4 research outputs found
Low-power All-analog Circuit for Rectangular-type Analog Joint Source Channel Coding
A low-complexity all-analog circuit is proposed to perform efficiently Analog
Joint Source Channel Coding (AJSCC), which can compress two or more sensor
signals into one with controlled distortion while also being robust against
wireless channel impairments. The idea is to realize the rectangular-type AJSCC
using Voltage Controlled Voltage Sources (VCVS). The proposal is verified by
Spice simulations as well as breadboard and Printed Circuit Board (PCB)
implementations. Results indicate that the design is feasible for
low-complexity systems like persistent wireless sensor networks requiring low
circuit power.Comment: 4 pages ISCAS 2016. arXiv admin note: text overlap with
arXiv:1701.05599, arXiv:1907.0144
Improved Circuit Design of Analog Joint Source Channel Coding for Low-power and Low-complexity Wireless Sensors
To enable low-power and low-complexity wireless monitoring, an improved
circuit design of Analog Joint Source Channel Coding (AJSCC) is proposed for
wireless sensor nodes. This innovative design is based on Analog Divider Blocks
(ADB) with tunable spacing between AJSCC levels. The ADB controls the switching
between two types of Voltage Controlled Voltage Sources (VCVS). LTSpice
simulations were performed to evaluate the performance of the circuit, and the
power consumption and circuit complexity of this new ADB-based design were
compared with our previous parallel-VCVS design. It is found that this improved
circuit design based on ADB outperforms the design based on parallel VCVS for a
large number of AJSCC levels (>= 16), both in terms of power consumption as
well as circuit complexity, thus enabling persistent and higher
temporal/spatial resolution environmental sensing.Comment: 8 pages, IEEE Sensor Journa
Signal Recovery Performance Analysis in Wireless Sensing with Rectangular-Type Analog Joint Source-Channel Coding
The signal recovery performance of the rectangular-type Analog Joint
Source-Channel Coding (AJSCC) is analyzed in this work for high and medium/low
Signal-to-Noise Ratio (SNR) scenarios in the wireless sensing systems. The
analysis and derivations of the medium/low SNR scenario are based on the
comprehensive listing of all the signal variation cases in the
three-dimensional signal mapping curve of the rectangular-type AJSCC.
Theoretical formulations of Mean Square Error (MSE) performance are derived for
both analog sensing and digital sensing systems with rectangular-type AJSCC.
Evaluation results indicate that, there are optimal parameters in the
rectangular-type AJSCC to minimize the signal recovery MSE performance at high
and medium/low SNR scenarios. In addition, the performance of digital sensing
with low-resolution Analog-to-Digital Conversion (ADC) is compared with analog
sensing for both high and medium/low SNR scenarios in this work. The
theoretical and evaluation results have practical value to the wireless sensing
system designs based on the rectangular-type AJSCC
Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things
Scalability is a major issue for Internet of Things (IoT) as the total amount
of traffic data collected and/or the number of sensors deployed grow. In some
IoT applications such as healthcare, power consumption is also a key design
factor for the IoT devices. In this paper, a multi-signal compression and
encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed
that works fully in the analog domain without the need for power-hungry
Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all
the input signals but one. While saving power, this method can also reduce the
number of devices by combining one or more sensing functionalities into a
single device (called 'AJSCC device'). Apart from analog encoding, AJSCC
devices communicate to an aggregator node (FPMM receiver) using a novel
Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint
modulation and multiplexing technique presents three mayor advantages---it is
robust to interference at particular frequency bands, it protects against
eavesdropping, and it consumes low power due to a very low Signal-to-Noise
Ratio (SNR) operating region at the receiver. Performance of the proposed
multi-signal compression method and FPMM technique is evaluated via simulations
in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR),
respectively.Comment: 9 pages, IEEE MASS 201