Deep Learning based Symbol Detection for Molecular Communications

被引：4
作者：
Sharma, Sanjeev [1 ]
Dixit, Dharmendra [2 ]
Deka, Kuntal [3 ]
机构：
[1] IIT BHU, EC, Varanasi, Uttar Pradesh, India
[2] REC Sonbhadra, ECE, Churk, India
[3] IIT Goa, ES, Ponda 403401, Goa, India
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATIONS SYSTEMS (IEEE ANTS) | 2020年
关键词：
Molecular communication; deep learning; inter-symbol interference; diffusion process; biological networks; RECEIVER;
D O I：
10.1109/ANTS50601.2020.9342782
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
Molecular communication (MC) can play an indispensable role in nanonetworks and Internet of Bio-nano Things based applications. However, inter-symbol interference (ISI), due to slow diffusion of molecules can severely degrade system's performance. In this paper, we propose a deep learning (DL)based receiver design to decode the data symbols in MC. The proposed DL-based receiver (DLR) does not require the channel state information and threshold value(s) implicitly to decode the data symbols. The DLR is trained offline by applying the data symbols generated from simulation based on diffusion channel statistics, then it is used for recovering the online transmitted data symbols directly. Impact of various system parameters such as diffusion coefficient, noise and ISI level, and frame duration are analyzed for DLR. DLR's performance is also compared to conventional detection methods. Results show that DLR can be a viable and practical choice in MC system design.
引用
收藏
页数：6
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