63 research outputs found
Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar
We present the design and hardware implementation of a radar prototype that
demonstrates the principle of a sub-Nyquist collocated multiple-input
multiple-output (MIMO) radar. The setup allows sampling in both spatial and
spectral domains at rates much lower than dictated by the Nyquist sampling
theorem. Our prototype realizes an X-band MIMO radar that can be configured to
have a maximum of 8 transmit and 10 receive antenna elements. We use frequency
division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and
apply the Xampling framework for signal recovery. The prototype also implements
a cognitive transmission scheme where each transmit waveform is restricted to
those pre-determined subbands of the full signal bandwidth that the receiver
samples and processes. Real-time experiments show reasonable recovery
performance while operating as a 4x5 thinned random array wherein the combined
spatial and spectral sampling factor reduction is 87.5% of that of a filled
8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar,
Sonar and Remote Sensing (CoSeRa) 201
Measurement Matrix Design for Compressive Sensing Based MIMO Radar
In colocated multiple-input multiple-output (MIMO) radar using compressive
sensing (CS), a receive node compresses its received signal via a linear
transformation, referred to as measurement matrix. The samples are subsequently
forwarded to a fusion center, where an L1-optimization problem is formulated
and solved for target information. CS-based MIMO radar exploits the target
sparsity in the angle-Doppler-range space and thus achieves the high
localization performance of traditional MIMO radar but with many fewer
measurements. The measurement matrix is vital for CS recovery performance. This
paper considers the design of measurement matrices that achieve an optimality
criterion that depends on the coherence of the sensing matrix (CSM) and/or
signal-to-interference ratio (SIR). The first approach minimizes a performance
penalty that is a linear combination of CSM and the inverse SIR. The second one
imposes a structure on the measurement matrix and determines the parameters
involved so that the SIR is enhanced. Depending on the transmit waveforms, the
second approach can significantly improve SIR, while maintaining CSM comparable
to that of the Gaussian random measurement matrix (GRMM). Simulations indicate
that the proposed measurement matrices can improve detection accuracy as
compared to a GRMM
MIMO Systems with Reconfigurable Antennas: Joint Channel Estimation and Mode Selection
Reconfigurable antennas (RAs) are a promising technology to enhance the
capacity and coverage of wireless communication systems. However, RA systems
have two major challenges: (i) High computational complexity of mode selection,
and (ii) High overhead of channel estimation for all modes. In this paper, we
develop a low-complexity iterative mode selection algorithm for data
transmission in an RA-MIMO system. Furthermore, we study channel estimation of
an RA multi-user MIMO system. However, given the coherence time, it is
challenging to estimate channels of all modes. We propose a mode selection
scheme to select a subset of modes, train channels for the selected subset, and
predict channels for the remaining modes. In addition, we propose a prediction
scheme based on pattern correlation between modes. Representative simulation
results demonstrate the system's channel estimation error and achievable
sum-rate for various selected modes and different signal-to-noise ratios
(SNRs)
Joint Angle and Delay Cram\'{e}r-Rao Bound Optimization for Integrated Sensing and Communications
In this paper, we study a multi-input multi-output (MIMO) beamforming design
in an integrated sensing and communication (ISAC) system, in which an ISAC base
station (BS) is used to communicate with multiple downlink users and
simultaneously the communication signals are reused for sensing multiple
targets. Our interested sensing parameters are the angle and delay information
of the targets, which can be used to locate these targets. Under this
consideration, we first derive the Cram\'{e}r-Rao bound (CRB) for angle and
delay estimation. Then, we optimize the transmit beamforming at the BS to
minimize the CRB, subject to communication rate and power constraints. In
particular, we obtain the optimal solution in closed-form in the case of
single-target and single-user, and in the case of multi-target and multi-user
scenario, the sparsity of the optimal solution is proven, leading to a
reduction in computational complexity during optimization. The numerical
results demonstrate that the optimized beamforming yields excellent positioning
performance and effectively reduces the requirement for a large number of
antennas at the BS.Comment: This paper has been submitted to IEEE TV
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
MIMO technology has enabled spatial multiple access and has provided a higher
system spectral efficiency (SE). However, this technology has some drawbacks,
such as the high number of RF chains that increases complexity in the system.
One of the solutions to this problem can be to employ reconfigurable antennas
(RAs) that can support different radiation patterns during transmission to
provide similar performance with fewer RF chains. In this regard, the system
aims to maximize the SE with respect to optimum beamforming design and RA mode
selection. Due to the non-convexity of this problem, we propose machine
learning-based methods for RA antenna mode selection in both dynamic and static
scenarios. In the static scenario, we present how to solve the RA mode
selection problem, an integer optimization problem in nature, via deep
convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of
offline and online training is employed for the dynamic RA state selection. For
the proposed MAB, the computational complexity of the optimization problem is
reduced. Finally, the proposed methods in both dynamic and static scenarios are
compared with exhaustive search and random selection methods
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