111 research outputs found
Relative Entropy-Based Waveform Optimization for Rician Target Detection with Dual-Function Radar Communication Systems
In this paper, we consider waveform design for dualfunction
radar-communication systems based on multiple-inputmultiple-out arrays. To
achieve better Rician target detection performance, we use the relative entropy
associated with the formulated detection problem as the design metric. We also
impose a multiuser interference energy constraint on the waveforms to ensure
the achievable sum-rate of the communications. Two algorithms are presented to
tackle the nonlinear non-convex waveform design problem. In the first
algorithm, we derive a quadratic function to minorize the objective function.
To tackle the quadratically constrained quadratic programming problem at each
iteration, a semidefinite relaxation approach followed by a rank-one
decomposition procedure and an efficient alternating direction method of
multipliers (ADMM) are proposed, respectively. In the second algorithm, we
present a novel ADMM algorithm to tackle the optimization problem and employ an
efficient minorization-maximization approach in the inner loop of the ADMM
algorithm. Numerical results demonstrate the superiority of both algorithms.
Moreover, the presented algorithms can be extended to synthesize
peak-to-average-power ratio constrained waveforms, which allows the radio
frequency amplifier to operate at an increased efficiency
Multi-Spectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing
This paper focuses on the joint design of transmit waveforms and receive
filters for airborne multiple-input-multiple-output (MIMO) radar systems in
spectrally crowded environments. The purpose is to maximize the output
signal-to-interference-plus-noise-ratio (SINR) in the presence of
signal-dependent clutter. To improve the practicability of the radar waveforms,
both a multi-spectral constraint and a peak-to-average-power ratio (PAPR)
constraint are imposed. A cyclic method is derived to iteratively optimize the
transmit waveforms and receive filters. In particular, to tackle the
encountered non-convex constrained fractional programming in designing the
waveforms (for fixed filters), we resort to the Dinkelbach's transform,
minorization-maximization (MM), and leverage the alternating direction method
of multipliers (ADMM). We highlight that the proposed algorithm can iterate
from an infeasible initial point and the waveforms at convergence not only
satisfy the stringent constraints, but also attain superior performance
Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks
Maneuvering target tracking will be an important service of future wireless
networks to assist innovative applications such as intelligent transportation.
However, tracking maneuvering targets by cellular networks faces many
challenges. For example, the dense network and high-speed targets make the
selection of the sensing nodes (SNs), e.g., base stations, and the associated
power allocation very difficult, given the stringent latency requirement of
sensing applications. Existing methods have demonstrated engaging tracking
performance, but with very high computational complexity. In this paper, we
propose a model-driven deep learning approach for SN selection to meet the
latency requirement. To this end, we first propose an iterative SN selection
method by jointly exploiting the majorization-minimization (MM) framework and
the alternating direction method of multipliers (ADMM). Then, we unfold the
iterative algorithm as a deep neural network (DNN) and prove its convergence.
The proposed model-driven method has a low computational complexity, because
the number of layers is less than the number of iterations required by the
original algorithm, and each layer only involves simple matrix-vector
additions/multiplications. Finally, we propose an efficient power allocation
method based on fixed point (FP) water filling (WF) and solve the joint SN
selection and power allocation problem under the alternative optimization
framework. Simulation results show that the proposed method achieves better
performance than the conventional optimization-based methods with much lower
computational complexity
Sparse Array Design for Dual-Function Radar-Communications System
The problem of sparse array design for dual-function radar-communications is
investigated. Our goal is to design a sparse array which can simultaneously
shape desired beam responses and serve multiple downlink users with the
required signal-to-interference-plus-noise ratio levels. Besides, we also take
into account the limitation of the radiated power by each antenna. The problem
is formulated as a quadratically constrained quadratic program with a
joint-sparsity-promoting regularization, which is NP-hard. The resulting
problem is solved by the consensus alternating direction method of multipliers,
which enjoys parallel implementation. Numerical simulations exhibit the
effectiveness and superiority of the proposed method which leads to a more
power-efficient solution.Comment: Accepted by IEEE Communications Letter
Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing. In this paper, we deal with obtaining sparse solution to near- field radar imaging problems by developing efficient sparse reconstruction, which avoid storing and using large-scale sensing matrices. We demonstrate that the "fast multipole method" can be employed within sparse reconstruction algorithms to efficiently compute the sensing operator and its adjoint (backward) operator, hence improving the computation speed and memory usage, especially for large-scale 3-D imaging problems. For several near-field imaging scenarios including point scatterers and 2-D/3-D extended targets, the performances of sparse reconstruction algorithms are numerically tested in comparison with a classical solver. Furthermore, effectiveness of the fast multipole method and efficient reconstruction are illustrated in terms of memory requirement and processing time
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