BATS Coding Scheme for Multi-hop Data Transport
draft-irtf-nwcrg-bats-01
The information below is for an old version of the document.
| Document | Type | Active Internet-Draft (nwcrg RG) | |
|---|---|---|---|
| Authors | Shenghao Yang , Xuan Huang , Raymond W. Yeung , Dr. John K. Zao | ||
| Last updated | 2021-07-28 | ||
| Replaces | draft-yang-nwcrg-bats | ||
| Stream | Internet Research Task Force (IRTF) | ||
| Formats | plain text html xml htmlized pdfized bibtex | ||
| Stream | IRTF state | Active RG Document | |
| Consensus boilerplate | Unknown | ||
| Document shepherd | Vincent Roca | ||
| IESG | IESG state | I-D Exists | |
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| Responsible AD | (None) | ||
| Send notices to | vincent.roca@inria.fr |
draft-irtf-nwcrg-bats-01
NWCRG S. Yang
Internet-Draft CUHK(SZ)
Intended status: Informational X. Huang
Expires: 29 January 2022 R. W. Yeung
CUHK
J. K. Zao
NCTU
28 July 2021
BATS Coding Scheme for Multi-hop Data Transport
draft-irtf-nwcrg-bats-01
Abstract
BATS code is a class of efficient linear network coding scheme with a
matrix generalization of fountain codes as the outer code, and batch-
based linear network coding as the inner code. This document
describes a baseline BATS coding scheme for communication through
multi-hop networks, and discusses the related research issues towards
a more sophisticated BATS coding scheme. This document is a product
of the Coding for Efficient Network Communications Research Group
(NWCRG).
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 29 January 2022.
Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
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provided without warranty as described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.2. Terminology and Definitions . . . . . . . . . . . . . . . 4
2. A Use Case of BATS Coding Scheme . . . . . . . . . . . . . . 4
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 4
2.2. Data Delivery Procedures . . . . . . . . . . . . . . . . 6
2.2.1. Source Node Data Partitioning and Padding . . . . . . 6
2.2.2. Source Node Outer Code Encoding Procedure . . . . . . 7
2.2.3. Recoding Procedures . . . . . . . . . . . . . . . . . 8
2.2.4. Destination Node Procedures . . . . . . . . . . . . . 9
2.3. Recommendation for the Parameters . . . . . . . . . . . . 10
2.4. Example DDP Packet Format . . . . . . . . . . . . . . . . 10
2.4.1. Packet Header . . . . . . . . . . . . . . . . . . . . 10
2.4.2. Packet Payload . . . . . . . . . . . . . . . . . . . 11
3. BATS Code Specification . . . . . . . . . . . . . . . . . . . 11
3.1. Common Parts . . . . . . . . . . . . . . . . . . . . . . 11
3.2. Outer Code Encoder . . . . . . . . . . . . . . . . . . . 12
3.3. Inner Code Encoder (Recoder) . . . . . . . . . . . . . . 13
3.4. Belief Propagation Decoder . . . . . . . . . . . . . . . 14
4. Research Issues . . . . . . . . . . . . . . . . . . . . . . . 15
4.1. Coding Design Issues . . . . . . . . . . . . . . . . . . 15
4.2. Protocol Design Issues . . . . . . . . . . . . . . . . . 16
4.3. Application Related Issues . . . . . . . . . . . . . . . 17
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 18
6. Security Considerations . . . . . . . . . . . . . . . . . . . 18
6.1. Provision of Confidentiality Protection . . . . . . . . . 18
6.2. Countermeasures against Pollution Attacks . . . . . . . . 18
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
7.1. Normative References . . . . . . . . . . . . . . . . . . 19
7.2. Informative References . . . . . . . . . . . . . . . . . 19
Appendix A. Additional Stuff . . . . . . . . . . . . . . . . . . 21
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 21
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1. Introduction
This document specifies a baseline BATS code [Yang14] scheme for data
delivery in multi-hop networks, and discusses the related research
issues towards a more sophisticated scheme. The BATS code described
here includes an outer code and an inner code. The outer code is a
matrix generalization of fountain codes (see also the RapterQ code
described in RFC 6330 [RFC6330]), which inherits the advantages of
reliability and efficiency and possesses the extra desirable property
of being network coding compatible. The inner code, also called
recoding, is formed by linear network coding for combating packet
loss, improving the multicast efficiency, etc. A detailed design and
analysis of BATS codes are provided in the BATS monograph [Yang17].
A BATS coding scheme can be applied in multi-hop networks formed by
wireless communication links, which are inherently unreliable due to
interference. Existing transport protocols like TCP use end-to-end
retransmission, while network protocols such as IP might enable
store-and-forward at the relays, so that packet loss would accumulate
along the way.
A BATS coding scheme can be used for various data delivery
applications like file transmission, video streaming over wireless
multi-hop networks, etc. Different from traditional forward error
correcting (FEC) schemes that are applied either hop-by-hop or end-
to-end, the BATS coding scheme combines the end-to-end coding (the
outer code) with certain hop-by-hop coding (the inner code), and
hence can potentially achieve better performance.
The baseline coding scheme described here considers a network with
multiple communication flows. For each flow, the source node encodes
the data for transmission separately. Inside the network, however,
it is possible to mix the packets from different flows for recoding.
In this document, we describe a simple case where recoding is
performed within each flow. Note that the same encoding/decoding
scheme described here can be used with different recoding schemes as
long as they follow the principle as we illustrate in this document.
The purpose of the baseline BATS coding scheme is twofold. First, it
provides researchers and engineers a starting point for developing
network communication applications/protocols based on BATS codes.
Second, it helps to make the research issues more clear towards a
sophisticated BATS code based network protocol. Important research
directions include the security issues, congestion control and
routing algorithms for BATS codes, etc.
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This document is a product of and represents the collaborative work
and consensus of the Coding for Efficient Network Communications
Research Group (NWCRG); it is not an IETF product and is not an IETF
standard.
1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
1.2. Terminology and Definitions
* DDP: data delivery protocol.
* DDP packet: the packet formed by a DDP employing a BATS coding
scheme.
* source packet: the packet formed by the data for delivery.
* BATS/coded packet: the packet generated by a BATS code encoder/
recoder.
* batch: a set of coded packets generated by a BATS coding scheme
with the same subset of the source packets.
* recoded packet: a coded packet generated by a recoder.
* degree: the number of source packets used in a batch generation.
2. A Use Case of BATS Coding Scheme
The BATS coding scheme described in this document can be used, for
example, by a Data Delivery Protocol (DDP). Though this document is
not about a DDP, we briefly illustrate in this section how a BATS
coding scheme is employed by a DDP to make the role of the coding
scheme clear.
2.1. Introduction
A BATS coding scheme includes an outer code encoder (also called
encoder), an inner code encoder (also called recoder) and a decoder,
which decodes the outer code and the inner code jointly. We describe
a data delivery process that involves one source node, one
destination node, and multiple intermediate nodes in between as
illustrated in Figure 1. This process includes the following
procedures:
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+-+-+-+-+-+-+-+ +-+-+-+-+-+-+-+-+-+-+
| source node | --> | intermediate node | --> ...
+-+-+-+-+-+-+-+ +-+-+-+-+-+-+-+-+-+-+
+-+-+-+-+-+-+-+-+-+-+ +-+-+-+-+-+-+-+-+-+-+
--> | intermediate node | --> | destination node |
+-+-+-+-+-+-+-+-+-+-+ +-+-+-+-+-+-+-+-+-+-+
Figure 1: A network model for data delivery.
Outer code encoding is performed at the source node which has the
data for transmission:
- The DDP provides the data to be delivered and the related
information to the BATS encoder.
- The BATS encoder generates a sequence of batches, each
consisting of a set of coded packets (also called BATS packets)
and the information pertaining to the batch.
The batches generated at the source node are further recoded
before transmitting:
- A BATS recoder generates recoded packets of a batch.
- The DDP forms and transmits the DDP packets using the batches
and the corresponding batch information.
Recoding is performed at an intermediate node that does not need
the data:
- The DDP extracts the batches and the corresponding batch
information from its received DDP packets.
- A BATS recoder generates recoded packets of a batch.
- The DDP forms and transmits DDP packets using the recoded
packets and the corresponding batch information.
Decoding is performed at the destination node that needs the data:
- The DDP extracts the batches and the corresponding batch
information from its received DDP packets.
- A BATS decoder tries to recover the transmitted data using the
received batches.
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- The DDP sends the decoded data to the application that needs
the data.
2.2. Data Delivery Procedures
Suppose that the DDP has F octets of data for transmission. We
describe the procedures of one BATS session for transmitting the F
octets. There is a limit on F of a single BATS session. If the
total data has more than the limit, the data needs to be transmitted
using multiple BATS sessions. The limit on F of a single BATS
session depends on the PMTU (path maximum transmission unit) of the
network, which MUST be known by the DDP. We have F is no more than
(PMTU-7)2^16-1 octets.
2.2.1. Source Node Data Partitioning and Padding
The DDP first determines the following parameters:
* Batch size (M): the number of coded packets in a batch.
* Recoding field size (q): the number of elements in the finite
field for recoding. q is 2 or 2^8
* BATS payload size (TO): the number of payload octets in a BATS
packet, including the coded data and the coefficient vector.
Based on the above parameters, the parameters T, O and K are
calculated as follows:
* O: the number of octets of a coefficient vector, calculated as O =
ceil(M*log2(q)/8).
* T: the number of data octets of a coded packet, calculated as T =
TO - O.
* K: number of source packets, calculated as K = floor(F/T)+1.
The data MUST be padded to have T*K octets, which will be partitioned
into K source packets b[0], ..., b[K-1], each of T octets. In our
padding scheme, b[0], ..., b[K-2] are filled with data octets, and
b[K-1] is filled with the remaining data octets and padding octets.
Let P = K*T-F denote the number of padding octets. We use b[K-1, 0],
..., b[K-1, T-P-1] to denote the T-P source octets and b[K-1, T-P],
..., b[K-1, T-1] to denote the P padding octets in b[K-1],
respectively. The padding insertion process is shown in Figure 2.
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Z = T - P
j = 1
v = 1
Let bl be the last source packet b[K-1]
for i = Z, Z+1, ..., T-1 do
bl[i] = j
if i+1 >= v+Z do
j += 1
v += j
Figure 2: Data Padding Insertion Process
2.2.2. Source Node Outer Code Encoding Procedure
The DDP provides the BATS encoder to be decribed at Section 3.2 with
the following information:
* Batch size (M): the number of coded packets in a batch.
* Recoding field size (q): the number of elements in the finite
field for recoding.
* Maximum degree (MAX_DEG): a positive integer.
* Degree distribution (DD): an unsigned integer array of size
MAX_DEG+1.
* A sequence of batch IDs (BID) (j, j = 0, 1, ...).
* Number of source packets (K).
* Packet size (T): the number of octets in a source packet.
* Source packets (b[i], i = 0, 1, ..., K-1).
Using this information, the (outer code) encoder generates a batch
for each batch ID. The DDP recieves from the encoder a sequence of
batches, where the batch with ID j has
* a degree d[j], and
* M coded packets (x[j,i], i =0, 1, ..., M-1), each containing TO
octets.
The DDP will use the batches to form DDP packets to be transmitted to
other network nodes towards the destination nodes. The DDP MUST
deliver with each coded packet with its
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* d: degree
* BID: batch ID
The DDP MUST deliver the following information to each recoder:
* M: batch size M
* q: recoding field size
The DDP MUST deliver the following information to each decoder:
* M: batch size
* q: recoding field size
* K: the number of source packets
* T: the number of octets in a source packet
The BID is used by both recoders and decoders. The BATS payload size
TO MUST be known by all the nodes.
2.2.3. Recoding Procedures
Both the source node and the intermediate nodes perform recoding on
the batches before transmission. At the source node, the recoder
receives the batches from the outer code encoding procedure. At an
intermediate node, the DDP receives the DDP packets from the other
network nodes, and should be able to extract coded packets and the
corresponding batch information from these packets.
If the DDP choose not to recode, it just forwards the packets it
received. Otherwise, the DDP can choose a recoder to be described in
Section 3.3 and provides the recoder with the following information:
* the batch size M,
* the recoding field size q,
* a number of received coded packets of the same batch, each
containing TO octets, and
* link statistics, e.g., packet loss rates.
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For a received batch, the recoder determines a positive integer Mr,
the number of recoded packets to be transmitted for the batch. The
recoder uses the information provided by the DDP to generate Mr
recoded packets, each containing TO octets. The DDP uses the Mr
recoded packets to form the DDP packets for transmitting.
2.2.4. Destination Node Procedures
A destination node needs the data transmitted by the source node. At
the destination node, the DDP receives DDP packets from an
intermediate network node, and should be able to extract coded
packets and the corresponding batch information from these packets.
The DDP use a decoder described in Section 3.4 and provides the
decoder with the following information:
* M: batch size,
* q: recoding field size,
* K: the number of source packets
* T: the number of octets of a source packet
* A sequence of batches, each of which is formed by a number of
coded packets belonging to the same batch, with their
corresponding BIDs and degrees.
The decoder uses this information to decode the outer code and the
inner code jointly and recover the K source packets. If successful,
the decoder returns the recovered K source packets to the DDP, which
will use the K source packets to form the F octets data. The
recommended padding deletion process is shown as follows:
// this procedure returns the number P of padding octets
// at the end of b[K-1]
Let bl be the last decoded source packet b[K-1]
PL = bl[T-1]
if PL == 1 do
return P = 1
WI = T - 1
while bl[WI] == PL do
WI = WI - 1
return P = (1 + bl[WI]) * bl[WI] + T - WI - 1
Figure 3: Data Padding Deletion Process
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2.3. Recommendation for the Parameters
The recommendation for the parameters M and q is shown as follows:
* When q=2, M=16,32,64
* When q=256, M=8,16,32,64
It is RECOMMENDED that K is at least 128. The encoder/decoder SHALL
support an arbitrary positive integer value less than 2^16. However,
the BATS coding scheme to be described is not optimized for small K.
2.4. Example DDP Packet Format
A DDP can form a DDP packet with a header (5 octets) and a payload
(TO octets). A DDP packet has totally 5+TO octets.
2.4.1. Packet Header
The BATS packet header has 40 bits (5 octets) and includes fields K,
Mq, BID, and d.
0 1 2 3
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| K | Mq | BID |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| d |
+-+-+-+-+-+-+-+-+
Figure 4: BATS packet header format.
* K: 16-bit unsigned integer, specifying the number of source
packets of the BATS session.
* Mq: 4-bit unsigned integer to specify the value of M and q as
Table 1.
* BID: 12-bit unsigned integer, specifying the batch ID of the batch
the packet belongs to.
* d: 8-bit unsigned integer, specifying the batch degree of the
batch the packet belongs to.
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+======+====+=====+====+
| Mq | M | q | O |
+======+====+=====+====+
| 0010 | 16 | 2 | 2 |
+------+----+-----+----+
| 0100 | 32 | 2 | 4 |
+------+----+-----+----+
| 0110 | 64 | 2 | 8 |
+------+----+-----+----+
| 0001 | 8 | 256 | 8 |
+------+----+-----+----+
| 0011 | 16 | 256 | 16 |
+------+----+-----+----+
| 0101 | 32 | 256 | 32 |
+------+----+-----+----+
| 0111 | 64 | 256 | 64 |
+------+----+-----+----+
Table 1: Values of Mq
field
2.4.2. Packet Payload
O T
+-----------------------+-------------------------------+
| coefficient vector | coded data |
+-----------------------+-------------------------------+
Figure 5: BATS packet payload format.
The payload has TO octets, where the first O octets contain the
coefficient vector and the remaining T octets contain the coded data.
.
* coefficient vector: O octets. The range of the value of O is in
Table 1.
* coded data: T octets. T is at most PMTU - 7, where 7 is the total
length of the header plus 2, the minimum value of O.
3. BATS Code Specification
3.1. Common Parts
The T octets of a source packets are treated as a column vector of T
elements in GF(256). Linear algebra and matrix operations over
finite fields are assumed in this section.
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Suppose that a pseudorandom number generator Rand() which generates
an unsigned integer of 32 bits is shared by both encoding and
decoding. The pseudorandom generator can be initialized by
Rand_Init(S) with seed S. When S is not provided, the pseudorandom
generator is initialized arbitrarily. One example of such a
pseudorandom generator is defined in RFC 8682 [RFC8682].
A function called BatchSampler is used in both encoding and decoding.
The function takes two integers j and d as input, and generates an
array idx of d integers and a d x M matrix G. The function first
initializes the pseudorandom generator with j, sample d distinct
integers from 0 to K-1 as idx, and sample d*M integers from 0 to 255
as G. See the pseudocode in Figure 6.
function BatchSampler(j,d)
// initialize the pseudorandom generator by seed j.
Rand_Init(j)
// sample d distinct integers between 0 and K-1.
for k = 0, ..., d-1 do
r = Rand() % K
while r already exists in idx do
r = Rand() % K
idx[k] = r
// sample d x M matrix
for r = 0, ..., d-1 do
for c = 0,...,M-1 do
G[r,c] = Rand() % 256
return idx, G
Figure 6: Batch Sampler Function
3.2. Outer Code Encoder
Define a function called DegreeSampler that return an integer d using
the degree distribution DD. We expect that the empirical
distribution of the returning d converges to DD(d) when d < K. One
design of DegreeSampler is illustrated in Figure 7.
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function DegreeSampler(j, DD)
Let CDF be an array
CDF[0] = 0
for i = 1, ..., MAX_DEG do
CDF[i] = CDF[i-1] + DD[i]
Rand_Init()
r = Rand() % CDF[MAX_DEG]
for d = 1, ..., MAX_DEG do
if r >= CDF[d] do
return min(d,K)
return min(MAX_DEG,K)
Figure 7: Degree Sampler Function
Let b[0], b[1], ..., b[K-1] be the K source packets. A batch with
BID j is generated using the following steps.
* Obtain a degree d by calling DegreeSampler with input j.
* Obtain idx and G[j] by calling BatchSampler with input j and d.
* Let B[j] = (b[idx[0]], b[idx[1]], ..., b[idx[d-1]]). Form the
batch X[j] = B[j]*G[j], whose dimension is T x M.
* Form the TO x M matrix Xr[j], where the first O rows of Xr[j] form
the M x M identity matrix I with entries in GF(q), and the last T
rows of Xr[j] is X[j].
See the pseudocode of the batch generating process in Figure 8.
function GenBatch(j)
d = DegreeSampler(j)
(idx, G) = BatchSampler(j,d)
B = (b[idx[0]], b[idx[i]], ..., b[idx[d-1]])
X = B * G
Xr = [I_M; X]
return Xr
Figure 8: Batch Generation Function
3.3. Inner Code Encoder (Recoder)
The inner code comprises (random) linear network coding applied on
the coded packets belonging to the same batch. At a particular
network node, recoded packets are generated by (random) linear
combinations of the received coded packets of a batch. The recoded
packets have the same BID, degree and coded packet length.
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The number Mr of recoded packets for a batch is decided first by the
recoder. Mr can be set as M. When the link statistics is known, the
recoder can try to obtain the link packet loss rate e for the link to
transmit the recoded batch, and set Mr to be (1+e)M.
Suppose that coded packets xr[i], i = 0, 1, ..., r-1, which have the
same BID j, have been received at an intermediate node. Using the
recommended packet format, it can be verified whether the
corresponding packet headers of these coded packets are the same.
Then a recoded packet can be generated by one of the following two
approaches:
* forwarding: when receiving xr[i], directly use xr[i] as a recoded
packet.
* linear combination recoding: (randomly) choose a sequence of
coefficients c[i], i = 0, 1, ..., r-1 from GF(q). Generate
c[0]xr[0]+c[1]xr[1]+...+c[r-1]xr[r-1] as a recoded packet.
A recoder can combine these two approaches to generate recoded
packets. For example, the recoder will output xr[i], i = 0, 1, ...,
r-1 as r systematic recoded packets and generate Mr-r recoded packets
using linear combinations of randomly chosen coefficients.
3.4. Belief Propagation Decoder
The decoder receives a sequence of batches Yr[j], j = 0, 1, ..., n-1,
each of which is a TO-row matrix over GF(256). The degree d[j] of
batch j is also known. Let Y[j] be the submatrix of the last T rows
of Yr[j]. When q = 256, let H[j] be the first M rows of Yr[j]; when
q = 2, let H[j] be the matrix over GF(256) formed by embedding each
bit in the first M/8 rows of Yr[j] into GF(256).
By calling BatchSampler with input j and d[j], we obtain idx[j] and
G[j]. According to the encoding and recoding processes described in
Section 3.2 and Section 3.3, we have the system of linear equations
Y[j] = B[j]G[j]H[j] for each received batch with ID j, where B[j] =
(b[idx[j,0]], b[idx[j,1]], ..., b[idx[j,d-1]]) is unknown.
We describe a belief propagation (BP) decoder that can efficiently
solve the source packets when a sufficient number of batches have
been received. A batch j is said to be decodable if rank(G[j]H[j]) =
d[j] (i.e., the system of linear equations Y[j] = B[j]G[j]H[j] with
B[j] as the variable matrix has a unique solution). The BP decoding
algorithm has multiple iterations. Each iteration is formed by the
following steps:
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* Decoding step: Find a batches j that is decodable. Solve the
corresponding system of linear equations Y[j] = B[j]G[j]H[j] and
decode B[j].
* Substitution step: Substitute the decoded source packets into
undecodable batches. Suppose that a decoded source packet b[k] is
used in generating a undecodable Y[j]. The substitution involves
1) removing the entry in idx[j] corresponding to k, 2) removing
the row in G[j] corresponding to b[k], and 3) reducing d[j] by 1.
The BP decoder repeats the above steps until no batches are decodable
during the decoding step.
4. Research Issues
The baseline BATS coding scheme described in Section 2 and Section 3
needs various refinement and complement towards becoming a more
sophisticated network communication application. Various related
research issues are discussed in this section, but the security
related issues are left to Section 6.
4.1. Coding Design Issues
The BATS code specification in Section 3 has nearly optimal
throughput when the number of source packets K is sufficiently large.
But when K is small, the degree sampler function in Figure 7 and the
BatchSampler function in Figure 6 based on a pseudorandom generator
may not sample all the source packets evenly, so that some of the
source packets are not well protected. One approach to solve this
issue is to generate a deterministic degree sequence when the number
of batches is relatively small, and design a special pseudorandom
generator that has a good sampling performance when K is small.
The belief propagation decoder in Section 3.4 guarantees the recovery
of a given fraction of the source packets. To recover all the source
packets, a precode can be applied to the source packets to generate a
fraction of redundant packets before applying the outer code
encoding. Moreover, when the belief propagation decoder stops, it is
possible to continue with inactivation decoding, where certain source
packets are treated inactive so that a similar belief propagation
process can be resumed. The reader is referred to RFC 6330 [RFC6330]
for the design of a precode with a good inactivation decoding
performance.
There are research issues related to recoding discussed in
Section 3.3. One question is how many recoded packets to generate
for each batch. Though it is asymptotically optimal when using the
same number of recoded packets for all batches, it has been shown
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that transmitting a different number of recoded packets for different
batches can improve the recoding efficiency. The intuition is that
for a batch with a lower rank, a smaller number of recoded packets
need to be transmitted. This kind of recoding scheme is called
adaptive recoding [Yin19].
Packet loss in network communication is usually bursty, which may
harm the recoding performance. One way to resolve this issue is to
transmit the packets of different batches in a mixed order, which is
also called batch interleaving [Yin20]. How to efficiently
interleave batches without increasing too much end-to-end latency is
a research issue.
Though we only focus on the BATS coding scheme with one source node
and one destination node, a BATS coding scheme can be used for
multiple source and destination nodes. To benefit from multiple
source nodes, we would need different source nodes to generate
statistically independent batches. For communicating the same data
to multiple destination nodes, which is also call multicast, it is
well-known that linear network coding [Li03] achieves the mulicast
capacity. BATS codes can benefit from network coding due to its
inner code, but how to efficiently implement multicast needs further
research.
4.2. Protocol Design Issues
The baseline scheme in this document focuses on reliable
communication. There are other issues to be considered towards
designing a fully functionally DDP based on a BATS coding scheme.
Here we discuss some network management issues that are closely
related to a BATS coding scheme: routing, congestion control and
media access control.
The outer code of a BATS code can be regarded as a channel code for
the channel induced by the inner code, and hence the network
management algorithms should try to maximize the capacity of the
channel induced by the inner code. A network utility maximization
problem [Dong20] for BATS coding can be applied to study routing,
congestion control and media access control jointly. Compared with
the network utility maximization for Internet, there are two major
differences. First, the network flow rate is not measured by the
rate of the raw packets. Instead, a rank based measurement induced
by the inner code is applied for BATS coding schemes. Second, due to
recoding, the raw packet rate of a flow may not be the same for
different links, i.e., no flow conservation for BATS coding schemes.
These differences affect both the objective and the constraints of
the utility maximization problem.
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Practical congestion control, routing and media access control
algorithms for BATS coding schemes deserve more research efforts.
Due to the recoding operation, congestion control cannot be only
performed end-to-end. The rate of transmitting batches can be
controlled end-to-end, but the number of recoded packets generated
for a batch must be controlled at the intermediate nodes, which
introduces new research issues for congestion control. For routing,
the BATS coding scheme is flexible for implementing multi-path data
transmission, and different batches can be transmitted on a different
path between a source node and a destination node. Under the
scenario of BATS coding schemes, media access control can have some
different considerations: Retransmission is not necessary, and a
reasonably high packet loss rate can be tolerated.
4.3. Application Related Issues
There are more research issues pertaining to different applications.
The reliable communication technique provided by BATS codes can be
used for a broad range of network communication scenarios. In
general, a BATS coding scheme is suitable for data delivery in
networks with multiple hops and unreliable links.
One class of typical application scenario is wireless mesh and ad hoc
networks [Toh02], including vehicular networks, wireless sensor
networks, smart-lamppost networks, etc. These networks are
characterized by a large number of network devices connected
wirelessly with each other without a centralized network
infrastructure. A BATS coding scheme is suitable for high data load
delivery in such networks without the requirement that the point-to-
point/one-hop communication is highly reliable. Therefore, employing
a BATS coding scheme can provide more freedom for media access
control, including power control so that the overall network
throughput can be improved.
Another typical application scenario of BATS coding schemes is
underwater acoustic networks [Sprea19], where the propagation delay
of acoustic waves in underwater can be as long as several seconds.
Due to the long delay, feedback based mechanisms become inefficient.
Moreover, point-to-point/one-hop underwater acoustic communication
(for both the forward and reverse directions) is highly unreliable.
Due to these reasons, traditional networking techinques developed for
radio and wireline networks cannot be directly applied to underwater
networks. As a BATS coding scheme does not rely on the feedback for
reliability communication and can tolerate highly unreliable links,
it makes a good candidate for developing data delivery protocols for
underwater acoustic networks.
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Last but not least, due to its capability of performing multi-source
multi-destination communications, a BATS coding scheme can be applied
in various content distribution scenarios. For example, a BATS
coding scheme can be a candidate for the erasure code used in the
liquid data networking framework [Byers20] of CCN (content centric
networking), and provides the extra benefit of network coding
[Zhang16].
5. IANA Considerations
This memo includes no request to IANA.
6. Security Considerations
Subsuming both Random Linear Network Codes (RLNC) and fountain codes,
BATS codes naturally inherit both their desirable capability of
offering confidentiality protection as well as their vulnerability
towards pollution attacks.
6.1. Provision of Confidentiality Protection
Since the transported messages are linearly combined with random
coefficients at each recoding node, it is statistically impossible to
recover the individual messages by capturing the coded messages at
any one or small number of nodes. As long as the coding matrices of
the transported messages cannot be fully recovered, any attempt of
decoding any particular symbol of the transported messages is
equivalent to random guessing [Bhattad05].
The threat towards confidentiality, however, also exists in the form
of eavesdropping on the initial encoding process, which takes place
at the encoding nodes. In these nodes, the transported data are
presented in plain text and can be read along their transfer paths.
Hence, information isolation between the encoding process and all
other user processes running on the node must be assured.
In addition, the authenticity and trustworthiness of the encoding,
recoding and decoding program running on all the nodes must be
attested by a trusted authority. Such a measure is also necessary in
countering pollution attacks.
6.2. Countermeasures against Pollution Attacks
Like all network codes, BATS codes are vulnerable under pollution
attacks. In these attacks, one or more compromised coding node(s)
can pollute the coded messages by injecting forged messages into the
coding network and thus prevent the receivers from recovering the
transported data correctly.
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The research community has long been investigating the use of various
signature schemes (including homomorphic signatures) to identify the
forged messages and stall the attacks (see [Zhao07], [Yu08],
[Agrawal09]). However, these countermeasures are regarded as being
too computationally expensive to be employed in broadband
communications. Hence, a system-level approach based on Trusted
Computing [TC-Wikipedia] is proposed as a practical alternative to
protect BATS codes against pollution attacks. This Trusted Computing
based protection consists of the following countermeasures:
1. Attestation and Validation of all BATS encoding, recoding and
decoding nodes in the network. Remote attestation and repetitive
validation of the identity and capability of these node based on
valid public key certificates with proper authorization MUST be a
pre-requisite for admitting these nodes to a network and
permitting them to remain on that network.
2. Attestation of all encoding, recoding and decoding programs used
in the coding nodes. All programs used to perform the BATS
encoding, recoding and decoding processes MUST be remotely
attested before they are permitted to run on any of the coding
nodes. Reloading or alteration of programs MUST NOT be permitted
during an encoding session. Programs MUST be attested or
validated again when they are executed in new execution
environments instantiated even in the same node.
3. Original Authentication of all coded messages using network level
security protocols such as IPsec or Peer Authentication over
session-based communication using transport level security
protocols such as TLS/DTLS MUST be employed in order to provide
Message Origin or Communication Peer Authentication to every
coded message sent through the coding network.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8682] Saito, M., Matsumoto, M., Roca, V., Ed., and E. Baccelli,
"TinyMT32 Pseudorandom Number Generator (PRNG)", RFC 8682,
DOI 10.17487/RFC8682, January 2020,
<https://www.rfc-editor.org/info/rfc8682>.
7.2. Informative References
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[Agrawal09]
Agrawal, S. and D. Boneh, "Homomorphic MACs: MAC-based
integrity for network coding", International Conference on
Applied Cryptography and Network Security , 2009.
[Bhattad05]
Bhattad, K. and K.R. Narayanan, "Weakly Secure Network
Coding", ISIT , 2007.
[Byers20] Byers, J.W. and M. Luby, "Liquid Data Networking", ICN ,
2020.
[Dong20] Dong, Y., Jin, S., Yang, S., and H.H.F. Yin, "Network
Utility Maximization for BATS Code enabled Multihop
Wireless Networks", ICC , 2020.
[Li03] Li, S.-Y.R.., Yeung, R.W., and N. Cai, "Linear Network
Coding", IEEE Transactions on Information Theory , 2003.
[RFC6330] Luby, M., Shokrollahi, A., Watson, M., Stockhammer, T.,
and L. Minder, "RaptorQ Forward Error Correction Scheme
for Object Delivery", RFC 6330, DOI 10.17487/RFC6330,
August 2011, <https://www.rfc-editor.org/info/rfc6330>.
[Sprea19] Sprea, N., Bashir, M., Truhachev, D., Srinivas, K.V.,
Schlegel, C., and C. Claudio Sacchi, "BATS Coding for
Underwater Acoustic Communication Networks", OCEANS ,
2019.
[TC-Wikipedia]
"Trusted Computing",
Wikipedia https://en.wikipedia.org/wiki/Trusted_Computing.
[Toh02] Toh, C.K., "Ad Hoc Mobile Wireless Networks", Prentice
Hall Publishers , 2002.
[Yang14] Yang, S. and R.W. Yeung, "Batched Sparse Codes", IEEE
Transactions on Information Theory 60(9), 5322-5346, 2014.
[Yang17] Yang, S. and R.W. Yeung, "BATS Codes: Theory and
Practice", Morgan & Claypool Publishers , 2017.
[Yin19] Yin, H.H.F., Tang, B., Ng, K.H., Yang, S., Wang, X., and
Q. Zhou, "A Unified Adaptive Recoding Framework for
Batched Network Coding", ISIT , 2019.
[Yin20] Yin, H.H.F., Yeung, R.W., and S. Yang, "A Protocol Design
Paradigm for Batched Sparse Codes", Entroy , 2020.
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[Yu08] Yu, Z., Wei, Y., Ramkumar, B., and Y. Guan, "An Efficient
Signature-Based Scheme for Securing Network Coding Against
Pollution Attacks", INFOCOM , 2008.
[Zhang16] Zhang, G. and Z. Xu, "Combing CCN with network coding: An
architectural perspective", Computer Networks , 2016.
[Zhao07] Zhao, F., Kalker, T., Medard, M., and K.J. Han,
"Signatures for content distribution with network coding",
ISIT , 2007.
Appendix A. Additional Stuff
Authors' Addresses
Shenghao Yang
CUHK(SZ)
Shenzhen
Guangdong,
China
Phone: +86 755 8427 3827
Email: shyang@cuhk.edu.cn
Xuan Huang
CUHK
Hong Kong
Hong Kong SAR,
China
Phone: +852 3943 8375
Email: 1155136647@link.cuhk.edu.hk
Raymond W. Yeung
CUHK
Hong Kong
Hong Kong SAR,
China
Phone: +852 3943 8375
Email: whyeung@ie.cuhk.edu.hk
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John K. Zao
NCTU
Hsinchu
Taiwan,
China
Email: jkzao@ieee.org
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