4,522 research outputs found
Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding
In this paper, we consider the problem of signal recovery from 1-bit noisy
measurements. We present an efficient method to obtain an estimation of the
signal of interest when the measurements are corrupted by white or colored
noise. To the best of our knowledge, the proposed framework is the pioneer
effort in the area of 1-bit sampling and signal recovery in providing a unified
framework to deal with the presence of noise with an arbitrary covariance
matrix including that of the colored noise. The proposed method is based on a
constrained quadratic program (CQP) formulation utilizing an adaptive
quantization thresholding approach, that further enables us to accurately
recover the signal of interest from its 1-bit noisy measurements. In addition,
due to the adaptive nature of the proposed method, it can recover both fixed
and time-varying parameters from their quantized 1-bit samples.Comment: This is a pre-print version of the original conference paper that has
been accepted at the 2018 IEEE Asilomar Conference on Signals, Systems, and
Computer
5G Millimeter Wave Cellular System Capacity with Fully Digital Beamforming
Due to heavy reliance of millimeter-wave (mmWave) wireless systems on
directional links, Beamforming (BF) with high-dimensional arrays is essential
for cellular systems in these frequencies. How to perform the array processing
in a power efficient manner is a fundamental challenge. Analog and hybrid BF
require fewer analog-to-digital converters (ADCs), but can only communicate in
a small number of directions at a time,limiting directional search, spatial
multiplexing and control signaling. Digital BF enables flexible spatial
processing, but must be operated at a low quantization resolution to stay
within reasonable power levels. This paper presents a simple additive white
Gaussian noise (AWGN) model to assess the effect of low resolution quantization
of cellular system capacity. Simulations with this model reveal that at
moderate resolutions (3-4 bits per ADC), there is negligible loss in downlink
cellular capacity from quantization. In essence, the low-resolution ADCs limit
the high SNR, where cellular systems typically do not operate. The findings
suggest that low-resolution fully digital BF architectures can be power
efficient, offer greatly enhanced control plane functionality and comparable
data plane performance to analog BF.Comment: To appear in the Proceedings of the 51st Asilomar Conference on
Signals, Systems, and Computers, 201
Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs
With the congestion of the sub-6 GHz spectrum, the interest in massive
multiple-input multiple-output (MIMO) systems operating on millimeter wave
spectrum grows. In order to reduce the power consumption of such massive MIMO
systems, hybrid analog/digital transceivers and application of low-resolution
digital-to-analog/analog-to-digital converters have been recently proposed. In
this work, we investigate the energy efficiency of quantized hybrid
transmitters equipped with a fully/partially-connected phase-shifting network
composed of active/passive phase-shifters and compare it to that of quantized
digital precoders. We introduce a quantized single-user MIMO system model based
on an additive quantization noise approximation considering realistic power
consumption and loss models to evaluate the spectral and energy efficiencies of
the transmit precoding methods. Simulation results show that
partially-connected hybrid precoders can be more energy-efficient compared to
digital precoders, while fully-connected hybrid precoders exhibit poor energy
efficiency in general. Also, the topology of phase-shifting components offers
an energy-spectral efficiency trade-off: active phase-shifters provide higher
data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
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