910 research outputs found
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Deep Task-Based Analog-to-Digital Conversion
Analog-to-digital converters (ADCs) allow physical signals to be processed
using digital hardware. Their conversion consists of two stages: Sampling,
which maps a continuous-time signal into discrete-time, and quantization, i.e.,
representing the continuous-amplitude quantities using a finite number of bits.
ADCs typically implement generic uniform conversion mappings that are ignorant
of the task for which the signal is acquired, and can be costly when operating
in high rates and fine resolutions. In this work we design task-oriented ADCs
which learn from data how to map an analog signal into a digital representation
such that the system task can be efficiently carried out. We propose a model
for sampling and quantization that facilitates the learning of non-uniform
mappings from data. Based on this learnable ADC mapping, we present a mechanism
for optimizing a hybrid acquisition system comprised of analog combining,
tunable ADCs with fixed rates, and digital processing, by jointly learning its
components end-to-end. Then, we show how one can exploit the representation of
hybrid acquisition systems as deep network to optimize the sampling rate and
quantization rate given the task by utilizing Bayesian meta-learning
techniques. We evaluate the proposed deep task-based ADC in two case studies:
the first considers symbol detection in multi-antenna digital receivers, where
multiple analog signals are simultaneously acquired in order to recover a set
of discrete information symbols. The second application is the beamforming of
analog channel data acquired in ultrasound imaging. Our numerical results
demonstrate that the proposed approach achieves performance which is comparable
to operating with high sampling rates and fine resolution quantization, while
operating with reduced overall bit rate
Ultra-Wideband Secure Communications and Direct RF Sampling Transceivers
Larger wireless device bandwidth results in new capabilities in terms of higher data rates and security. The 5G evolution is focus on exploiting larger bandwidths for higher though-puts. Interference and co-existence issues can also be addressed by the larger bandwidth in the 5G and 6G evolution. This dissertation introduces of a novel Ultra-wideband (UWB) Code Division Multiple Access (CDMA) technique to exploit the largest bandwidth available in the upcoming wireless connectivity scenarios. The dissertation addresses interference immunity, secure communication at the physical layer and longer distance communication due to increased receiver sensitivity. The dissertation presents the design, workflow, simulations, hardware prototypes and experimental measurements to demonstrate the benefits of wideband Code-Division-Multiple-Access. Specifically, a description of each of the hardware and software stages is presented along with simulations of different scenarios using a test-bench and open-field measurements. The measurements provided experimental validation carried out to demonstrate the interference mitigation capabilities. In addition, Direct RF sampling techniques are employed to handle the larger bandwidth and avoid analog components. Additionally, a transmit and receive chain is designed and implemented at 28 GHz to provide a proof-of-concept for future 5G applications. The proposed wideband transceiver is also used to demonstrate higher accuracy direction finding, as much as 10 times improvement
Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining
The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising
approach to significantly reduce the power consumption and hardware cost of
multiple-input multiple-output (MIMO) receivers. However, the nonlinear
distortion due to 1-bit quantization fundamentally changes the optimal
communication strategy and also imposes a capacity penalty to the system. In
this paper, the capacity of a Gaussian MIMO channel in which the antenna
outputs are processed by an analog linear combiner and then quantized by a set
of zero threshold ADCs is studied. A new capacity upper bound for the zero
threshold case is established that is tighter than the bounds available in the
literature. In addition, we propose an achievability scheme which configures
the analog combiner to create parallel Gaussian channels with phase
quantization at the output. Under this class of analog combiners, an algorithm
is presented that identifies the analog combiner and input distribution that
maximize the achievable rate. Numerical results are provided showing that the
rate of the achievability scheme is tight in the low signal-to-noise ratio
(SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs
analog temporal and spatial processing is proposed. The proposed receiver
attains the capacity in the high SNR regime.Comment: 30 pages, 9 figures, Submitted to IEEE Transactions on Communication
AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing
The enormous success of advanced wireless devices is pushing the demand for
higher wireless data rates. Denser spectrum reuse through the deployment of
more access points per square mile has the potential to successfully meet the
increasing demand for more bandwidth. In theory, the best approach to density
increase is via distributed multiuser MIMO, where several access points are
connected to a central server and operate as a large distributed multi-antenna
access point, ensuring that all transmitted signal power serves the purpose of
data transmission, rather than creating "interference." In practice, while
enterprise networks offer a natural setup in which distributed MIMO might be
possible, there are serious implementation difficulties, the primary one being
the need to eliminate phase and timing offsets between the jointly coordinated
access points.
In this paper we propose AirSync, a novel scheme which provides not only time
but also phase synchronization, thus enabling distributed MIMO with full
spatial multiplexing gains. AirSync locks the phase of all access points using
a common reference broadcasted over the air in conjunction with a Kalman filter
which closely tracks the phase drift. We have implemented AirSync as a digital
circuit in the FPGA of the WARP radio platform. Our experimental testbed,
comprised of two access points and two clients, shows that AirSync is able to
achieve phase synchronization within a few degrees, and allows the system to
nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC
and higher layer aspects of a practical deployment. To the best of our
knowledge, AirSync offers the first ever realization of the full multiuser MIMO
gain, namely the ability to increase the number of wireless clients linearly
with the number of jointly coordinated access points, without reducing the per
client rate.Comment: Submitted to Transactions on Networkin
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