Acoustic Communication 15.5 Point-to-Point Links:Signal Processing 377
Part B | 15.5
total span must match that of the multipath. In other
words, respecting the physical aspects of acoustic propagation in developing a concise channel representation
is the key to successful signal processing. Identification
of significant channel components has attracted due interest in the acoustic community over the past several
years, and much attention has been devoted to the topic
of sparse channel estimation. [15.37] gives an excellent
primer.
Multicarrier Modulation/Detection
Multicarrier modulation in the form of orthogonal
frequency division multiplexing (OFDM) has been
adopted as a standard for many of the emerging wireless
radio systems – wireless local area networks (WLAN),
digital audio and video broadcast (DAB/DVB), and
long-term evolution that supports the fourth generation
(4G) and future generations of cellular systems. However, it has only recently come into the forefront of
acoustic communications research, and at the moment,
there appears to be little effort to implement this technology in a commercial modem (e.g., by the French
Thales).
The appeal of OFDM lies in the computational efficiency of FFT-based processing, and in the fact that
it easily scales to different bandwidths (unlike with
single-carrier systems, where the equalizer length has
to be adjusted in accordance with the bandwidth B
because it determines the symbol duration and hence
the extent of ISI, with OFDM it simply suffices to
increase/decrease the number of carriers K, i. e., the
size of the FFT, while keeping the same carrier separation f D B=K). Namely, the demodulated signal is
simply represented as
y k .n/ D H k .n/d k .n/ C z k .n/ ;
(15.27)
where d k .n/ is the data symbol transmitted on the
k-th carrier during the n-th OFDM block of duration T D 1=f ; H k .n/ is the channel transfer function
H.f ; t/ evaluated at the k-th carrier frequency at the
time of the n-th block, and z k .n/ is the noise. This
signal admits a very simple detector, and extensions
to multiple receiving elements are straightforward. In
addition, by virtue of having a narrowband signal on
each carrier, OFDM is easily conducive to MIMO processing [15.40, 62], adaptive modulation [15.63], and
differentially coherent detection [15.64]. However, its
sensitivity to frequency offset and time-variation of the
channel demands special attention. Issues related to
power efficiency also need to be kept in mind, as OFDM
is sensitive to nonlinear distortions [15.65].
OFDM signal processing encompasses two stages:
pre-FFT synchronization and post-FFT data detection. To account for motion-induced Doppler frequency
shifting, which can amount to more than a full carrier spacing, front-end resampling is often necessary.
A simple method for estimating the needed resampling
rate is to measure the time between two synchronization
preambles that frame several OFDM blocks and compare it to the expected frame duration [15.66].
Since the Doppler factor is relatively large to begin
with (e.g., on the order of 10
3 for a relative velocity
of 1:5 m=s), Doppler shifting that remains after initial resampling cannot be neglected. Two approaches
have been pursued to address this issue: one is based
on the assumption that residual Doppler shift is the
same for all carriers [15.66], while another allows for
nonuniform frequency shifting across the signal bandwidth [15.44].
Channel estimation for OFDM systems has been addressed in different forms: in one, each OFDM block
is processed independently of the other blocks, thus
allowing for the possibility that the channel changes
completely from one block to another [15.41, 62], while
another form exploits correlation between adjacent
blocks [15.40, 67], which makes it advantageous on
slowly varying channels. Similarly as in single-carrier
systems, accurate channel estimation is the key to successful data detection in OFDM, and it benefits greatly
from proper channel modeling to reduce the number of
unknown parameters that need to be estimated. Methods for identification of sparse systems, such as matching pursuit (MP) and basis pursuit (BP), which improve
upon traditional least squares (LS) estimation, were
found to be beneficial and well suited to channel estimation in acoustic OFDM. These methods have been
applied to both block-individual channel estimation that
uses pilot carriers only [15.41], and to block-adaptive,
decision-directed channel estimation [15.68].
The effect of time variability is a subtle one when
it comes to acoustic OFDM systems: if the channel
can change from one block to another, then perhaps
it will change within each block. If this variation is
nonnegligible, inter-carrier interference (ICI) will arise
at the demodulator output, and carrier-by-carrier detection will no longer be optimal. ICI equalization
then becomes necessary. Such a situation is somewhat
counter-productive, as it destroys the original motivation of using OFDM for the simplicity of its implementation. Nonetheless, it may arise from pushing the limits
of bandwidth efficiency: the more carriers in a given
bandwidth, the better the bandwidth utilization, but the
narrower the carrier spacing f D 1=T, the longer the
OFDM block duration T, and the channel variation may
become nonnegligible. The problem of ICI equalization
is analogous to that of ISI equalization in single-carrier
systems, except that the equalizer now operates across
Part B | 15.5
total span must match that of the multipath. In other
words, respecting the physical aspects of acoustic propagation in developing a concise channel representation
is the key to successful signal processing. Identification
of significant channel components has attracted due interest in the acoustic community over the past several
years, and much attention has been devoted to the topic
of sparse channel estimation. [15.37] gives an excellent
primer.
Multicarrier Modulation/Detection
Multicarrier modulation in the form of orthogonal
frequency division multiplexing (OFDM) has been
adopted as a standard for many of the emerging wireless
radio systems – wireless local area networks (WLAN),
digital audio and video broadcast (DAB/DVB), and
long-term evolution that supports the fourth generation
(4G) and future generations of cellular systems. However, it has only recently come into the forefront of
acoustic communications research, and at the moment,
there appears to be little effort to implement this technology in a commercial modem (e.g., by the French
Thales).
The appeal of OFDM lies in the computational efficiency of FFT-based processing, and in the fact that
it easily scales to different bandwidths (unlike with
single-carrier systems, where the equalizer length has
to be adjusted in accordance with the bandwidth B
because it determines the symbol duration and hence
the extent of ISI, with OFDM it simply suffices to
increase/decrease the number of carriers K, i. e., the
size of the FFT, while keeping the same carrier separation f D B=K). Namely, the demodulated signal is
simply represented as
y k .n/ D H k .n/d k .n/ C z k .n/ ;
(15.27)
where d k .n/ is the data symbol transmitted on the
k-th carrier during the n-th OFDM block of duration T D 1=f ; H k .n/ is the channel transfer function
H.f ; t/ evaluated at the k-th carrier frequency at the
time of the n-th block, and z k .n/ is the noise. This
signal admits a very simple detector, and extensions
to multiple receiving elements are straightforward. In
addition, by virtue of having a narrowband signal on
each carrier, OFDM is easily conducive to MIMO processing [15.40, 62], adaptive modulation [15.63], and
differentially coherent detection [15.64]. However, its
sensitivity to frequency offset and time-variation of the
channel demands special attention. Issues related to
power efficiency also need to be kept in mind, as OFDM
is sensitive to nonlinear distortions [15.65].
OFDM signal processing encompasses two stages:
pre-FFT synchronization and post-FFT data detection. To account for motion-induced Doppler frequency
shifting, which can amount to more than a full carrier spacing, front-end resampling is often necessary.
A simple method for estimating the needed resampling
rate is to measure the time between two synchronization
preambles that frame several OFDM blocks and compare it to the expected frame duration [15.66].
Since the Doppler factor is relatively large to begin
with (e.g., on the order of 10
3 for a relative velocity
of 1:5 m=s), Doppler shifting that remains after initial resampling cannot be neglected. Two approaches
have been pursued to address this issue: one is based
on the assumption that residual Doppler shift is the
same for all carriers [15.66], while another allows for
nonuniform frequency shifting across the signal bandwidth [15.44].
Channel estimation for OFDM systems has been addressed in different forms: in one, each OFDM block
is processed independently of the other blocks, thus
allowing for the possibility that the channel changes
completely from one block to another [15.41, 62], while
another form exploits correlation between adjacent
blocks [15.40, 67], which makes it advantageous on
slowly varying channels. Similarly as in single-carrier
systems, accurate channel estimation is the key to successful data detection in OFDM, and it benefits greatly
from proper channel modeling to reduce the number of
unknown parameters that need to be estimated. Methods for identification of sparse systems, such as matching pursuit (MP) and basis pursuit (BP), which improve
upon traditional least squares (LS) estimation, were
found to be beneficial and well suited to channel estimation in acoustic OFDM. These methods have been
applied to both block-individual channel estimation that
uses pilot carriers only [15.41], and to block-adaptive,
decision-directed channel estimation [15.68].
The effect of time variability is a subtle one when
it comes to acoustic OFDM systems: if the channel
can change from one block to another, then perhaps
it will change within each block. If this variation is
nonnegligible, inter-carrier interference (ICI) will arise
at the demodulator output, and carrier-by-carrier detection will no longer be optimal. ICI equalization
then becomes necessary. Such a situation is somewhat
counter-productive, as it destroys the original motivation of using OFDM for the simplicity of its implementation. Nonetheless, it may arise from pushing the limits
of bandwidth efficiency: the more carriers in a given
bandwidth, the better the bandwidth utilization, but the
narrower the carrier spacing f D 1=T, the longer the
OFDM block duration T, and the channel variation may
become nonnegligible. The problem of ICI equalization
is analogous to that of ISI equalization in single-carrier
systems, except that the equalizer now operates across