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Abstract— A major limiting factor in increasing the
throughput of wireless networks has been the bottleneck of
prohibitive signaling overhead. A number of header
compression schemes have been proposed to solve this
particular problem. These, however, come with their set of
limitations and suffer from a loss of practicality in certain
cases. Our contribution in this paper is two-fold; firstly, we
propose a practical framework for cross-layer header
compression which improves vastly on existing data rates (by
more than 25% of raw throughput at high SNRs). This is
achieved by applying independent compression algorithms to
both MAC and PHY data units, thus ensuring better
throughput. Secondly, we analyze the performance of proposed
scheme in slow fading as well as fast fading environments to
demonstrate robustness.
Index Terms— 802.11, source coding, header compression,
throughput enhancement.
I. INTRODUCTION
The most valuable resource in any wireless communication
system is the bandwidth available. Driven by user’s
demands for ever-increasing data rates, many sophisticated
algorithms and optimization models have emerged over the
last few years for wireless links especially WLANs. These
methods include popular approaches such as link adaptation,
beamforming and switching between spatial diversity and
multiplexing in MIMO systems. Most of these techniques
aim at maximizing the utilization of the physical link in
some way. This approach, while intuitively sound, suffers
from a number of technical shortcomings.
Firstly, it has been established in [1] that using the current
MAC/PHY header specifications places an upper bound of
less than 100 Mbps regardless of any optimization on the
PHY layer Transmitter (Tx) characteristics.
Secondly, with the ever-increasing complexity of
optimization algorithms, the overhead necessary for
reasonable receiver functionality increases proportionally. A
very pertinent example of this second limitation is seen in
modern WLANs. Despite employing OFDM as the primary
multiplexing technique, the 802.11 family of standards [7]
(and especially the 802.11n version [6]) employ link
adaptation algorithm on a per-packet basis instead of on a
per-subcarrier basis. This is the perfect limiting case where
“too much overhead” proves prohibitive for the optimization
algorithm.
Hussain Syed Kazmi is a researcher with the Image Processing Centre,
National University of Sciences and Technology, Pakistan.
Haroon Raja is a post graduate student and researcher at Core
Communications and Networks Laboratory, School of Electrical
Engineering and Computer Science, National University of Sciences and
Technology, Pakistan
To solve these two problems, a number of schemes have
been proposed both for the case of WLANs as well as other
wireless networks. These include, but are not limited to,
frame aggregation [1] and header compression algorithms
such as Robust Header Compression (ROHC) [2]. Whereas
aggregation is specialized for WLANs, ROHC is employed
for TCP/IP/UDP header compression. Furthermore,
aggregation based techniques do not cater for the PHY layer
header and robustness is generally not guaranteed beyond
certain aggregated packet lengths so that while the raw
throughputs might be increasing, the goodput might not be
following a similar trend.
The approach we adopt in this work is two-pronged; it
provides an algorithm that caters for robust compression of
both MAC and PHY header information thus enhancing the
overall goodput of WLANs. A Doppler shift dependent
entropy coded scheme is presented for efficient compression
of the PHY header while a simple stateful approach based
on mitigating redundant information suffices for robust
compression of MAC header. The simultaneous use of these
two compression techniques results in significant
performance gains.
The structure of the paper is as follows. Section 2 presents a
formal overview of the system model under consideration;
the numerous variables involved and their dependencies are
also highlighted. A brief review of existing techniques is
presented in section 3 before elaborating the proposed
algorithm in Section 4. Section 5 analyses and compares
results of both the proposed and existing schemes prior to
conclusion in section 6.
II. SYSTEM MODEL
Overall system is consisting of two distinct parts;
communication system used for transmission and the
compressor portion that has been proposed in this paper.
A. Communication Model
For our performance analysis purposes, we are assuming
a rate-adaptive 802.11n WLAN [6] using the MIMO-OFDM
framework which will employ diversity model for better
reliability (2x2 MIMO in diversity mode is used). The rate
adaptation is carried out by means of a simple MCS
(modulation and coding scheme) vs. SNR lookup table
which has been developed by plotting SNR vs. BER curves
in MATLAB as is done in previous works like [3].
The channel is assumed to be a slow fading Rayleigh
model. This is based on the typical workplace scenario
where users set-up their workstations and work for a couple
of hours instead of wandering about randomly. Based on
this starting point, we define a few independent and
Throughput Enhancement by Cross-layer
Header Compression in WLANs
Hussain Syed Kazmi and Haroon Raja
2010 16th Asia-Pacific Conference on Communications (APCC)
978-1-4244-8129-3/10/$26.00 ©2010 IEEE 329
dependent variables in the system. The Doppler Spread (or
alternatively the coherence time of the channel) is one such
independent variable; the average SNR of the
communication link is another. The SNR at the receiving
node, packet length used by the transmitter, the channel
correlation between successive packets, the mode of the
transmitted packet and the type of compression scheme
being employed are all dependent variables on the other
hand. Doppler spread has strong negative correlation with
the channel state between successive packets and
consequently the received SNR [4].
Figure: System Structure
B. Compressor Design
There are 2 distinct stages of the compressor:
1. MAC layer header compressor
The MAC layer compressor is essentially a context based
stateful compression algorithm that is designed to remove
the redundancy in header information.
2. PHY layer header compressor
The PHY compression algorithm aims at exploiting the
similarity measure of the PHY header between consecutive
packets based on a correlation measure of the Doppler
spread and the channel condition. Broadly speaking, based
on the difference between corresponding PHY header fields
on successive packets, an entropy coding scheme is applied.
Lempel-Ziv coding [9] and a number of its variants perform
inefficiently due to the relatively small length of the PHY
header [8]. By employing Adaptive Huffman coding, a
periodically updating probability function of the repetition
bits to calculate the correlation model, we can arrive at a
reliable probability and resultant code tree.
It has been shown in [5, 8] that for Huffman coding the
difference between optimal bit length per code word ( ) and
estimated bit length ( ) will be:
(1)
Where is the error in the ith estimated probability with
reference to the true probability and is given by:
and are the optimal and estimated code word lengths
whereas and are the average number of bits per code
word for the case of estimated and true probabilities, given
by:
The first term in (1) reduces to approximately zero for
practical values of implying that the initial estimate
of probabilities is reasonable. In this case, the equation
reduces to:
By extending this result further using Lagrange
multipliers and assuming the mean squared error in the
probability estimate as the variance, σ2, ΔL can be shown to
be the product of the variance in and the variance of for
the worst case implying there is a loss of optimality caused
by imperfect estimates [8]. For the best case ΔL can also
equal 0. In light of these conclusions, we have designed the
compressor in a way that it employs adaptive Huffman
coding. This mitigates the worst case scenario by employing
an initial estimated standard entropy chart which the
algorithm updates after 10 packets on both communicating
nodes to minimize any false probability estimates.
The possibility of the occurrence of a fast fading channel
is catered for by estimating the Doppler spread at both ends
using a measure of the channel coherence time. If the
Doppler Spread exceeds a certain value, PHY header
compression is not employed. For the time being, this
threshold has been set at 100 Hz based on heuristic
evidence. Further optimization is possible by making this
threshold adaptive as well.
III. A REVIEW OF EXISTING TECHNIQUE – ROHC
Roughly speaking, ROHC [2] mechanism works as follows:
on one side, the compressor treats the packets to remove
redundancy using a context that is built from the information
observed in the past packet headers. On the other side, the
decoder creates its context from the received packets and
uses it to reconstruct the header of the incoming packet.
ROHC acts on upper level layers i.e. the network and
transport layer headers.
Compressor and decompressor contexts have to be
synchronized, otherwise, decompression will fail and the
decompressor will drop the packets until it can re-build its
context, meaning that packets not corrupted during
transmission will be dropped because its header cannot be
reconstructed. The decompressor re-builds its context
through the application of a repair mechanism. If context is
not repaired, then it will notify the compressor through
feedback messages.
IV. PROPOSED ALGORITHM FOR HEADER COMPRESSION
Header compression can be defined as a technique that
optimizes bit allocation by exploiting the redundancy in the
header information both within the same packet header and
also, more importantly, between consecutive packets
belonging to the same packet stream. This technique is
intended to deal with the bandwidth constraints by reducing
the header size. Our proposed compression scheme is two-
fold; attempting to remove the redundancy in both the PHY
as well as MAC header. These 2 techniques are elaborated
in the following sections:
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A. MAC layer header compression
The 802.11n standard defines the MAC frame format as:
Figure [6]: MAC Protocol Data Unit (MPDU) Frame format
1) For Infrastructure based networks
For the case of the infrastructure based WLANs, the
MAC header compression becomes a trivial problem since a
major portion of the redundancy (MAC headers) can be
removed from the packet by using a scheme similar to the
Network Address Translation (NAT) at the Access Point
(AP) or the router. The AP assigns a short identifier instead
of the 4 MAC addresses when the initial connection is
established between the station (STA) and the AP. The QoS
(Quality of Service) control field can also be similarly
compressed after the initial handshake since the QoS
requirements of a link remain the same over the length of a
session. So for this purpose, after the initial handshake the
AP assigns the user 4 network identifiers which must be
compliant with:
(Translated identifier)x = 2*n; n € {N<24}, x ϵ
1,2,3,4}
Where n belongs to N which is the set of all natural
numbers smaller than 24; this ensures that the translated
identifiers combine to form some multiple of the basic unit,
the byte, while also guaranteeing that the translating
identifier does not exceed the original MAC address
allocations (the probability of this happening in a practical
WLAN approaches zero). x is defined as the set containing
values 1 through 4 which refer to the source, destination, AP
at the source end and AP at the destination end respectively.
2) For ad-hoc networks
It is possible for the STAs in an ad-hoc network to
establish the short identifier for themselves at the initial
handshake based on a somewhat similar formula i.e.
(Translated identifier)x = 4*n; n ϵ {N<12}, x ϵ {1,2}
In this case, x is defined as the set containing values 1
through 2 which refer to the source and destination only
since there is no infrastructure in place for the need of AP
addresses to arise. The change of 2 to 4 in the equation
multiplier keeps the equation compliant with byte
specifications. The QoS specifications can be dealt with in a
similar way.
B. PHY layer header compression
The PHY compression algorithm is same and equally
robust for both ad-hoc and infrastructure based wireless
communication links. The technique consists of the
following general steps:
1. Transmit initial 10 packets without any
compression on the PHY header. Assuming that the
link is bidirectional and the receiver transmits an
ACK based on the CRC check on the received
packet, both ends estimate the channels SNR and
the rate of its variation. From the variations of the
channel, the Doppler spread can be estimated.
a. The maximum Doppler spread can be
calculated as a simple reciprocal function of
the coherence time of the channel which is
estimated from the received signals. Details on
calculation of coherence time are present in
[4]. A simplified form of this expression can
be given as:
Doppler Spread, Ds = (1/Tc)
Where Tc is defined as the channel coherence time.
b. After the transmission and reception of
every PHY header the difference vector D
is computed at the transmitter and the
receiver respectively which is defined as:
D = (PHY_Hdr(1:32))n .– (PHY_Hdr(1:32))n-1
Where (PHY_Hdr)n is defined as the PHY layer header
of the nth packet while (PHY_Hdr)n-1 is the previous packet
and is defined as the bitwise subtraction operator. The
difference vector D is thus defined as the bitwise difference
of the first 32 bits of 2 adjacent PHY headers. The D vector
has therefore dimensions of 1x32 (excluding the last 16 bits
of the header which are the CRC and tail bits of each
physical header.) If the Doppler spread does not limit the
functionality of the communication link, then the
information obtained in (1) can be used to determine the
transmit parameters (such as MCS, spatial streams etc.) If
the Doppler Spread is indeed above a certain threshold and
is limiting the robustness of the algorithm then the
compression algorithm is not implemented. The D matrix is
however computed continually to update statistics. D matrix
is assumed to be always positive since the receiver can
verify the CRC of the PHY header to determine what was
the original bit sequence sent between the ambiguous cases.
The D matrix is divided into 4 separate fields.
a. The first 7 bits which are the MCS field.
b. The next bit is the bandwidth indicator bit;
this bit will not be compressed.
c. The next 16 bits are the complete packet
length.
d. Next 10 bits include support for
information such as switching between
MIMO modes, aggregation, sounding and
coding techniques etc.
2. The fields of the D vector are used to update the
adaptive entropy coding function E(Dx) (where x ϵ
{1,2,3} and is the value of the field of the
difference matrix) on both ends such that for every
count of a particular value of a field of the Dx
vector, weight is added to that particular branch of
the entropy coding tree for that particular field.
Once the initial 10 packets have been transmitted,
the updated tree is used to code the 3 fields of the
D vector individually for subsequent transmissions
and receptions such that
Transmitted PHY_Hdr = E(Dx) xϵ{1,2,3}
The function E(Dx) continues to update its branch weights
using the fields of the D vector.
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V. PERFORMANCE ANALYSIS AND COMPARISON
A. Logical Intuition behind gain by PHY header
compression
As the standard states that MCS (modulation and coding
scheme employed for the transmitted packet) 0 through 15
are mandatory in 20 MHz with 800 ns guard interval at an
access point (AP) while MCS 0 through 7 are mandatory in
20 MHz with 800 ns guard interval at all STAs. All other
MCSs and modes are optional, specifically including Tx
(transmit) and Rx (receive) support of 400 ns guard interval,
operation in 40 MHz, and support of MCSs with indices 16
through 76. Most of the systems are employing only the
mandatory MCS 0 through 7, which are coded using 3 bits.
The standard, however allocates 7 bits for each MCS. This
tightening up of wasted bits might seem as trivial in the
grander scheme of things but optimal allocation of bits using
the proposed entropy coding scheme significant
performance gains have been observed. It stands to reason
that for very high data rate WLANs, each and every bit
counts.
1) Theoretical Illustration of gain by compressing MAC
header
a) Best case scenario: Infrastructure based WLAN
with almost static channel
1. Original header bytes = approx. 42 (36 MAC + 6
PHY), compressed header bytes = 18 (13 MAC + 3
PHY), header size reduction = approx. 60%
2. Payload size in bytes = 300, total packet size = 342
bytes, after compression total size = 318 bytes, so
overall sustained improvement = at least. 7%.
b) Worst case scenario: ad-hoc WLAN with fast
fading channel
1. Original header bytes = approx. 30 (24 MAC + 6
PHY), compressed header bytes = 18, header size
reduction = 40%
2. Payload size in bytes = 1000, total packet size =
1030 bytes, after compression total size = 1018
bytes, so overall sustained improvement = at least
1%.
B. Simulation Results
The results from figure show that significant throughput
gain is achieved by employing the proposed coding
techniques when compared with state-of-the art packet
based LA algorithm without compression. Indeed
throughput gains in the region of 25% of original raw
throughput are possible at high SNR’s (for packet lengths of
300 bytes) under the conditions of a slow fading channel
with a Doppler spread less than our prescribed threshold
(100 Hz) The algorithm, however, performs admirably even
in high Doppler spread environments by still providing
considerable gains. This is due to the fact that the transmitter
refrains from compressing the PHY header when the
channel is fast fading i.e. channel correlation between
successive packets is weak. The MAC layer coding is still
performed since it only removes redundant information that
depends on the session and not the channel. This results in
an overall increase in the data rates (and intuitively proves
to also reduce the packet error rate somewhat since the total
number of OFDM symbols transmitted has decreased).
An important fact to glean from figure here is that the gains
only start appearing when the link adaption algorithm shifts
to higher modulation schemes such as 16QAM and 64QAM.
Figure 1: Throughput comparison
This is due to the fact that the compressor is essentially
reducing the number of OFDM symbols the Tx has to
transmit over the wireless medium. As seen from figure, the
lower modulation schemes use so many OFDM symbols for
transmission of data that reduction by a small fraction
causes no gain in throughputs. However at the higher end of
the modulation spectrum, the same reduction results in very
significant gains.
Figure 2: Comparison of No. of OFDM symbols required to transmit 2.5Mb
of user data
For our analysis purposes, the last figure we present is a
comparison of the fraction of overheads incurred in the 3
different cases of a) no compression applied, b) compression
applied in a slow fading channel and c) compression applied
in a fast fading channel. It is evident that there is a very
significant decrease in the signaling overhead incurred in the
system without sacrificing data rates which is what we had
originally set out to do. In addition, the abscissa being a
measure of the data rates, illustrates another important
relationship. The data rates are plotted according to the
results obtained from earlier calculations along a range of 0
to 30dB and tend to cluster rather tightly at lower data rates.
This is due to the fact that data rates start increasing
dramatically only after a certain SNR threshold is reached.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20 25 30
No
rmal
ized
Th
rou
ghp
ut
SNR (dB)
With
Compression
(Doppler
Spread
50Hz)With
Compression
(Doppler
Spread
250Hz)Without
Compression
(Packet
based LA)
332
Figure 3: Comparison of fraction of header overhead
VI. CONCLUSION AND FUTURE EXTENSION
To conclude, the proposed algorithm has proved to
significantly improve throughputs by compressing signaling
overhead while proving highly robust at the same time by
setting a static cut-off threshold for the PHY layer
compression. Future extensions of the work include
generalizing of the proposed framework to multiuser
systems as well as incorporating a dynamic threshold for
PHY compression by employing a history based learning
algorithm.
REFERENCES
[1] Youngsoo Kim, Sunghyun Choi, Kyunghun Jang, Hyosun
Hwang.“Throughput Enhancement of IEEE 802.11 WLAN via
Frame Aggregation”, in Proc. of IEEE Vehicular Technology
Conference (VTC)-Fall, pp. 3030 - 3034, Sept. 2004.
[2] C. Bormann, Ed. 2001. RObust Header Compression (ROHC):
Framework and four profiles: RTP, UDP, ESP, and
uncompressed. RFC 3095. IETF Network Working Group.
[3] Yaser Pourmohammadi Fallah, Panos Nasiopoulos and Hussein
Alnuweiri, “Efficient Transmission of H.264 Video over
Multirate IEEE 802.11e WLANs”, EURASIP Journal
onWireless Communications and Networking Volume 2008.
[4] A. Goldsmith, Wireless Communications. Cambridge, UK:
Cambridge University Press, 2005.
[5] D. A. Huffman, “A Method for the Construction of Minimum-
Redundancy Codes”, Proceedings of the IRE, Vol. 4D, pp.
1098-1101, Sept. 1952.
[6] Local and metropolitan area networks requirements part 11:
Wireless LAN medium access control (MAC) and physical layer
(PHY), Feb 2007.IEEE Standards Association. [7] IEEE, Part 11: Wireless LAN Medium Access Control (MAC)
and Physical Layer (PHY) Specifications. IEEE Std 802.11-
1999, Aug. 1999. [8] Richard B. Wells, “Applied Coding and Information Theory for
Engineers” Pearson Prentice Hall Publication. [9] J. Ziv and A. Lempel “Compression of individual sequences via
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