Abstract

The demand for mobile data is likely to grow at a pace more than envisaged in the coming years. Further, as applications such as the Internet of Things (IoT) come to fruition, there will be increased diversity in the types of devices demanding Internet connectivity and their requirements. Significant increase in data rate requirements is also expected due to services such as Ultra High Definition (UHD) video streaming and cloud computing. To meet all these demands, physical layer waveform candidates for future generations of communications need to be robust and inherently capable of extending into multiple domains (space, time, frequency, users, transmission media, code etc.) to ensure efficient utilization of resources. Multiple domains can be innately integrated into the design process of modulation schemes by using tensors, which are multi-way arrays. This paper introduces a unified tensor framework, providing a foundation for multi-domain communication systems that can be used to represent, design and analyse schemes that span several domains. Transmitted signals are represented by $N$ th order time function tensors which are coupled, using a system tensor of order $N+M$ , with the received signals which are represented by another tensor of order $M$ through the contracted convolution. We begin with the continuous time representation of the tensor system model and present both the strict multi-domain generalization of the Nyquist criterion for zero interference (inter-tensor and intra-tensor interference) as well as a relaxation. We present an equivalent discrete time system model, and as an example of using the tensor framework we derive tensor based linear equalization methods to combat multi-domain interference. An application to multi-user MIMO-GFDM illustrates the utility of this novel framework for derivation of joint domain signal processing techniques.

Highlights

  • W IRELESS communications and the Internet have been two of the most disruptive technologies in recent history and the synergetic relationship between them has led to exponential demand for mobile communication services

  • Comparing (62) with (53) we can see that it is a special case of the generalized Nyquist criterion where the overall system tensor is of size M × M with components Hi1,i2 (t) = pi2 (t) ∗ b(t) ∗ ri1 (t) and the data tensor is of order one with components d[n] = an

  • The transmitted signals are represented by Nth order time function tensors which are coupled, using a system tensor of order N + M, with the received signals which are represented by another signal tensor of order M through the contracted convolution

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Summary

INTRODUCTION

W IRELESS communications and the Internet have been two of the most disruptive technologies in recent history and the synergetic relationship between them has led to exponential demand for mobile communication services. Such strong constraints are not required for a tensor to offer a unique decomposition due to the use of higher dimensions [27], [28] This is one of the reasons for the gain in popularity of tensor based approached in wireless communications over recent years. A blind receiver that uses tensor decompositions for SIMO and MIMO OFDM systems is presented in [31]. Two constrained tensor models dubbed the PARATUCK-(N1, N) and Tucker-(N1, N) are introduced in [37], which are used to derive semi-blind receivers for MIMO OFDM-CDMA systems. Using this framework, we present the foundations for a multi-domain communication system that can be used to represent, design and analyse future wireless or wired communication systems. In Appendix A, we present the proof for a tensor Cauchy Schwartz inequality and Appendix B contains the proof of Theorem 2

BASICS OF TENSORS WITH APPLICATIONS TO SIGNALS AND SYSTEMS
TENSOR MODEL
SYSTEMS MODELED USING THE TENSOR FRAMEWORK
GENERALIZED FREQUENCY DIVISION
LINEAR EQUALIZATION
MIMO GFDM
CONCLUSION
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