^-6

训练算法	全称	特点
BFG	BFGS quasi-Newton algorithm	对于较小的网络是一种有效的训练算法，对于非常大的网络效率并不高
BR	Bayesian regularization	使用此函数训练的网络必须使用MSE或SSE性能函数
CGB	conjugate gradient with Powell-beale restart	据Powell-Beale重启的共轭梯度反向传播来更新权重和偏差值的网络训练函数
CGF	conjugate gradient backpropagation with Fletcher-Reeves update	共轭梯度法中存储量要求最小的算法，但泛化能力较差
CGP	conjugate gradient backpropagation with Polak-Ribiére updates	要求的存储量大，但收敛速度快
LM	Levenberg-Marquardt	避免了直接计算Hessian矩阵，从而减少了训练中的计算量，可以更快地完成计算，但是需要较大内存，而且对于大型矩阵收敛速度慢
OSS	one-step secant backpropagation	比 BFG 算法占用更少的存储空间和计算量。与共轭梯度算法相比，它每完成一次训练都需要更多的存储空间和计算量
RP	resilient backpropagation	可以在不降低网络设计准确性的前提下，提供更快的权重和偏差的局部自适应。运行时需要存储每个权重和偏差的更新值，会占用较多的存储空间
SCG	scaled conjugate gradient	将模值信赖域算法与共轭梯度算法结合起来，减少了用于调整方向时搜索网络的时间

训练算法	R^-square	SSE	RMSE
BFG	9.037 4×10^-1	4.102 4×10²	7.261 5×10^-1
BR	7.708 2×10^-1	3.804 8×10³	2.211 4
CGB	4.286 3×10^-1	9.486 1×10³	3.491 8
CGF	8.087 6×10^-1	2.152 6×10³	1.663 4
CGP	5.853 7×10^-1	3.729 8×10³	2.189 5
LM	9.759 9×10^-1	1.542 7×10³	1.408 2
OSS	6.886 6×10^-1	4.152 7×10³	2.310 3
RP	7.565 6×10^-1	3.729 3×10³	2.189 4
SCG	7.772 5×10^-1	2.310 1×10³	1.723 2

Channel modeling of molecular communication via free diffusion with multiple receiver

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