2,215 research outputs found
Embracing corruption burstiness: Fast error recovery for ZigBee under wi-Fi interference
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for
ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to
in-packet blocks for recovering a certain number of random corruptions. Therefore the bursty nature of ZigBee in-packet corruptions
under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks
have no errors but still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying
complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be
helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as
”recovering any k consecutive errors” instead of ”recovering any random k errors”. This lowered requirement allows us to design far
more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple
yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced.
More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission
overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47%)
and latency (by 22%)This work was supported by the National Natural Science
Foundation of China (No. 61602095 and No. 61472360), the
Fundamental Research Funds for the Central Universities (No.
ZYGX2016KYQD098 and No. 2016FZA5010), National Key
Technology R&D Program (Grant No. 2014BAK15B02), CCFIntel
Young Faculty Researcher Program, CCF-Tencent Open
Research Fund, China Ministry of Education—China Mobile
Joint Project under Grant No. MCM20150401 and the EU FP7
CLIMBER project under Grant Agreement No. PIRSES-GA-
2012-318939. Wei Dong is the corresponding author
Space-time coding techniques with bit-interleaved coded modulations for MIMO block-fading channels
The space-time bit-interleaved coded modulation (ST-BICM) is an efficient
technique to obtain high diversity and coding gain on a block-fading MIMO
channel. Its maximum-likelihood (ML) performance is computed under ideal
interleaving conditions, which enables a global optimization taking into
account channel coding. Thanks to a diversity upperbound derived from the
Singleton bound, an appropriate choice of the time dimension of the space-time
coding is possible, which maximizes diversity while minimizing complexity.
Based on the analysis, an optimized interleaver and a set of linear precoders,
called dispersive nucleo algebraic (DNA) precoders are proposed. The proposed
precoders have good performance with respect to the state of the art and exist
for any number of transmit antennas and any time dimension. With turbo codes,
they exhibit a frame error rate which does not increase with frame length.Comment: Submitted to IEEE Trans. on Information Theory, Submission: January
2006 - First review: June 200
Beyond the Bits: Cooperative Packet Recovery Using Physical Layer Information
PhD thesisWireless networks can suffer from high packet loss rates. This paper shows that the loss rate can be significantly reduced by exposing information readily available at the physical layer. We make the physical layer convey an estimate of its confidence that a particular bit is ``0'' or ``1'' to the higher layers. When used with cooperative design, this information dramatically improves the throughput of the wireless network. Access points that hear the same transmission combine their information to correct bits in a packet with minimal overhead. Similarly, a receiver may combine multiple erroneous transmissions to recover a correct packet. We analytically prove that our approach minimizes the errors in packet recovery. We also experimentally demonstrate its benefits using a testbed of GNU software radios. The results show that our approach can reduce loss rate by up to 10x in comparison with the current approach, and significantly outperforms prior cooperation proposals
Blind identification of an unknown interleaved convolutional code
We give here an efficient method to reconstruct the block interleaver and
recover the convolutional code when several noisy interleaved codewords are
given. We reconstruct the block interleaver without assumption on its
structure. By running some experimental tests we show the efficiency of this
method even with moderate noise
Integer-Forcing Source Coding
Integer-Forcing (IF) is a new framework, based on compute-and-forward, for
decoding multiple integer linear combinations from the output of a Gaussian
multiple-input multiple-output channel. This work applies the IF approach to
arrive at a new low-complexity scheme, IF source coding, for distributed lossy
compression of correlated Gaussian sources under a minimum mean squared error
distortion measure. All encoders use the same nested lattice codebook. Each
encoder quantizes its observation using the fine lattice as a quantizer and
reduces the result modulo the coarse lattice, which plays the role of binning.
Rather than directly recovering the individual quantized signals, the decoder
first recovers a full-rank set of judiciously chosen integer linear
combinations of the quantized signals, and then inverts it. In general, the
linear combinations have smaller average powers than the original signals. This
allows to increase the density of the coarse lattice, which in turn translates
to smaller compression rates. We also propose and analyze a one-shot version of
IF source coding, that is simple enough to potentially lead to a new design
principle for analog-to-digital converters that can exploit spatial
correlations between the sampled signals.Comment: Submitted to IEEE Transactions on Information Theor
Error resilient image transmission using T-codes and edge-embedding
Current image communication applications involve image transmission over noisy channels, where the image gets damaged. The loss of synchronization at the decoder due to these errors increases the damage in the reconstructed image. Our main goal in this research is to develop an algorithm that has the capability to detect errors, achieve synchronization and conceal errors.;In this thesis we studied the performance of T-codes in comparison with Huffman codes. We develop an algorithm for the selection of best T-code set. We have shown that T-codes exhibit better synchronization properties when compared to Huffman Codes. In this work we developed an algorithm that extracts edge patterns from each 8x8 block, classifies edge patterns into different classes. In this research we also propose a novel scrambling algorithm to hide edge pattern of a block into neighboring 8x8 blocks of the image. This scrambled hidden data is used in the detection of errors and concealment of errors. We also develop an algorithm to protect the hidden data from getting damaged in the course of transmission
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
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