On packet lengths and overhead for random linear
coding over the erasure channel
Brooke Shrader and Anthony Ephremides
Electrical and Computer Engineering Dept
and Institute for Systems Research
University of Maryland
College Park, MD 20742
bshrader, etony@umd.edu
Abstract— We assess the practicality of random network cod-
ing by illuminating the issue of overhead and considering it in
conjunction with increasingly long packets sent over the erasure
channel. We show that the transmission of increasingly long
packets, consisting of either of an increasing number of symbols
per packet or an increasing symbol alphabet size, results in
a data rate approaching zero over the erasure channel. This
result is due to an erasure probability that increases with packet
length. Numerical results for a particular modulation scheme
demonstrate a data rate of approximately zero for a large,
but finite-length packet. Our results suggest a reduction in the
performance gains offered by random network coding.
I. INTRODUCTION
It has been shown that network coding allows a source node
to multicast information at a rate approaching the maximum
flow of a network as the symbol alphabet size approaches
infinity [1], [2]. An infinitely large symbol alphabet would
correspond to transmission of infinitely long blocks of data,
which is neither possible nor practical. Subsequent works
have shown that a finite alphabet is sufficient to achieve the
maximum flow and a number of works have provided bounds
on the necessary alphabet size. For example, in [3] the alphabet
size is upper bounded by the product of the number of sources
with the number of receivers, while in [4] a lower bound on
the alphabet size is given by the number of receivers.
Random linear coding was proposed to allow for distributed
implementation [5]. In random network coding, a randomly
generated encoding vector is typically communicated to the
receiver by appending it to the header of the transmitted
packet. The overhead inherent in communicating the encoding
vector becomes negligible as the number of symbols in the
packet grows large. Regarding the alphabet size for random
network coding, a lower bound of 2 times the number of
receivers is given in [4].
The “length” of a transmitted packet (i.e., the number of bits
conveyed by the packet) depends on both the symbol alphabet
and the number of symbols per packet. From the previous
works listed above, it is clear that transmitting sufficiently long
packets is crucial to ensuring the existence of network codes
and to allowing random linear coding to operate with low
overhead.
However, long packets are more susceptible to noise, inter-
ference, congestion, and other adverse channel effects. This
point has been ignored in previous works on network coding.
In particular, a number of previous works [6]–[8] consider
transmission over an erasure channel, in which a packet
is either dropped with a probability ǫ or received without
error. In a wireline network, the erasure channel is used
to model dropped packets due to buffer overflows. Clearly,
longer packets take up more space in memory, so the erasure
probability will increase as the packet length grows. In the
case of a wireless network, there are a number of reasons that
the erasure probability will increase with the length of the
packet, as listed below.
• If we try to fit more bits into the channel using mod-
ulation, then for fixed transmission power, points in the
signal constellation will move closer together and errors
are more likely.
• If we try to fit more bits into the channel by decreasing
symbol duration, then we are constrained by bandwidth,
a carefully-controlled resource in wireless systems.
• Longer packets are more susceptible to the effects of
fading.
In this work, we model the erasure probability as a func-
tion of the packet length and investigate the implications on
network coding performance. This is somewhat reminiscent
of [9], in which the erasure probability is a function of the
link distance in a wireless network, and the effect on capacity
is investigated. Our emphasis will be on a wireless channel
with a fixed bandwidth, for which we will associate erasure
probability with the probability of symbol error for a given
modulation scheme.
We note that a careful examination of packet lengths in data
networks is not a new idea; most notably, many researchers
participated in a dispute over the packet size for the Asyn-
chronous Transfer Mode (ATM) standard in the 1980s. And
still many tradeoffs are being studied today at the network
level regarding the packet length. The interplay between packet
lengths and coding arises because of the way that network
coding unifies different layers of the protocol stack. Another
work which examines a similar problem is [10], in which
packet headers and packet lengths are analyzed for Reed-
Solomon coding in video applications.
http://arxiv.org/abs/0704.0831v1
II. THROUGHPUT OF RANDOM LINEAR CODING
We consider the following setting. A source node has K
units of information {s1, s2, . . . , sK} that it wants to transmit.
We will refer to each of these information units as packets and
let each packet be given by a vector of n q-ary symbols, where
q is the symbol alphabet (the size of a finite field) and n is
the number of symbols per packet. We consider values of q
which are powers of 2, i.e., q = 2u for some u ≥ 1. The
length of a packet is given by n log2 q bits. In Section III we
will consider the case where {s1, s2, . . . , sK} are packets of
uncoded information, whereas in Section IV we will assume
that a (deterministic) error-correcting code has been applied
in order to form {s1, s2, . . . , sK}.
The source generates random linear combinations by form-
ing the modulo-q sum
i=1 αisi, where αi are chosen ran-
domly and uniformly from the set {0, 1, 2, . . . , q − 1}. Note
that the resulting random linear combination is a packet with
length n log2 q bits. With each random linear combination
transmitted, the source appends a packet header identifying
αi, which requires an additional K log2 q bits of overhead
with every n log2 q bits transmitted. A receiver will collect
these random linear combinations until it has received enough
to decode. For transmission over an erasure channel, decoding
can be performed by Gaussian elimination. Let N denote the
number of random linear combinations needed for decoding.
Each of the N random linear combinations represents a linear
equation in {s1, s2, . . . , sK} and the distribution of N is given
Pr(N≤j)=Pr{a random K×j matrix has rank K}.
Note that Pr(N ≤ j) is equal to zero for j < K . For j ≥ K ,
the distribution can be found following the procedure in [11]
and is given by
Pr(N≤j) = (q
j−1)(qj−q)(qj−q2) . . . (qj−qK−1)
(1− q−j+i)
The expected value of N can be found from the above
distribution and is shown to be given by
E[N ] = K +
(1− q−j+i)
(1− q−i)−1 (1)
Clearly, as q → ∞, E[N ] → K .
We let ǫ(n log2 q) denote the erasure probability on the
channel, which is an increasing function of the packet length
n log2 q. The expected number of transmissions needed for the
receiver to decode the original K packets is given by
E[N ]
1− ǫ(n log2 q)
Over the course of these transmissions, the average number of
packets received for each transmission is
K(1− ǫ(n log2 q))
E[N ]
We account for the overhead by scaling the number of packets
received per transmission by n/(n+K), which is the ratio of
number of information symbols to the total number of symbols
(information plus overhead) sent with each transmission. We
define S as the effective portion of each transmission which
contains message information; S is a measure of throughput
in packets per transmission and takes values between 0 and 1.
E[N ]
(1− ǫ(n log2 q)) (2)
If the erasure probability is constant, ǫ(n log2 q) = e, then for
n, q → ∞, corresponding to infinitely long packets, the value
of S approaches 1− e, which is the Shannon capacity of the
erasure channel.
From the expression in (2), we can identify a tradeoff
between the packet length and the throughput. As the number
of information symbols per packet n grows large, the effect of
overhead becomes negligible, but the erasure probability grows
and the transmissions are more likely to fail. Alternatively, if
n approaches zero, transmissions are more likely to succeed,
but the overwhelming amount of overhead means that no
information can be transmitted. In a similar manner, as the
alphabet size q grows, the random linear coding becomes
more efficient in the sense that E[N ] → K , but again, the
erasure probability increases and transmissions are likely to
be unsuccessful. This tradeoff demonstrates that the alphabet
size, packet length, and overhead must be carefully weighed
in determining the performance of random linear coding over
the erasure channel.
III. PERFORMANCE WITHOUT PRE-CODING
In this section we consider the case where {s1, s2, . . . , sK}
are uncoded information symbols. We define the data rate in
bits per transmission as R, where
R = Sn log2 q. (3)
The data rate R accounts for the fact that each received
packet contains n log2 q bits of information. Since the packets
{s1, s2, . . . , sK} consist of uncoded information, for every
random linear combination sent, all n symbols must be re-
ceived without error. Then
Pr(erasure) = 1− (1− Pr(symbol error))n
and Pr(symbol error) will correspond to the symbol error
probability for q-ary modulation over the channel. We denote
the probability of symbol error by Pq and note that it is
independent of n but depends on q as well as features of
the channel such as pathloss, signal-to-noise ratio (SNR), and
fading effects. We will consider a wireless channel of limited
bandwidth, for which modulation techniques such as pulse
amplitude modulation (PAM), phase shift keying (PSK), and
0 500 1000 1500 2000
Symbols per packet, n
0 500 1000 1500 2000
Fig. 1. Throughput S (solid line) and data rate R (dotted line) versus symbols
per packet n without pre-coding for QAM modulation, K = 80, q = 8, and
SNR per bit γb=3.5dB.
quadrature amplitude modulation (QAM) are appropriate. For
these modulation techniques, Pq → 1 as q → ∞ [12].
In this setting, the erasure probability is given by
1− ǫ(n log2 q) = (1− Pq)n. (4)
From the above expression we note that, as suggested by
our discussion on the tradeoff between throughput and packet
length, as n → ∞, both S and R approach zero exponentially
fast. Additionally, for the modulation schemes mentioned
above, S and R approach zero as q → ∞, albeit at a slower
rate.
As a numerical example, we have plotted the throughput S
and data rate R for QAM modulation over an additive white
Gaussian noise (AWGN) channel. In this case the symbol error
probability for the optimum detector is approximated by [12]
Pq ≈ 1−
3γb log2 q
q − 1
where γb is the SNR per bit and Q is the complementary
cumulative distribution function for the Gaussian distribution.
The above expression for Pq holds with equality for log2 q
even. The results are shown in Figure 1, where q is fixed
and n is varied, and in Figure 2, where n is fixed and q is
varied. In all cases, the throughput and data rate are concave
functions, admitting optimum values for n and q. Furthermore,
if n grows large as q is fixed or if q grows large as n is fixed,
the throughput approaches zero.
To improve the performance described above, a number of
techniques might be employed, including enhanced receivers,
diversity techniques, and channel coding.
IV. PERFORMANCE WITH PRE-CODING
In this section we examine the effect of using a deter-
ministic pre-code prior to performing random linear coding.
We assume that an uncoded packet σi, consisting of k q-
ary information symbols, is passed through an encoder to
1 1.5 2 2.5 3 3.5 4 4.5 5
Symbol alphabet size, log
1 1.5 2 2.5 3 3.5 4 4.5 5
Fig. 2. Throughput S (solid line) and data rate R (dotted line) versus log of
the symbol alphabet log
q for QAM modulation, K = 80, n = 200, SNR
per bit γb=3.5dB, and no pre-coding.
add redundancy, producing the packet si consisting of n q-
ary symbols. Thus a q-ary code of rate Rpc = k/n is
used to produce {s1, s2, . . . , sK}. Random linear coding is
again performed on {s1, s2, . . . , sK} and we assume that the
codebook for the pre-code is known to the decoder and does
not need to be transmitted over the channel. At the receiver,
decoding of the random linear code will be performed first
using the coefficients αi, i = 1, 2, . . . ,K sent with each
random linear combination. Subsequently the pre-code will
be decoded. The use of the pre-code means that the system
will be tolerant to some errors in the n symbols sent in each
random linear combination.
To account for the pre-code, we modify the throughput S
expressed in (2) by multiplying it be Rpc, which results in
E[N ]
(1 − ǫ(n log2 q)). (6)
To obtain the data rate R in bits per transmission, S is now
scaled by k, the number of information symbols present in
each packet.
R = Sk log2 q (7)
We can bound the erasure probability, and thus the through-
put S and data rate R, using bounds on the error-correcting
capabilities of the pre-code. Let t denote the number of
symbol errors that the pre-code can correct. For a given code,
t = ⌊1/2(dmin − 1)⌋ errors, where dmin is the minimum
distance of the code. The erasure probability can be bounded
as follows.
1− ǫ(n log2 q)
i=t+1
Pr(i symbols in error)
i=t+1
P iq(1− Pq)n−i
⌊1/2(d−1)⌋
P iq(1− Pq)n−i
In the above expression, (a) holds with equality for the class
of perfect codes [12], (b) follows by assuming that errors
are independent, identically distributed ∼ Pq among the n
symbols, and (c) holds for d ≤ dmin. We make use of the
Gilbert-Varshamov bound to provide a lower bound on dmin
as follows.
d = inf
dmin :
dmin−2
(q − 1)i ≥ qn−k
Lower bounds on the throughput and data rate are given as
follows.
SLB =
E[N ]
⌊1/2(d−1)⌋
P iq(1− Pq)n−i (9)
RLB = SLBk log2 q (10)
For q → ∞ while all other parameters are fixed, if the
modulation scheme has Pq → 1, then SLB, RLB → 0. In
the limit of increasing symbols per packet n, we identify two
different cases. First, if the pre-code rate Rpc is fixed (i.e,
k → ∞ as n → ∞ with fixed ratio between the two) then
SLB → Rpc (multiplied by K/E[N ] ≈ 1) and RLB increases
without bound. On the other hand, if k is fixed (i.e., as n → ∞,
Rpc → 0) then SLB, RLB → 0.
We have computed numerical examples of SLB and RLB
assuming QAM modulation with Pq as given in (5). In Fig.
3 we display the throughput and data rate as a function of q.
Clearly, pre-coding allows the system to support higher q with
non-zero throughput, although the throughput does eventually
go to zero for large q. In comparing this result to Fig. 2,
note that all other parameters are fixed and the pre-code rate
Rpc = 1/2 results in a halving of the throughput and data
rate.
In Fig. 4 we have displayed the two different cases for
performance as a function of increasing symbols per packet
n. In Fig. 4(a), the rate Rpc is fixed at 1/2 and SLB → Rpc
while RLB increases without bound. In this case, pre-coding
ensures that the throughput and data rate increase with n.
However, there is a caveat: the result in Fig. 4(a) requires that
k → ∞, which means that the source must have an infinite
supply of uncoded information to be transmitted. On the other
hand, if the source has a finite amount of information to be
transmitted, as shown in Fig. 4(b), then as n → ∞, the pre-
code rate Rpc → 0 and the data rate also approaches zero.
In summary, the use of a pre-code can improve performance
on the erasure channel; indeed, pre-coding is the mechanism
which allows us to represent a noisy channel as an erasure
channel. However, pre-coding can only ensure that the data
rate increases with packet length if there is an infinite supply
of (uncoded) information at the source. This is a strong
assumption for a network, where data often arrives in bursts.
V. DISCUSSION
The primary contribution of this work is the elucidation
of the fact that the widely-used assumption of the erasure
probability being constant as the packet length increases leads
1 2 3 4 5 6
Symbol alphabet size, log
1 2 3 4 5 6
Fig. 3. Bounds on the throughput SLB (solid line) and data rate RLB
(dotted line) versus log of the symbol alphabet log
q for QAM modulation,
K = 80, n = 200, k = 100 (pre-code rate 1/2), and SNR per bit γb=3.5dB.
to idealized performance that cannot be obtained in a practical
scenario. We have shown that the assumption of increasingly
long packets, either due to increasingly many symbols per
packet or to an increasing alphabet size, can result in a
data rate of zero for random linear coding if the erasure
probability increases with packet length. While the use of
a pre-code prior to performing random linear coding can
improve performance, it can only ensure that the throughput
increases with packet length if there is an infinite supply
of data awaiting transmission at the source. We have also
demonstrated that the overhead needed for random linear
coding can have a devastating impact on the throughput. We
have focused our attention on the erasure channel, but the
tradeoff between packet length and throughput will arise in
other channels, particularly in wireless channels where these
effects can be exacerbated by fading. A number of techniques,
such as pre-coding, can be employed to combat the adverse
effects of the channel, but in making use of these techniques,
the performance gains offered by network coding will be
tempered.
ACKNOWLEDGMENT
This work is supported by the Office of Naval Research
through grant N000140610065, by the Department of Defense
under MURI grant S0176941, and by NSF grant CNS0626620.
Prepared through collaborative participation in the Commu-
nications and Networks Consortium sponsored by the U.S.
Army Research Laboratory under The Collaborative Technol-
ogy Alliance Program, Cooperative Agreement DAAD19-01-
2-0011. The U.S. Government is authorized to reproduce and
distribute reprints for Government purposes notwithstanding
any copyright notation thereon. The views and conclusions
contained in this document are those of the authors and should
not be interpreted as representing the official policies, either
expressed or implied, of the Army Research Laboratory or the
U. S. Government.
0 500 1000 1500 2000 2500
Symbols per packet, n
0 500 1000 1500 2000 2500
(a) Fixed rate pre-code Rpc = 1/2.
400 600 800 1000 1200 1400 1600 1800 2000 2200
Symbols per packet, n
400 600 800 1000 1200 1400 1600 1800 2000 2200
(b) Variable pre-code rate, k = 400.
Fig. 4. Bounds on the throughput SLB (solid line) and data rate RLB (dotted line) versus symbols per packet n for QAM modulation, K = 80, q = 8,
and SNR per bit γb=3.5dB.
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	Introduction
	Throughput of random linear coding
	Performance without pre-coding
	Performance with pre-coding
	Discussion
	References

  We assess the practicality of random network coding by illuminating the issue
of overhead and considering it in conjunction with increasingly long packets
sent over the erasure channel. We show that the transmission of increasingly
long packets, consisting of either of an increasing number of symbols per
packet or an increasing symbol alphabet size, results in a data rate
approaching zero over the erasure channel. This result is due to an erasure
probability that increases with packet length. Numerical results for a
particular modulation scheme demonstrate a data rate of approximately zero for
a large, but finite-length packet. Our results suggest a reduction in the
performance gains offered by random network coding.

Introduction
	Throughput of random linear coding
	Performance without pre-coding
	Performance with pre-coding
	Discussion
	References
