65,047 research outputs found
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems
Despite its promising performance gain, the realization of mmWave massive
MIMO still faces several practical challenges. In particular, implementing
massive MIMO in the digital domain requires hundreds of RF chains matching the
number of antennas. Furthermore, designing these components to operate at the
mmWave frequencies is challenging and costly. These motivated the recent
development of hybrid-beamforming where MIMO processing is divided for separate
implementation in the analog and digital domains, called the analog and digital
beamforming, respectively. Analog beamforming using a phase array introduces
uni-modulus constraints on the beamforming coefficients, rendering the
conventional MIMO techniques unsuitable and call for new designs. In this
paper, we present a systematic design framework for hybrid beamforming for
multi-cell multiuser massive MIMO systems over mmWave channels characterized by
sparse propagation paths. The framework relies on the decomposition of analog
beamforming vectors and path observation vectors into Kronecker products of
factors being uni-modulus vectors. Exploiting properties of Kronecker mixed
products, different factors of the analog beamformer are designed for either
nulling interference paths or coherently combining data paths. Furthermore, a
channel estimation scheme is designed for enabling the proposed hybrid
beamforming. The scheme estimates the AoA of data and interference paths by
analog beam scanning and data-path gains by analog beam steering. The
performance of the channel estimation scheme is analyzed. In particular, the
AoA spectrum resulting from beam scanning, which displays the magnitude
distribution of paths over the AoA range, is derived in closed-form. It is
shown that the inter-cell interference level diminishes inversely with the
array size, the square root of pilot sequence length and the spatial separation
between paths.Comment: Submitted to IEEE JSAC Special Issue on Millimeter Wave
Communications for Future Mobile Networks, minor revisio
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
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