Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
Abstract
:1. Introduction
- Propose a general tensor framework using the Einstein product to model multi-domain communication systems with examples.
- Develop tensor partial response signaling for addition of controlled inter- and intra-tensor domain interference for the purpose of spectral and cross-spectral shaping.
- Reveal the trade-off between multiple domains and develop a multi-domain beamforming approach through an information theoretic analysis of the tensor channel.
- Provide a review of some related tensor applications in communications found in literature to further motivate the tensor perspective.
2. Tensor System Model for Multi-Domain Communication Systems
2.1. A Generic System Model
2.2. Examples of Practical Systems
3. Simultaneous Signal Processing Across Domains
3.1. Tensor Partial Response Signaling
3.2. Tensor-Based Receiver Designs
3.2.1. Complexity of Tensor Multi-Linear MMSE Receiver
3.2.2. Application of Tensor Train Decomposition and Tensor Networks
3.2.3. Applications of Other Tensor Decompositions
4. Harnessing the Domain Trade-Off
4.1. Capacity of a Tensor Channel
4.2. Numerical Examples
4.3. Multi-Domain Beamforming (Precoding)
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Notation | Explanation |
---|---|
Order N tensor of size with complex entries. | |
and | Matrix of size and vector of size , respectively. |
Individual entries of tensor denoted using indices in subscript. | |
Colon in subscript indicates all the elements of a mode (first mode in this case) corresponding to fixed other modes ( in this case). | |
Sequence of order N tensors where each element is a function of the discrete variable k. | |
Function tensor where each element of an order N tensor is a function of continuous variable t. |
Step | Complexity |
---|---|
Find | . |
Find | . |
Find | . |
Find | . |
Tensor Tool | Example of Applications |
---|---|
PARAFAC (CP) | Model received signal in DS-CDMA and develop blind receiver methods [11] |
Tucker Decomposition | Data mining, Computer Vision, finding low rank structures in high dimensional data [32] |
PARATUCK | Semi blind receivers for joint channel estimation and data detection in MIMO OFDM CDMA systems [12] |
Tensor Train Decomposition | Reducing storage complexity in Big Data applications [32], space–time coding for MIMO OFDM relay systems [40] |
Tensor Inversion | Joint multi-domain equalization in systems such as MIMO GFDM [9] |
Tensor EVD using Einstein product | Multi-linear system theory [22], finding tensor channel capacity [25] |
Block Constrained PARAFAC | Blind multi-user detection and equalization for over-sampled, DS CDMA and OFDM systems [41] |
PARAFAC with Linear Dependencies (PARALIND) | Blind receiver for MIMO OFDM in the presence of carrier frequency offset [42] |
Short Biography of Authors
Divyanshu Pandey received the B.Tech degree in Communication and Computer Engineering from the LNM Institute of Information Technology, Jaipur, India, in 2011 and the M.S. degree in Electrical Engineering from University of Minnesota, Twin Cities, USA, in 2014. He worked as a Systems Engineer in WLAN PHY R&D team at Marvell Semiconductors Inc., Santa Clara, CA, USA from February 2015 to August 2017. He is currently pursuing the Ph.D. degree in Electrical Engineering from McGill University, Montreal, QC, Canada. His research interests include Information Theory, Wireless Communications, and Tensor Algebra with applications to communications and signal processing. | |
Adithya Venugopal received the B.Tech degree in Electronics and Communications Engineering from the Manipal Institute of Technology, India, in 2016. He worked as a Network Engineer at Cisco Systems Inc., Bangalore, India from Jan 2016 to Aug 2016 and received the M.S. degree in Electrical and Computer Engineering from McGill University, Montreal, QC, Canada in 2019. He is currently working with Fortinet Inc., Burnaby, BC, Canada in Network Security. His research interests include Digital Communications, Networking and Tensor Algebra with applications to communications and signal processing. | |
Harry Leib received the B.Sc. and M.Sc. degrees from the Technion—Israel Institute of Technology in 1977 and 1984. In 1987 he received the Ph.D. degree from the University of Toronto, Canada. After completing his Ph.D. studies, he was with the University of Toronto as a Post-doctoral Research Associate and as an Assistant Professor. Since 1989 he has been with the Department of Electrical and Computer Engineering at McGill University, where he is now a Full Professor teaching courses on Communication Systems, Information Theory, Detection, Estimation, and Probability Theory. His current research activities are in Digital Communications, Wireless Communication Systems, Global Navigation Satellite Systems, Statistical Signal Processing, and Information Theory. |
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Pandey, D.; Venugopal, A.; Leib, H. Multi-Domain Communication Systems and Networks: A Tensor-Based Approach. Network 2021, 1, 50-74. https://doi.org/10.3390/network1020005
Pandey D, Venugopal A, Leib H. Multi-Domain Communication Systems and Networks: A Tensor-Based Approach. Network. 2021; 1(2):50-74. https://doi.org/10.3390/network1020005
Chicago/Turabian StylePandey, Divyanshu, Adithya Venugopal, and Harry Leib. 2021. "Multi-Domain Communication Systems and Networks: A Tensor-Based Approach" Network 1, no. 2: 50-74. https://doi.org/10.3390/network1020005
APA StylePandey, D., Venugopal, A., & Leib, H. (2021). Multi-Domain Communication Systems and Networks: A Tensor-Based Approach. Network, 1(2), 50-74. https://doi.org/10.3390/network1020005