95 research outputs found
Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design
We focus on a dual-functional multi-input-multi-output (MIMO)
radar-communication (RadCom) system, where a single transmitter communicates
with downlink cellular users and detects radar targets simultaneously. Several
design criteria are considered for minimizing the downlink multi-user
interference. First, we consider both the omnidirectional and directional
beampattern design problems, where the closed-form globally optimal solutions
are obtained. Based on these waveforms, we further consider a weighted
optimization to enable a flexible trade-off between radar and communications
performance and introduce a low-complexity algorithm. The computational costs
of the above three designs are shown to be similar to the conventional
zero-forcing (ZF) precoding. Moreover, to address the more practical constant
modulus waveform design problem, we propose a branch-and-bound algorithm that
obtains a globally optimal solution and derive its worst-case complexity as a
function of the maximum iteration number. Finally, we assess the effectiveness
of the proposed waveform design approaches by numerical results.Comment: 13 pages, 10 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Coexistence Designs of Radar and Communication Systems in a Multi-path Scenario
The focus of this study is on the spectrum sharing between multiple-input
multiple-output (MIMO) communications and co-located MIMO radar systems in
multi-path environments. The major challenge is to suppress the mutual
interference between the two systems while combining the useful multi-path
components received at each system. We tackle this challenge by jointly
designing the communication precoder, radar transmit waveform and receive
filter. Specifically, the signal-to-interference-plus-noise ratio (SINR) at the
radar receiver is maximized subject to constraints on the radar waveform,
communication rate and transmit power. The multi-path propagation complicates
the expressions of the radar SINR and communication rate, leading to a
non-convex problem. To solve it, a sub-optimal algorithm based on the
alternating maximization is used to optimize the precoder, radar transmit
waveform and receive filter iteratively. Simulation results are provided to
demonstrate the effectiveness of the proposed design
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
Joint Design of Overlaid Communication Systems and Pulsed Radars
The focus of this paper is on co-existence between a communication system and
a pulsed radar sharing the same bandwidth. Based on the fact that the
interference generated by the radar onto the communication receiver is
intermittent and depends on the density of scattering objects (such as, e.g.,
targets), we first show that the communication system is equivalent to a set of
independent parallel channels, whereby pre-coding on each channel can be
introduced as a new degree of freedom. We introduce a new figure of merit,
named the {\em compound rate}, which is a convex combination of rates with and
without interference, to be optimized under constraints concerning the
signal-to-interference-plus-noise ratio (including {\em signal-dependent}
interference due to clutter) experienced by the radar and obviously the powers
emitted by the two systems: the degrees of freedom are the radar waveform and
the afore-mentioned encoding matrix for the communication symbols. We provide
closed-form solutions for the optimum transmit policies for both systems under
two basic models for the scattering produced by the radar onto the
communication receiver, and account for possible correlation of the
signal-independent fraction of the interference impinging on the radar. We also
discuss the region of the achievable communication rates with and without
interference. A thorough performance assessment shows the potentials and the
limitations of the proposed co-existing architecture
OFDM Waveform Optimisation for Joint Communications and Sensing
Radar systems are radios to sense objects in their surrounding environment. These operate at a defined set of frequency ranges. Communication systems are used to transfer information between two points. In the present day, proliferation of mobile devices and the advancement of technology have led to communication systems being ubiquitous. This has made these systems to operate at the frequency bands already used by the radar systems. Thus, the communication signal interferes a radar receiver and vice versa, degrading performance of both systems. Different methods have been proposed to combat this phenomenon. One of the novel topics in this is the RF convergence, where a given bandwidth is used jointly by both systems. A differentiation criterion must be adopted between the two systems so that a receiver is able to separately extract radar and communication signals. The hardware convergence due to the emergence of software-defined radios also motivated a single system be used for both radar and communication.
A joint waveform is adopted for both radar and communication systems, as the transmit signal. As orthogonal frequency-division multiplexing (OFDM) waveform is the most prominent in mobile communications, it is selected as the joint waveform. Considering practical cellular communication systems adopting OFDM, there often exist unused subcarriers within OFDM symbols. These can be filled up with arbitrary data to improve the performance of the radar system. This is the approach used, where the filling up is performed through an optimisation algorithm. The filled subcarriers are termed as radar subcarriers while the rest as communication subcarriers, throughout the thesis.
The optimisation problem minimises the Cramer--Rao lower bounds of the delay and Doppler estimates made by the radar system subject to a set of constraints. It also outputs the indices of the radar and communication subcarriers within an OFDM symbol, which minimise the lower bounds. The first constraint allocates power between radar and communication subcarriers depending on their subcarrier ratio in an OFDM symbol. The second constraint ensures the peak-to-average power ratio (PAPR) of the joint waveform has an acceptable level of PAPR.
The results show that the optimised waveform provides significant improvement in the Cramer--Rao lower bounds compared with the unoptimised waveform. In compensation for this, the power allocated to the communication subcarriers needs to be reduced. Thus, improving the performances of the radar and communication systems are a trade-off. It is also observed that for the minimum lower bounds, radar subcarriers need to be placed at the two edges of an OFDM symbol. Optimisation is also seen to improve the estimation performance of a maximum likelihood estimator, concluding that optimising the subcarriers to minimise a theoretical bound enables to achieve improvement for practical systems
Complexity Reduction in Beamforming of Uniform Array Antennas for MIMO Radars
Covariance matrix design and beamforming in
multiple-input multiple-output (MIMO) radar systems have
always been a time-consuming task with a substantial number of unknown variables in the optimization problem to be solved. Based on the radar and target conditions, beamforming can be a dynamic process and in real-time scenarios, it is critical to have a fast beamforming. In this paper, we propose a beampattern matching design technique that is much faster compared to the well-known traditional semidefinite quadratic programming (SQP) counterpart. We show how to calculate the covariance matrix of the probing transmitted signal to obtain the MIMO radar desired beampattern, using a facilitator library. While the proposed technique inherently satisfies the required practical constraints in covariance matrix design, it significantly reduces the number of unknown variables used in the minimum square error (MSE) optimization problem, and therefore reduces the computational complexity considerably. Simulation results show the superiority of the proposed technique in terms of complexity and speed, compared with existing methods. This superiority is enhanced by increasing the number of antennas
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