Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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WIRELESS COMMUNICATIONS DEVICE INCLUDING A JOINT
DEMODULATION FILTER FOR CO-CHANNEL INTERFERENCE
REDUCTION AND RELATED METHODS
Field of the Invention
The present invention relates to wireless communications systems, such as
cellular
communications systems, and, more particularly, to filtering received wireless
signals to
reduce unwanted interference.
Background
Cellular communications systems continue to grow in popularity and have become
an integral part of both personal and business communications. Cellular
telephones allow
users to place and receive voice calls most anywhere they travel. However,
with ever
increasing numbers of cellular phone users comes greater challenges for
wireless
communications device and network providers. One such challenge is addressing
interference caused between multiple cellular devices operating in a given
geographical
area. Cellular devices communicate with a cellular base station using common
or shared
wireless communications channels (i.e., frequencies). Yet, in some cases
signals between
other devices and a base station using the same channel may cause a desired
signal from
the base station to be significantly degraded or even dropped by the handheld
device. Such
interference is called co-channel interference.
Because of the increasing load on cellular communications infrastructures,
various
single-antenna interference cancellation (SAIC) approaches have been
investigated to
meet requirements for Downlink Advanced Receiver Performance (DARP). This
effort is
being standardized by the third generation mobile communications system and
the Third
Generation Partnership Project (3GPP).
One SAIC technique that has been investigated is based upon joint demodulation
of the desired and interfering sequences. Generally speaking, this approach
begins with a
standard least-squares (LS) estimate of the propagation channel and a static
channel
profile for the interferer. Then, a modified Viterbi decoder is used in which
half of the
state bits represent the user sequence and the other half represent the
interferer. A joint
branch metric is minimized and the estimated sequences for the desired and
interfering
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signal are used in a least mean squares (LMS) algorithm to update the channel
estimates
for both the desired and interfering propagation channel.
The 3GPP initiative has given consideration to the application of joint
demodulation in synchronized wireless networks. See, e.g., "Feasibility Study
on Single
Antenna Interference Cancellation (SAIC) for GSM Networks," 3GPP TR 45.903
Version
6Ø1, Release 6, European Telecommunications Standards Institute, 2004. This
is the
more limited case that requires one to assume that the base station
synchronization data
sequences (i.e., training sequences) of the desired-signal and dominant-
interferer overlap,
which in turn makes the estimation of the CIRs possible using previously known
techniques. It also requires one to assume that the interferer will be
dominant for the entire
burst.
However, in asynchronous network applications the training sequences of
interfering signals may not overlap those of the desired signal, which makes
CIR
estimation problematic. Accordingly, further developments may be desirable to
make joint
demodulation techniques practical to implement in both synchronous and
asynchronous
networks.
Brief Description of the Drawings
FIG. I is a schematic block diagram of an exemplary Single Antenna
Interference
Cancellation (SAIC) enabled joint demodulation Global System for Mobile
Communication (GSM) receiver in accordance with the present invention.
FIG. 2 is a schematic block diagram of an exemplary embodiment of the joint
demodulation receiver of FIG. 1 shown in greater detail.
FIG. 3 is a graph of simulated performance results for an SAIC joint
demodulation
receiver in accordance with the present invention and a typical GMSK receiver
in
accordance with the prior art.
FIG. 4 is a flow diagram of an exemplary joint demodulation filtering method
for
reducing co-channel interference between a desired signal and a co-channel
interfering
signal in accordance with the invention.
FIG. 5 is a schematic block diagram of an exemplary wireless communication
device in which the joint demodulation receiver of FIG. I may be used.
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Detailed Description of the Preferred Embodiments
The present description is made with reference to the accompanying drawings,
in
which preferred embodiments are shown. However, many different embodiments may
be
used, and thus the description should not be construed as limited to the
embodiments set
forth herein. Rather, these embodiments are provided so that this disclosure
will be
thorough and complete. Like numbers refer to like elements throughout.
Generally speaking, a wireless communications device including a housing and a
wireless transmitter and a wireless receiver carried by the housing is
described herein. In
particular, the wireless receiver may include a joint demodulation filter for
reducing co-
channel interference between a desired signal and a co-channel interfering
signal. The
joint demodulation filter may include an input receiving samples of the
desired signal and
the co-channel interfering signal, a Viterbi decoder, and a first signal path
between the
input and the Viterbi decoder comprising a first filter. The joint
demodulation filter may
further include a second signal path between the input and the Viterbi decoder
and
comprising a linear finite impulse response (FIR) modeler for generating a
channel
impulse response estimate for the co-channel interfering signal. Additionally,
a third signal
path may be between the input and the Viterbi decoder and include a whitened
matched
filter for generating a channel impulse response estimate for the desired
signal.
More particularly, the desired signal and the co-channel interfering signal
may
each include a training sequence, and the joint demodulation filter may
further include a
training-sequence locator upstream of the second and third paths and
downstream from the
input. Additionally, the third signal path may include a desired-signal
channel impulse
response (CIR) estimator upstream of the whitened matched filter for
generating a desired-
signal CIR estimate. Furthermore, the first filter may be a first finite
impulse response
(FIR) filter.
The second signal path may include a first summer and a second summer
connected downstream therefrom. Moreover, the second signal path may further
include a
remodulator between the desired-signal CIR estimator and the first summer and
cooperating therewith for subtracting a remodulated desired-signal training
sequence from
samples of the desired signal and the co-channel interfering signal to thereby
generate an
interference signal estimate. In addition, the linear FIR modeler may include
a blind
interference and CIR estimator, and a second FIR filter downstream from the
blind
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interference and CIR estimator. The Viterbi decoder may also iteratively build
a tree of
interferer bit sequence hypotheses.
A joint demodulation filtering method for reducing co-channel interference
between a desired signal and a co-channel interfering signal in a wireless
communications
receiver may include filtering receiving samples of the desired signal and the
co-channel
interfering signal using a first signal path comprising a first filter. The
method may further
include generating a channel impulse response estimate for the co-channel
interfering
signal using a second signal path comprising a linear finite impulse response
(FIR)
modeler, and generating a channel impulse response estimate for the desired
signal using a
third signal path comprising a whitened matched filter. In addition, a
decoding operation
may be performed based upon the filtered received samples of the desired
signal and the
co-channel interfering signal, the channel impulse response estimate for the
co-channel
interfering signal, and the channel impulse response estimate for the desired
signal using a
Viterbi decoder.
Turning first to FIGS. 1 and 2, a joint demodulation filter 10 in accordance
with an
exemplary embodiment illustratively includes an input 11 receiving samples of
a desired
signal and a co-channel interfering signal, e.g., from the antenna of a
wireless
communications device (e.g., a mobile cellular device). That is, the joint
demodulation
filter 10 may advantageously be implemented in a wireless receiver of a mobile
wireless
communications device. The various components of the joint demodulation filter
10 may
be implemented using software modules and a processing circuitry, such as a
digital signal
processor (DSP), for example, although other implementations are also
possible, as will be
appreciated by those skilled in the art. Exemplary components of a mobile
cellular device
in which the joint demodulation filter 10 may be used will be discussed
further below with
reference to FIG. 5.
The joint demodulation filter 10 further illustratively includes a Viterbi
decoder 30,
and a first signal path 12 between the input 11 and the Viterbi decoder
comprising a first
filter 46. In the exemplary embodiment shown in FIG. 2, the first filter 46
may be a finite
infinite response (FIR) filter, such as a matched filter, for example. Also, a
second signal
path 13 is included between the input 11 and the Viterbi decoder 30. The
second signal
path 13 illustratively includes a linear FIR modeler 15 for generating a
channel impulse
response estimate for the co-channel interfering signal. Additionally, a third
signal path 14
is illustratively connected between the input 11 and the Viterbi decoder 30.
The third
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signal branch illustratively includes a whitened matched filter 44 for
generating a channel
impulse response estimate for the desired signal, as will be discussed further
below.
Additional components of the exemplary joint demodulation filter 10
illustrated in
FIG. 2 will now be briefly identified, followed by a description of the
various functions
thereof. As noted above, in a cellular communications GSM-based network, for
example,
a desired signal and a co-channel interfering signal will each include a
training sequence.
The joint demodulation filter 10 illustratively includes a training-sequence
locator 20 for
the desired signal upstream of the second and third paths 13, 14 and
downstream from the
input 11. The third signal path 14 illustratively includes a desired-signal
CIR estimator 22
upstream of the whitened matched filter 44 for generating a desired-signal CIR
estimate.
The second signal path 13 also illustratively includes a first summer 26, a
second
summer 34 connected downstream from the first summer, and a remodulator 24
between
the desired-signal CIR estimator 22 and the first summer and cooperating
therewith for
subtracting a remodulated desired-signal training sequence from samples of the
desired
signal and the co-channel interfering signal to thereby generate an
interference signal
estimate. The linear FIR modeler 15 illustratively includes a blind
interference and CIR
estimator 28, coupled to the summer 26, and a second FIR filter 42 downstream
from the
blind interference and CIR estimator 28, which also receives an input from the
whitened
matched filter 44. The second summer 34 also receives an output of the blind
interference
and CIR estimator 28, as shown.
The second signal path 13 further illustratively includes a residual noise
power
(Pn) sample offset block 32 between the first and second summers 26, 34, a
significant
interferer component (Pif) sample offset block downstream from the second
summer, and
a Pif/Pn decision block 38 downstream from the Pif sample offset block, as
will be
discussed further below. A mixer 40 is downstream from the Pif sample offset
block 38
and also receives an output of the second FIR filter 42 as shown. The output
of the mixer
40 and the output of the whitened matched filter 44 are provided to the
Viterbi decoder 30,
as is the output of the first FIR 46.
The operation of the joint demodulation receiver 10 will now be described in
further detail. As noted above, the joint demodulation (JD) receiver 10 may
advantageously be used in wireless communications systems, such as in cellular
base
stations and mobile cellular communications devices, for example. Generally
speaking,
joint demodulation uses estimates for a channel impulse response (CIR) for a
desired
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signal and a dominant interferer associated therewith. For a GSM
implementation, which
will be discussed below, it will be assumed that the dominant interferer is a
GMSK
modulated signal conforming to the GSM specification.
The joint demodulation approach set forth herein may be applicable to both
synchronized and unsynchronized networks, in that this technique uses "blind"
interferer
data and channel estimation techniques rather than making the above-noted
assumptions.
Once the CIRs have been estimated, a two-dimensional (joint) adaptive Viterbi
state
structure may be used in the equalizer to estimate the data for both the
desired signal and
the interferer.
Simulations of the present joint demodulation technique have demonstrated
greater
than 10 dB carrier-to-interference(C/I) improvement at about 0 dB C/I in the
raw symbol
error rate and frame error rate for 12.2-rate AMR FS speech. In the
simulations, a new
joint-least-squares based technique was used for channel-offset positioning
and desired
and interferer CIR estimation. As noted above, this approach is coupled with
blind
estimation of the interferer data (i.e., with no a-priori knowledge of the
interferer's data).
The present joint demodulation approach may be particularly advantageous in
its
ability to provide relatively high gains (i.e., in its ability to receive at
very low signal-to-
noise ratios (SNRs)) when limited a-priori knowledge about the interferer is
available, as
will be discussed further below. Yet, the Viterbi algorithm (VA) complexity
may also
increase, (depending on the number of states used to model the interferer),
thus the
processing requirements and the additional complexity of the channel/data
estimators may
be a factor in some software or hardware implementations.
For the test configuration, a system level Block Error Rate (BLER) simulator
was
extended to support all of the interferer models/scenarios being used by the
3GPP DARP
work group. This extension also allows new interferer models to be developed
as needed.
The simulations were performed using Matlab.
The joint demodulation approach assumes that the dominant interference
component may be modeled as the noisy output of a finite-impulse-response
(FIR)
(unknown) filter with unknown, binary, random input (interferer) data. In the
case of a
dominant GMSK-modulated interferer, this assumption holds even if there are
additional,
weaker interference signals present, which are treated as residual noise.
Moreover, this
approach may be applied to other interferer modulation types using the above
modeling
assumption.
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Referring again to FIG. 2, the steps associated with the joint demodulation
approach are as follows. First, a base station training sequence (TS) for the
desired signal
is found (Block 20), the CIR for the desired signal is estimated (Block 22),
and the re-
modulated desired training sequence is removed from the input samples to form
the
interferer-signal estimate (Block 24). Furthermore, the "blind" estimation of
the interferer
CIR and data is performed based upon the interferer-signal estimate, at Blocks
26, 28.
Next, a joint least-squares desired/interferer channel estimation using the
desired training
sequence and estimated interferer data is performed at Block 30, as will be
discussed
further below.
In addition, the foregoing steps may be repeated (or performed in a vectorized
form) at multiple input sample offsets (as the timing offset varies). As such,
the offset
yielding the minimal residual noise power (Pn) may be selected, and a
determination may
be made as to whether the model applies (i.e., was a significant interferer
component (Pif)
detected or not), at Blocks 32, 34, 36, and 38. If so, demodulation is
performed using a
joint-demodulation (multi-dimensional state) Viterbi algorithm that estimates
and removes
the interference jointly with the estimation of the desired-signal data (Block
30).
Initially, the desired channel impulse response was estimated using a
conventional
training-sequence correlation (i.e., "channel-sounding") method, as will be
appreciated by
those skilled in the art. At low C/I levels, the least-squares method provides
the initial
desired channel impulse response estimate by multiplying the input samples by
a constant
(pre-computed) matrix (AHA)''AH, where A is the training-sequence convolution
matrix of
the desired signal.
For estimating the interferer, the above-noted SAIC Feasibility Study assumes
a
synchronous network model. More particularly, this model assumes that the
training
sequence of the interfering signal is aligned with the desired signal's
training sequence
within a -1 to +4 symbol offset. In this case, the interferer channel impulse
response can
be estimated using the training-sequence correlation technique (or least
squares, since the
training-sequence data is known) after removing the desired signal's (re-
modulated)
training sequence from the received samples.
However, to widen the potential applicability of the joint-demodulation
approach
to the asynchronous network case where the interferer data during the desired
signal's
training sequence is unknown, blind channel and data estimation and
demodulation
techniques are used. By way of background in this regard, reference is made to
the article
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by Seshadri entitled "Joint Data and Channel Estimation Using Blind Trellis
Search
Techniques," IEEE Trans. on Communications, vol. 42, no. 2/3/4, pgs. 1000-
1011, and
the article by Daneshgaran et al. entitled "Blind Estimation of Output Labels
of SIMO
Channels Based on a Novel Clustering Algorithm," IEEE Communications Letters,
vol. 2,
no. 11, November 1998, pgs. 307-309.
One particular difficulty of performing blind interferer estimation is the
very small
number of "observable" interferer (i.e., noisy) samples during the desired
signal's training-
sequence window. By way of reference, the sequence window is the length of the
desired
training sequence (for this embodiment, the training sequence length is 26, as
defined by
the GSM 05-series standards) less the desired signal's CIR length (5 is chosen
by this
simulation, however other values between I and 7 are possible depending on the
channel
models as defined by the GSM standards) plus one, or: 26 - 5 + 1= 22 (twenty-
two) in the
present example.
This approach uses an algorithm which combines concepts of vector quantization
and sequential decoding of convolutional codes. The algorithm is based on two
assumptions: (1) the interferer signal may be modeled with a linear Finite
Impulse
Response (FIR) source (Block 28); and (2) the interferer signal is corrupted
by residual
additive white (i.e., uncorrelated) Gaussian noise (after removing the
estimated desired
signal) (FIG. 1, 26).
With these two assumptions, the algorithm iteratively builds a tree of
interferer bit
sequence hypotheses. For each new bit added to a bit sequence hypothesis, it
computes the
new FIR state (or codebook index, as will be apparent to those skilled in the
art of vector
quantization) and averages all input samples corresponding to the same state
in a particular
sequence to estimate the FIR output (codebook value) for that state. The
distortion of a bit
sequence is what remains after removing the sequence's FIR outputs from the
input
samples (FIG. 2, 36). After keeping up to W (search width parameter) bit
sequences with
the lowest distortions, each sequence is extended by another 0/1 bit to yield
two new
sequences (2W total), and the process of re-estimating FIR outputs of each
sequence is
repeated followed by keeping the W sequences with minimum distortion (e.g.,
one-half the
sequences). When the sequence length reaches the number of interferer-signal
samples
available (22 for this embodiment, as described above), the sequence with the
lowest
distortion out of W candidates is chosen.
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This above-described algorithm provides the initial interferer data and
channel
impulse response estimates for subsequent joint least-squares desired-signal
and interferer-
channel estimation. At C/I levels below 5dB, the CIR position (offset), and
CIR value
estimation for the desired and interferer is affected by the cross-correlation
of the desired
and interferer data sequences. However, using the previously obtained
interferer data
estimate, a joint least-squares channel estimation is possible that removes
(i.e., accounts
for) this cross-correlation as follows:
A' A'A B'A h
s= ,
B AB BBg
where s contains the input samples during the desired training-sequence window
(26-5+1=22 as described previously), A (NxLh) and B (NxLg) are the desired-
signal and
interferer data-sequence convolution matrices (A is known and constant, B is
an estimate
for the interferer), and h and g are the desired-signal and interferer CIRs
respectively that
result from solving the above equations with Lh (5 in this embodiment) the
length of h,
and Lg (3 chosen for this embodiment) the length of g.
Once estimates of the desired and interferer channel impulse responses are
available, a two-dimensional state Viterbi algorithm may be applied. For a
Euclidean
distance metric, the whitened discrete time model filter (WMF) is computed
from the
estimated desired CIR (Block 44). The computation is also applied to the
interferer CIR,
and the three (Lg) largest resulting taps are used to form the interferer
codebook (i.e., a set
of possible interferer channel FIR outputs). Of course, other numbers of taps
Lh and Lg
may also be used in some embodiments.
The resulting desired-signal and interferer codebooks are passed to the joint-
demodulation Viterbi algorithm. The returned soft-decision metrics include the
forward
and backward recursion using the difference of the odd/even state minimum
metrics at
each stage (not path) as the soft decision value and sign.
Turning now to FIG. 3, simulated results for TCH-AFS 12.2 rate speech for a
typical urban fading profile at 50km vehicle speeds (TU-50) at the 1950MHz
band without
the use of frequency hopping and using interferer model DTSl are shown, as
will be
appreciated by those skilled in the art. C/I is the average carrier-to-
interference ratio.
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The dotted lines 50 and 51 represent the SER (symbol error rate) and FER
(frame
error rates) of the conventional GMSK receiver. The dashed lines 53 and 54
represent the
performance of the above-described SAIC-JD receiver. The solid lines 55 and 56
represent
the performance of a higher-complexity SAIC-JD receiver in accordance with an
exemplary embodiment of the invention in which the blind vector quantization
of the
interferer is performed using recursive least squares (RLS) updates while the
interferer
symbol sequence hypotheses are formed and evaluated. As will be appreciated by
those
skilled in the art, the performance plot demonstrates that both of the SAIC-JD
receivers
provide significant improvement over the conventional receiver in a high
interference
environment.
The amount of residual "noise" power remaining in the desired signal's
training-
sequence window after removing the desired (i.e., estimated) samples may be
used as a
test of model "fit" in some embodiments. If removing the subsequently
estimated
interferer does not reduce the residual power significantly, a non-
interference signal model
may be selected, and vice-versa.
A joint demodulation filtering method for reducing co-channel interference
between a desired signal and a co-channel interfering signal will now be
described with
reference to FIG. 4. Beginning at Block 60, receiving samples of the desired
signal and the
co-channel interfering signal are filtered using a first signal path 12
comprising a first
filter 46, at Block 61. The method may further include generating a channel
impulse
response estimate for the co-channel interfering signal using a second signal
path 13
comprising a linear finite impulse response (FIR) modeler 15, at Block 62, and
generating
a channel impulse response estimate for the desired signal using a third
signal path 14
comprising a whitened matched filter 44, at Block 63. In addition, a decoding
operation
may be performed based upon the filtered received samples of the desired
signal and the
co-channel interfering signal, the channel impulse response estimate for the
co-channel
interfering signal, and the channel impulse response estimate for the desired
signal using a
Viterbi decoder 30, at Block 64, thus concluding the illustrated method (Block
65).
One example of a hand-held mobile wireless communications device 1000 that
may be used in accordance with the system 20 is further described in the
example below
with reference to FIG. 5. The device 1000 illustratively includes a housing
1200, a keypad
1400 and an output device 1600. The output device shown is a display 1600,
which is
preferably a full graphic LCD. Other types of output devices may alternatively
be utilized.
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A processing device 1800 is contained within the housing 1200 and is coupled
between
the keypad 1400 and the display 1600. The processing device 1800 controls the
operation
of the display 1600, as well as the overall operation of the mobile device
1000, in response
to actuation of keys on the keypad 1400 by the user.
The housing 1200 may be elongated vertically, or may take on other sizes and
shapes (including clamshell housing structures). The keypad may include a mode
selection
key, or other hardware or software for switching between text entry and
telephony entry.
In addition to the processing device 1800, other parts of the mobile device
1000 are
shown schematically in FIG. 5. These include a communications subsystem 1001;
a short-
range communications subsystem 1020; the keypad 1400 and the display 1600,
along with
other input/output devices 1060, 1080, 1100 and 1120; as well as memory
devices 1160,
1180 and various other device subsystems 1201. The mobile device 1000 is
preferably a
two-way RF communications device having voice and data communications
capabilities.
In addition, the mobile device 1000 preferably has the capability to
communicate with
other computer systems via the Internet.
Operating system software executed by the processing device 1800 is preferably
stored in a persistent store, such as the flash memory 1160, but may be stored
in other
types of memory devices, such as a read only memory (ROM) or similar storage
element.
In addition, system software, specific device applications, or parts thereof,
may be
temporarily loaded into a volatile store, such as the random access memory
(RAM) 1180.
Communications signals received by the mobile device may also be stored in the
RAM
1180.
The processing device 1800, in addition to its operating system functions,
enables
execution of software applications 1300A-1300N on the device 1000. A
predetermined set
of applications that control basic device operations, such as data and voice
communications 1300A and 1300B, may be installed on the device 1000 during
manufacture. In addition, a personal information manager (PIM) application may
be
installed during manufacture. The PIM is preferably capable of organizing and
managing
data items, such as e-mail, calendar events, voice mails, appointments, and
task items. The
PIM application is also preferably capable of sending and receiving data items
via a
wireless network 1401. Preferably, the PIM data items are seamlessly
integrated,
synchronized and updated via the wireless network 1401 with the device user's
corresponding data items stored or associated with a host computer system.
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Communication functions, including data and voice communications, are
performed through the communications subsystem 1001, and possibly through the
short-
range communications subsystem. The communications subsystem 1001 includes a
receiver 1500, a transmitter 1520, and one or more antennas 1540 and 1560. In
addition,
the communications subsystem 1001 also includes a processing module, such as a
digital
signal processor (DSP) 1580, and local oscillators (LOs) 1601. The specific
design and
implementation of the communications subsystem 1001 is dependent upon the
communications network in which the mobile device 1000 is intended to operate.
For
example, a mobile device 1000 may include a conununications subsystem 1001
designed
to operate with the MobitexTM, Data TACTM or General Packet Radio Service
(GPRS)
mobile data communications networks, and also designed to operate with any of
a variety
of voice communications networks, such as AMPS, TDMA, CDMA, WCDMA, PCS,
GSM, EDGE, etc. Other types of data and voice networks, both separate and
integrated,
may also be utilized with the mobile device 1000. The mobile device 1000 may
also be
compliant with other communications standards such as 3GSM, 3GPP, UMTS, etc.
Network access requirements vary depending upon the type of communication
system. For example, in the Mobitex and DataTAC networks, mobile devices are
registered on the network using a unique personal identification number or PIN
associated
with each device. In GPRS networks, however, network access is associated with
a
subscriber or user of a device. A GPRS device therefore requires a subscriber
identity
module, commonly referred to as a SIM card, in order to operate on a GPRS
network.
When required network registration or activation procedures have been
completed,
the mobile device 1000 may send and receive communications signals over the
communication network 1401. Signals received from the communications network
1401
by the antenna 1540 are routed to the receiver 1500, which provides for signal
amplification, frequency down conversion, filtering, channel selection, etc.,
and may also
provide analog to digital conversion. Analog-to-digital conversion of the
received signal
allows the DSP 1580 to perform more complex communications functions, such as
demodulation and decoding. In a similar manner, signals to be transmitted to
the network
1401 are processed (e.g. modulated and encoded) by the DSP 1580 and are then
provided
to the transmitter 1520 for digital to analog conversion, frequency up
conversion, filtering,
amplification and transmission to the communication network 1401 (or networks)
via the
antenna 1560.
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In addition to processing communications signals, the DSP 1580 provides for
control of the receiver 1500 and the transmitter 1520. For example, gains
applied to
communications signals in the receiver 1500 and transmitter 1520 may be
adaptively
controlled through automatic gain control algorithms implemented in the DSP
1580.
In a data communications mode, a received signal, such as a text message or
web
page download, is processed by the communications subsystem 1001 and is input
to the
processing device 1800. The received signal is then further processed by the
processing
device 1800 for an output to the display 1600, or alternatively to some other
auxiliary I/O
device 1060. A device user may also compose data items, such as e-mail
messages, using
the keypad 1400 andlor some other auxiliary I/O device 1060, such as a
touchpad, a rocker
switch, a thumb-wheel, or some other type of input device. The composed data
items may
then be transmitted over the communications network 1401 via the
communications
subsystem 1001.
In a voice communications mode, overall operation of the device is
substantially
similar to the data communications mode, except that received signals are
output to a
speaker 1100, and signals for transmission are generated by a microphone 1120.
Alternative voice or audio I/O subsystems, such as a voice message recording
subsystem,
may also be implemented on the device 1000. In addition, the display 1600 may
also be
utilized in voice communications mode, for example to display the identity of
a calling
party, the duration of a voice call, or other voice call related information.
The short-range communications subsystem enables communication between the
mobile device 1000 and other proximate systems or devices, which need not
necessarily
be similar devices. For example, the short-range communications subsystem may
include
an infrared device and associated circuits and components, or a BluetoothTM
communications module to provide for communication with similarly-enabled
systems
and devices.
Many modifications and other embodiments will come to the mind of one skilled
in the art having the benefit of the teachings presented in the foregoing
descriptions and
the associated drawings. Therefore, it is understood that various
modifications and
embodiments are intended to be included within the scope of the appended
claims.
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