2,780 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Smart Sensor Networks For Sensor-Neural Interface
One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface.
In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system.
The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption
Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions
The Internet of Medical Things (IoMT) paradigm will enable next generation
healthcare by enhancing human abilities, supporting continuous body monitoring
and restoring lost physiological functions due to serious impairments. This
paper presents intra-body communication solutions that interconnect implantable
devices for application to the nervous system, challenging the specific
features of the complex intra-body scenario. The presented approaches include
both speculative and implementative methods, ranging from neural signal
transmission to testbeds, to be applied to specific neural diseases therapies.
Also future directions in this research area are considered to overcome the
existing technical challenges mainly associated with miniaturization, power
supply, and multi-scale communications.Comment: https://www.sciencedirect.com/science/article/pii/S138912862300163
A low-power/low-voltage CMOS wireless interface at 5.7 GHz with dry electrodes for cognitive networks
This paper describes a low-power/low-voltage CMOS
wireless interface (CMOS-WiI) at 5.7 GHz with dry electrodes for
congnitive networks. The electrodes are 4 x 4 microtip arrays and
acquire electroencephalogram (EEG) signals in key- points for
processing. The CMOS-WiI was fabricated in a UMC 0.18 µm
RF CMOS process and its total power consumption is 23mW with
a voltage-supply of only 1.5 V. The carrier frequency is digitally
selectable and it can be one of 16 possible values in the range
5.42–5.83 GHz, with 27.12 MHz steps. These multiple carriers
allow a better spectrum allocation as well as the acquisition,
processing and transmission of high-quality EEG signals from 16
electrode arrays. The microtips array was fabricated through bulk
micromachining of a -type silicon substrate in a potassium
hydroxide solution and avoids long subject preparations for EEG
data acquisition. The reactive sputtering of iridium dioxide (IrO)
on the surface of the array guarantees its biocompatibility. The
fabrication process was trimmed in a way that each microtip
presents solid angles of 54.7 , a width in the range 150–200 µm, a
height of 100–200 µm, and a microtip interspacing of 2 µm. The
microtips array coated with IrO together with the CMOS-WiI
permit the remote monitoring of EEG signals from freely-moving
subjects
Low-Power Circuits for Brain–Machine Interfaces
This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use
in multi-electrode arrays; an analog linear decoding and learning
architecture for data compression; low-power radio-frequency
(RF) impedance-modulation circuits for data telemetry that
minimize power consumption of implanted systems in the body;
a wireless link for efficient power transfer; mixed-signal system
integration for efficiency, robustness, and programmability; and
circuits for wireless stimulation of neurons with power-conserving
sleep modes and awake modes. Experimental results from chips
that have stimulated and recorded from neurons in the zebra
finch brain and results from RF power-link, RF data-link, electrode-
recording and electrode-stimulating systems are presented.
Simulations of analog learning circuits that have successfully
decoded prerecorded neural signals from a monkey brain are also
presented
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