towards dual-functional radar- communication systems

Towards Dual-functional Radar-

Communication Systems:

Optimal Waveform Design

1. Introduction

2. System Model

3. Closed-form Waveform Designs

4. Trade-off Between Radar and Comms

5. Constant-modulus Waveform Design

6. Numerical Results

1. Introduction

3

Civilian Motivation

Explosive growth in the number of connected devices and increasing demand for wireless spectrum

1. Introduction

4

Military Motivation

Integrating multiple RF functions on the airborne/shipborne platforms, e.g., radar, data-link and electronic warfare systems

1. Introduction

5

Dual-functional RadCom

- Sharing both the spectrum and the hardware platform between the two systems;

- Supporting simultaneous target detection and wireless communication;

1. Introduction

2. System Model





2. System Model

7

General scenario: Allocate information and power in both LoS and NLoS channels

Ø Radar Model

§ Beampattern

§ Sample Covariance Matrix

Ø Communication Model

§ Received Signal

§ Average SINR

§ Achievable Sum-rate

1. Introduction

2. System Model






9

Omnidirectional Waveform Design2 minimize the multi-user interference (MUI) for commmin

1. . orthogonal waveform con

s

straint for MIMO radar

F

H TPs tL N

X

HX S

XX I

The globally optimal solution of the above Orthogonal Procrustes Problem (OPP) is given by

where is the SVD.

least-squares (LS) problem on the complex Stiefel manifold


10

Directional Waveform Design2 minimize the multi-user interference (MUI) for commmin

1. . directional waveform const

s

raint for MIMO radar

F

dHs t

L

X

HX S

X RX

The globally optimal solution of the above problem is given by

where is the SVD,

is the Cholesky decomposition (or other valid square-roots).

1. Introduction

2. System Model


4. Trade-off Between Radar and Communication




12

Total Power Constrained Design

2 20

2

min 1

1. .

F F

TFs t P

L

XHX S X X

X

Strong duality holds for the above Trust-region subproblem (TRS), which can be optimally solved by solving the KKT equations below:

2

, 2 2 0, Lagrangian multiplier

, Primal feasiblity

0 Dual feasiblity

opt opt opt N opt

opt TF

opt N

LP

X Q I X G

X

Q I

L

±

where


13

Low-complexity Algorithm

1. Obtain the minimum point of the following 1-demensional convex function via golden-section search.

where is the eigenvalue decomposition.

2. Obtain the designed dual-functional waveform matrix by


14

Per-antenna Power Constrained Design

An LS problem on the complex oblique manifold


15

Objective Function on the Oblique Manifold

§ Euclidean Gradient

§ Riemannian Gradient

§ Tangent Space

§ Retraction

§ Inner Product


16

Riemannian Conjugate Gradient (RCG) Algorithm


17

Complexity

1. Introduction

2. System Model






19

Problem Formulation

where

2

0

,

min communication MUI

. vec , radar waveform similarity constraint (SC)

, , , constant-modulus constraint (CMC)

F

Ti j

s t

Px i jN

XHX S

X X


20

Branch-and-bound Algorithm

The BnB algorithm yields the globally optimal solution for the nonconvex CM waveform design problem efficiently


21

Lower-bound Acqusition:Convex Relaxation

Upper-bound Acqusition: Feasible Solution


22

Accelerated Gradient Projection for QP-LB


23

Gradient Projection for QP-UB

11PR 2k Hs x v H Hv s

1. Introduction

2. System Model






25

Waveform Designs for Given Radar Beampatterns

- The first four designs can realize the dual-functions for both radar and comms

- The comms performance can be considerably improved by allowing slight performance-loss at radar


26

Performance Trade-off Between Radar and Comms

- The performance trade-off can be explicitly shown by using weighted optimizations

- The performance for both radar and comms becomes worse with increased UEs


27

Constant-modulus Waveform Design

In each iteration, the LB rises while the UB decreases. The optimal solution can be obtained within tens of iterations.


28


- The proposed BnB algorithm significantly outperforms the conventional SQR-BS method

- The performance of the BnB is very close to the convex relaxation bound


29


Radar pulse compression p e r f o r m a n c e i s g u a r a n t e e d b y B n B , which is exactly the same as the conventional SQR-BS method

Summary

30

§ We propose dual-functional waveform design approaches for both omnidirectional and directional radar beampatterns, and derive the closed-form solutions;

§ We propose weighted optimizations that achieve a flexible trade-off between the radar and communication performance under both total and per-antenna power constraints, and solve the problems with low-complexity algorithms;

§ We consider the waveform design with CMC and SC constraints, and develop a branch-and-bound algorithm to obtain the globally optimal solutions, which outperforms the conventional SQR algorithm;

§ We derive the computational complexity for the proposed algorithms analytically.

References

31

[1] F. Liu, L. Zhou, C. Masouros, A. Li, W. Luo, and A. Petropulu, "Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design," to appear in IEEE Transactions on Signal Processing.

[2] F. Liu, C. Masouros, A. Li, T. Ratnarajah, and J. Zhou, "MIMO Radar and Cellular Coexistence: A Power-Efficient Approach Enabled by Interference Exploitation," IEEE Transactions on Signal Processing, vol. 66, no. 14, pp. 3681-3695, 2018.

[3] F. Liu, C. Masouros, A. Li, H. Sun and L. Hanzo, "MU-MIMO Communications with MIMO Radar: From Coexistence to Joint Transmission," IEEE Transactions on Wireless Communications, vol. 17, no. 4, pp. 2755-2770, 2018.

[4] F. Liu, C. Masouros, P. V. Amadori and H. Sun, "An Efficient Manifold Algorithm for Constructive Interference Based Constant Envelope Precoding," IEEE Signal Processing Letters, vol. 24, no. 10, pp. 1542-1546, 2017.

[5] F. Liu, C. Masouros, A. Li and T. Ratnarajah, "Robust MIMO Beamforming for Cellular and Radar Coexistence," IEEE Wireless Communications Letters, vol. 6, no. 3, pp. 374-377, 2017.

[6] F. Liu, A. Garcia-Rodriguez, C. Masouros and G. Geraci, "Interfering Channel Estimation in Radar-Cellular Coexistence: How Much Information Do We Need?" submitted to IEEE Transactions on Wireless Communications.

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