towards dual-functional radar- communication systems
TRANSCRIPT
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
3. Closed-form Waveform Designs
4. Trade-off Between Radar and Comms
5. Constant-modulus Waveform Design
6. Numerical Results
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
3. Closed-form Waveform Designs
4. Trade-off Between Radar and Comms
5. Constant-modulus Waveform Design
6. Numerical Results
3. Closed-form Waveform Designs
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
3. Closed-form Waveform Designs
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
3. Closed-form Waveform Designs
4. Trade-off Between Radar and Communication
5. Constant-modulus Waveform Design
6. Numerical Results
4. Trade-off Between Radar and Comms
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
4. Trade-off Between Radar and Comms
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
4. Trade-off Between Radar and Comms
14
Per-antenna Power Constrained Design
An LS problem on the complex oblique manifold
4. Trade-off Between Radar and Comms
15
Objective Function on the Oblique Manifold
§ Euclidean Gradient
§ Riemannian Gradient
§ Tangent Space
§ Retraction
§ Inner Product
1. Introduction
2. System Model
3. Closed-form Waveform Designs
4. Trade-off Between Radar and Communication
5. Constant-modulus Waveform Design
6. Numerical Results
5. Constant-modulus Waveform Design
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
5. Constant-modulus Waveform Design
20
Branch-and-bound Algorithm
The BnB algorithm yields the globally optimal solution for the nonconvex CM waveform design problem efficiently
5. Constant-modulus Waveform Design
21
Lower-bound Acqusition:Convex Relaxation
Upper-bound Acqusition: Feasible Solution
1. Introduction
2. System Model
3. Closed-form Waveform Designs
4. Trade-off Between Radar and Communication
5. Constant-modulus Waveform Design
6. Numerical Results
6. Numerical Results
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
6. Numerical Results
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
6. Numerical Results
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.
6. Numerical Results
28
Constant-modulus Waveform Design
- The proposed BnB algorithm significantly outperforms the conventional SQR-BS method
- The performance of the BnB is very close to the convex relaxation bound
6. Numerical Results
29
Constant-modulus Waveform Design
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.