3,054 research outputs found
Expander Chunked Codes
Chunked codes are efficient random linear network coding (RLNC) schemes with
low computational cost, where the input packets are encoded into small chunks
(i.e., subsets of the coded packets). During the network transmission, RLNC is
performed within each chunk. In this paper, we first introduce a simple
transfer matrix model to characterize the transmission of chunks, and derive
some basic properties of the model to facilitate the performance analysis. We
then focus on the design of overlapped chunked codes, a class of chunked codes
whose chunks are non-disjoint subsets of input packets, which are of special
interest since they can be encoded with negligible computational cost and in a
causal fashion. We propose expander chunked (EC) codes, the first class of
overlapped chunked codes that have an analyzable performance,where the
construction of the chunks makes use of regular graphs. Numerical and
simulation results show that in some practical settings, EC codes can achieve
rates within 91 to 97 percent of the optimum and outperform the
state-of-the-art overlapped chunked codes significantly.Comment: 26 pages, 3 figures, submitted for journal publicatio
Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes
Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure
Channel exist. Random Linear Codes (RLC), where redundancy packets consist of
random linear combinations of source packets over a certain finite field, are a
simple yet efficient coding technique, for instance massively used for Network
Coding applications. However the price to pay is a high encoding and decoding
complexity, especially when working on , which seriously limits the
number of packets in the encoding window. On the opposite, structured block
codes have been designed for situations where the set of source packets is
known in advance, for instance with file transfer applications. Here the
encoding and decoding complexity is controlled, even for huge block sizes,
thanks to the sparse nature of the code and advanced decoding techniques that
exploit this sparseness (e.g., Structured Gaussian Elimination). But their
design also prevents their use in convolutional use-cases featuring an encoding
window that slides over a continuous set of incoming packets.
In this work we try to bridge the gap between these two code classes,
bringing some structure to RLC codes in order to enlarge the use-cases where
they can be efficiently used: in convolutional mode (as any RLC code), but also
in block mode with either tiny, medium or large block sizes. We also
demonstrate how to design compact signaling for these codes (for
encoder/decoder synchronization), which is an essential practical aspect.Comment: 7 pages, 12 figure
Reliable Broadcast to A User Group with Limited Source Transmissions
In order to reduce the number of retransmissions and save power for the
source node, we propose a two-phase coded scheme to achieve reliable broadcast
from the source to a group of users with minimal source transmissions. In the
first phase, the information packets are encoded with batched sparse (BATS)
code, which are then broadcasted by the source node until the file can be
cooperatively decoded by the user group. In the second phase, each user
broadcasts the re-encoded packets to its peers based on their respective
received packets from the first phase, so that the file can be decoded by each
individual user. The performance of the proposed scheme is analyzed and the
rank distribution at the moment of decoding is derived, which is used as input
for designing the optimal BATS code. Simulation results show that the proposed
scheme can reduce the total number of retransmissions compared with the
traditional single-phase broadcast with optimal erasure codes. Furthermore,
since a large number of transmissions are shifted from the source node to the
users, power consumptions at the source node is significantly reduced.Comment: ICC 2015. arXiv admin note: substantial text overlap with
arXiv:1504.0446
V2X Content Distribution Based on Batched Network Coding with Distributed Scheduling
Content distribution is an application in intelligent transportation system
to assist vehicles in acquiring information such as digital maps and
entertainment materials. In this paper, we consider content distribution from a
single roadside infrastructure unit to a group of vehicles passing by it. To
combat the short connection time and the lossy channel quality, the downloaded
contents need to be further shared among vehicles after the initial
broadcasting phase. To this end, we propose a joint infrastructure-to-vehicle
(I2V) and vehicle-to-vehicle (V2V) communication scheme based on batched sparse
(BATS) coding to minimize the traffic overhead and reduce the total
transmission delay. In the I2V phase, the roadside unit (RSU) encodes the
original large-size file into a number of batches in a rateless manner, each
containing a fixed number of coded packets, and sequentially broadcasts them
during the I2V connection time. In the V2V phase, vehicles perform the network
coded cooperative sharing by re-encoding the received packets. We propose a
utility-based distributed algorithm to efficiently schedule the V2V cooperative
transmissions, hence reducing the transmission delay. A closed-form expression
for the expected rank distribution of the proposed content distribution scheme
is derived, which is used to design the optimal BATS code. The performance of
the proposed content distribution scheme is evaluated by extensive simulations
that consider multi-lane road and realistic vehicular traffic settings, and
shown to significantly outperform the existing content distribution protocols.Comment: 12 pages and 9 figure
Batched Sparse Codes
Network coding can significantly improve the transmission rate of
communication networks with packet loss compared with routing. However, using
network coding usually incurs high computational and storage costs in the
network devices and terminals. For example, some network coding schemes require
the computational and/or storage capacities of an intermediate network node to
increase linearly with the number of packets for transmission, making such
schemes difficult to be implemented in a router-like device that has only
constant computational and storage capacities. In this paper, we introduce
BATched Sparse code (BATS code), which enables a digital fountain approach to
resolve the above issue. BATS code is a coding scheme that consists of an outer
code and an inner code. The outer code is a matrix generation of a fountain
code. It works with the inner code that comprises random linear coding at the
intermediate network nodes. BATS codes preserve such desirable properties of
fountain codes as ratelessness and low encoding/decoding complexity. The
computational and storage capacities of the intermediate network nodes required
for applying BATS codes are independent of the number of packets for
transmission. Almost capacity-achieving BATS code schemes are devised for
unicast networks, two-way relay networks, tree networks, a class of three-layer
networks, and the butterfly network. For general networks, under different
optimization criteria, guaranteed decoding rates for the receiving nodes can be
obtained.Comment: 51 pages, 12 figures, submitted to IEEE Transactions on Information
Theor
- …