RPG/ACP/NSP WP 3
International Civil Aviation Organization
Regional Preparatory Group (RPG) Meeting for World Radiocommunication
Conference 2007 (WRC-2007), ACP Working Group B and F and NSP SSG
Meetings.
Bangkok, Thailand, 21-25 February 2005.
.
Agenda Item: 1 (WRC Agenda item 1.6)
WIDEBAND WIRELESS CHANNEL CHARACTERIZATION
OF THE
MLS BAND (5000-5150 MHZ)
STATUS REPORT
Larry Foore
NASA Glenn Research Center
United States of America
David W. Matolak
Ohio University
United States of America
Summary
Under the Space Based Technologies (SBT) project (informally known as ACAST), the
NASA Glenn Research Center is sponsoring a channel sounding effort proposed by Dr.
David Matolak of Ohio University. The effort plans to characterize the 5000-5150 MHz
band (with emphasis on the MLS extension band 5091-5150 MHz) in the form of a set of
statistical channel models, parameterized appropriately for the area(s) of interest (surface and
potentially terminal areas). A proper statistical characterization of the channel is not only
necessary for optimal waveform design, but for the evaluation of existing potential
waveforms in these areas.
This report is being presented to the International Civil Aviation Organization (ICAO) WG-F
to inform the aviation community of the progress made toward properly evaluating this
channel for wireless wideband signaling. Included are photographs of the test equipment and
current facilities, a brief description of the test plan, and example preliminary measurement
results.
Background
The ACAST project at the NASA Glenn Research Center has committed to supporting the
FAA in providing research and technical support for the efficient use and protection of
aviation spectrum. Through industry support functions such as the I-CNS 2004 Conference
and the ACAST Workshop 2004, NASA has identified the protection of the 5000-5150 MHz
band (with emphasis on the MLS extension band 5091-5150 MHz) for aviation use as one of
the top priorities for the ACAST project.
In consideration of WRC-07 Agenda item 1.6, NASA (in consultation with the FAA) has
decided to evaluate the use of the MLS extension band for short range, wideband signaling in
the surface area environment. This channel will be thoroughly characterized and understood
in order to provide a proper evaluation of any particular system or waveform. There have
been some academic efforts concerning aeronautical channel modeling [1], which rely
heavily on land-mobile channel models for surface area environments. However, much of
the terrestrial research is concerned primarily with the statistical characterization of path-loss
in relation to land and urbanization variations. The multipath associated with the airport
surface environment has the potential to differ significantly from channel models developed
exclusively for land mobile (cellular) communications, due to the wideband nature of the
signals being considered.
In an effort to properly characterize the use of the MLS extension band for wideband
signaling, the NASA Glenn Research center has sponsored a grant proposal created by Dr.
David Matolak of Ohio University. Dr. Matolak has organized a research effort that will
create a parametric, stochastic model of the surface area channel for the MLS extension band
[2,3]. The model development process relies heavily on empirical data, which is obtained
from channel sounding. Various transmit/receive positions, surface variations, and weather
conditions will be used to develop a collection of channel impulse responses (CIRs). This
data will be categorized, parameterized, and properly analyzed to develop the channel
models.
Wideband Channel Sounding
Wideband channel characterization varies significantly from narrowband characterization,
especially in potentially rich multipath environments such as the airport surface. Timedelayed reflections of the transmitted signal arrive at the receiver with varying amplitudes
and potentially different Doppler shifts. For wideband signals, as signal bandwidth becomes
larger there is a greater potential for the behavior of individual spectral components to
become uncorrelated due to phase shifts along reflected paths. This results in frequencyselective fading. The bandwidth over which all frequency components are affected similarly
is termed coherence or correlation bandwidth [4].
In the time domain, the time-delayed echoes of the transmitted signal overlap at the receiver
causing intersymbol interference (ISI). Without corrective measures via channel equalization
techniques, the delay spread sets the lower bound on error rate for a specified data rate [4].
This delay spread, coupled with Doppler shifts, must be properly characterized for a given
channel for waveform design/analysis.
Much of the theoretical foundation for classifying randomly time-variant (fading) linear
channels was developed in a classical paper by Bello [5]. Bello’s work describes dispersive
channels in a statistical sense, bringing together the consideration of both random time delays
and random Doppler shifts into system transfer functions. Bello also showed the relationship
between various classifications of dispersive channels, namely the Wide Sense Stationary
(WSS), Uncorrelated Scatterer (US), WSSUS, and Quasi-WSSUS channels.
Parsons’ text does an excellent job of explaining how this theoretical development relates to
practical channel sounding and classification [4]. By assuming that a dispersive channel is
stationary over short intervals of time, Parsons shows how the assumption of WSSUS can be
applied to obtain the autocorrelation function of the channel output from the time distribution
of received power, or the power-delay profile.
A relatively recent paper published by Bultitude [6] discusses some pitfalls and
recommended practices when describing a radio channel with regards to empirical data
collected via channel sounding techniques. More specifically, Bultitude demonstrates how to
ensure the assumptions of stationarity and ergodicity from empirical data, as well as how to
correctly estimate a channel’s frequency correlation function from this data. Ultimately, as
Bultitude points out, the correlation bandwidth sets the lower bound for error performance.
From the theory set forth by Bello, Parsons has clearly explained how the theory has practical
application in wideband channel characterization. Bultitude provides guidance beyond this
by demonstrating actual data processing for the examples of Rayleigh and Rician fading
channels.
Dr. Matolak has constructed a plan to collect the empirical data necessary to follow the
suggested path to wideband channel characterization in the MLS extension band. This status
report discusses the equipment being used and how it relates to the theory discussed.
Additionally, sample power-delay profiles are discussed from preliminary indoor trials.
Modified Berkeley Varitronics Raptor Model Channel Sounding System
The sounding system utilized for data collection in this effort is a modified Raptor model
developed by Berkley Varitronics Systems. The standard model of the Raptor was developed
primarily for indoor multipath and propagation analysis in the range of 2.4-2.85 GHz. The
modified model being used for the sounding effort has translated the output signal and
corresponding tuned receiver to the MLS band.
In the simplest sense, the transmitter portion of this system outputs an oversampled PN
sequence that is BPSK modulated. This signal is filtered via a 128 stage FIR to produce the
desired baseband signal mask. This wideband signal is then converted up to the desired
frequency range. The maximum output power of the Raptor transmitter is approximately 33
dBm, which is relative to the types of short range, wideband signaling that may be used in
surface area networks.
The received signal, after proper down conversion and IF filtering, is I/Q demodulated and
fed to a bank of digital correlators. These correlators allow the receiver to search for the
multipath components and identify Doppler shifts to produce power-delay profiles.
The desired output for this effort is in the form of power-delay profiles that are saved offline
and post-processed via Matlab routines. These routines will produce the important channel
parameters (with consideration of Bultitude’s work [6]), such as the correlation bandwidth,
coherence time, the RMS delay spread, etc.
Figure 1 displays the Raptor receive and transmit units side-by-side.
Receiver
Transmitter
Figure 1 - Raptor Channel Sounding System (Left – Receiver, Right – Transmitter)
The following figure displays the Raptor transmit configuration as seen in the lab. Note that
the transmitter is connected to an omni antenna. These antennas have an approximate 2 dBi
gain.
Omni
Transmitter
Figure 2 - Raptor Transmitter and Omni Antenna
The following figure displays the Raptor receive configuration as seen in the lab.
Omni
Data Logger
Receiver
Figure 3 - Raptor Receiver, Omni Antenna, and Data Logging Laptop
Sample Output
As previously mentioned, the desired output from the receiver comes in the form of powerdelay profiles. The power-delay data will be post processed to determine important channel
parameters such as the correlation bandwidth, coherence time, and RMS delay spread.
The following two plots are examples of power-delay profiles obtained with the Raptor
system. These data sets were obtained from indoor testing, so the measured values of delay
spread are expectedly small.
Figure 4 - Power-Delay Profile, Indoor, Minimal Multipath
Figure 5 - Power-Delay Profile, Indoor, Observable Multipath
The first plot (Fig. 4) displays a strong Line-of-Sight (LOS) component, with some correlated
power received afterwards. There was no significant multipath component from this
collection.
The second plot (Fig. 5) shows not only a primary component, but a secondary echo that is 56 dB down from the primary. This demonstrates the Raptor’s capability to discern multipath
components and is preliminary to testing on Ohio University’s airport surface.
Going Forward
Over the course of the remainder of 2005, Dr. Matolak in conjunction with NASA and the
FAA, plans to sound the surface areas of at least 3 major U.S. airports. A sounding campaign
is planned to begin at the Cleveland Hopkins International Airport at the end of February,
2005. Ohio University’s own local airport, as well as (potentially) the NASA Glenn
Research Center campus, will be used as a testing ground for procedures.
Ultimately, the development of the database and parametric models will aid in the evaluation
of existing system performance in the MLS band or the design of an optimal waveform for
the band. For example, existing waveforms may require additional channel equalization for
optimal performance pending the degree of delay spread experienced in this channel.
Correspondingly, an optimal bandwidth will be identified for the design of a waveform
specific to this channel.
Recommendations
This channel characterization effort will contribute to efforts set forth to address Agenda Item
1.6 of the WRC-07. The data collected during these sounding campaigns will allow not only
for the development of wideband channel models, but also narrowband channel models of the
type that may be used in surface sensor collection networks (fixed, directional point-to-point
links).
NASA respectively recommends that this work be considered as part of the investigation to
develop requirements for aeronautical systems in the MLS extension band (note Resolution
414 of WRC-03). Particularly, this work may influence the re-characterization of this band
for the use of AM(R)S systems to support next-generation aeronautical communication
systems on the airport surface.
References
[1] M. Haas, “Aeronautical Channel Modeling,” IEEE Trans. on Vehicular Tech., vol. 51, no.
2, pp. 254-264, March 2002.
[2] D. W. Matolak, “Wireless Channel Characterization: Overview and Application to 5 GHz
Band Airport Surface/Terminal Environments,” Ohio University Report for NASA Glenn
Research Center, May 2004.
[3] D. W. Matolak, “5 GHz Wireless Channel Characterization for Airport Surface Areas:
Project Workplan and Approximate Schedule,” Ohio University Report for NASA Glenn
Research Center, August 2004.
[4] J. D. Parsons, The Mobile Radio Propagation Channel, 2nd ed., John Wiley & Sons, New
York, NY, 2000.
[5] P. A. Bello, “Characterization of Randomly Time-Variant Linear Channels”, IEEE Trans.
on Comm. Sys., CS11, pp. 360-393, December 1963.
[6] R. J. C. Bultitude, “Estimating Frequency Correlation Functions From Propagation
Measurements on Fading Radio Channels: A Critical Review”, IEEE Journal on Selected
Areas in Comm., vol. 20, no. 6, pp.1133-1143, August 2002