Performance Analysis of D-MoSK Modulation in Mobile Diffusive-Drift Molecular Communications

Molecular communication (MC), in which molecules serve as the carrier for data transmission, plays an essential role in nanonetworks. In this article, a mobile diffusive-drift MC model is investigated, which consists of a mobile transmit nanomachine (TN) and a mobile receive nanomachine (RN). The depleted molecule shift keying (D-MoSK) modulation is utilized in this model to perform end-to-end communication. To explore the performance of D-MoSK, we derive the closed-form expressions of symbol error rate (SER) as well as the channel capacity, and then we give out the numerical results. It is observed from the numerical results that, if compared with the molecule shift keying modulation, the D-MoSK modulation can exhibit better performances in terms of SER, channel capacity, and complexity under the employed model. Also, the impacts of several crucial parameters on the performance are evaluated and discussed comprehensively. The obtained results are expected to provide guidance significance for the design of a practical mobile diffusive-drift MC system.

[1]  Sugata Sanyal,et al.  Survey of Security and Privacy Issues of Internet of Things , 2015, ArXiv.

[2]  Mugen Peng,et al.  Performance Analysis of Reversible Binding Receptor Based Decode-and-Forward Relay in Molecular Communication Systems , 2018, IEEE Wireless Communications Letters.

[3]  Mugen Peng,et al.  Diffusion based molecular communication: principle, key technologies, and challenges , 2017, China Communications.

[4]  A. Vasilakos,et al.  Molecular Communication and Networking: Opportunities and Challenges , 2012, IEEE Transactions on NanoBioscience.

[5]  Vahid Jamali,et al.  Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems , 2017, IEEE Transactions on Communications.

[6]  Yi Lu,et al.  Comparison of Channel Coding Schemes for Molecular Communications Systems , 2015, IEEE Transactions on Communications.

[7]  Neeraj Varshney,et al.  On Flow-Induced Diffusive Mobile Molecular Communication: First Hitting Time and Performance Analysis , 2018, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[8]  Siyi Wang,et al.  Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications , 2016, IEEE Transactions on NanoBioscience.

[9]  Özgür B. Akan,et al.  Body area nanonetworks with molecular communications in nanomedicine , 2012, IEEE Communications Magazine.

[10]  Mugen Peng,et al.  Fog-computing-based radio access networks: issues and challenges , 2015, IEEE Network.

[11]  Pramod K. Varshney,et al.  Impact of Intermediate Nanomachines in Multiple Cooperative Nanomachine-Assisted Diffusion Advection Mobile Molecular Communication , 2019, IEEE Transactions on Communications.

[12]  Md. Humaun Kabir,et al.  D-MoSK Modulation in Molecular Communications , 2015, IEEE Transactions on NanoBioscience.

[13]  Neeraj Varshney,et al.  Optimal Transmitted Molecules and Decision Threshold for Drift-Induced Diffusive Molecular Channel With Mobile Nanomachines , 2019, IEEE Transactions on NanoBioscience.

[14]  Takahiro Hara,et al.  Performance Evaluation of Leader–Follower-Based Mobile Molecular Communication Networks for Target Detection Applications , 2017, IEEE Transactions on Communications.

[15]  Weisi Guo,et al.  Experimental Nakagami distributed noise model for molecular communication channels with no drift , 2015 .

[16]  Mugen Peng,et al.  Recent Advances of Edge Cache in Radio Access Networks for Internet of Things: Techniques, Performances, and Challenges , 2019, IEEE Internet of Things Journal.

[17]  Mahtab Mirmohseni,et al.  Type-Based Sign Modulation and Its Application for ISI Mitigation in Molecular Communication , 2016, IEEE Transactions on Communications.

[18]  Mugen Peng,et al.  Recent Advances in Fog Radio Access Networks: Performance Analysis and Radio Resource Allocation , 2016, IEEE Access.

[19]  Ming Xia,et al.  Performance Analysis of Diffusive Mobile Multiuser Molecular Communication With Drift , 2018, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[20]  Dogu Arifler,et al.  Connectivity Properties of Free Diffusion-Based Molecular Nanoscale Communication Networks , 2017, IEEE Transactions on Communications.

[21]  H. Birkan Yilmaz,et al.  ISI-Aware Channel Code Design for Molecular Communication via Diffusion , 2019, IEEE Transactions on NanoBioscience.

[22]  Andrew W. Eckford,et al.  A Comprehensive Survey of Recent Advancements in Molecular Communication , 2014, IEEE Communications Surveys & Tutorials.

[23]  Mugen Peng,et al.  Network Slicing in Fog Radio Access Networks: Issues and Challenges , 2017, IEEE Communications Magazine.

[24]  Hao Yan,et al.  Adaptive Detection and ISI Mitigation for Mobile Molecular Communication , 2018, IEEE Transactions on NanoBioscience.

[25]  Y. Koucheryavy,et al.  The internet of Bio-Nano things , 2015, IEEE Communications Magazine.

[26]  A. Vasilakos,et al.  Molecular Communication Among Biological Nanomachines: A Layered Architecture and Research Issues , 2014, IEEE Transactions on NanoBioscience.

[27]  Tadashi Nakano,et al.  Graph-Based Modeling of Mobile Molecular Communication Systems , 2018, IEEE Communications Letters.

[28]  Werner Haselmayr,et al.  Transposition Errors in Diffusion-Based Mobile Molecular Communication , 2017, IEEE Communications Letters.

[29]  Paeiz Azmi,et al.  Performance Evaluation and Optimal Detection of Relay-Assisted Diffusion-Based Molecular Communication With Drift. , 2017, IEEE transactions on nanobioscience.

[30]  Murat Kuscu,et al.  Fundamentals of Molecular Information and Communication Science , 2017, Proceedings of the IEEE.