4,708 research outputs found
Energy-efficient Wireless Analog Sensing for Persistent Underwater Environmental Monitoring
The design of sensors or "things" as part of the new Internet of Underwater
Things (IoUTs) paradigm comes with multiple challenges including limited
battery capacity, not polluting the water body, and the ability to track
continuously phenomena with high temporal/spatial variability. We claim that
traditional digital sensors are incapable to meet these demands because of
their high power consumption, high complexity (cost), and the use of
non-biodegradable materials. To address the above challenges, we propose a
novel architecture consisting of a sensing substrate of dense analog
biodegradable sensors over which lies the traditional Wireless Sensor Network
(WSN). The substrate analog biodegradable sensors perform Shannon mapping (a
data-compression technique) using just a single Field Effect Transistor (FET)
without the need for power-hungry Analog-to-Digital Converters (ADCs) resulting
in much lower power consumption, complexity, and the ability to be powered
using only sustainable energy-harvesting techniques. A novel and efficient
decoding technique is also presented. Both encoding/decoding techniques have
been verified via Spice and MATLAB simulations accounting for underwater
acoustic channel variations.Comment: 5 pages, IEEE UComms 201
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
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