JP2009506595A - Wireless communication device and associated method including joint demodulation filter for co-channel interference reduction - Google Patents

Abstract

A wireless communication device may include a housing and a wireless transmitter and receiver received by the housing. The wireless receiver may include a combined demodulation filter for reducing co-channel interference between the desired signal and the co-channel interference signal, the combined demodulation filter receiving the desired signal and a sample of the co-channel interference signal An input unit, a Viterbi decoder, and a first signal path between the input unit and the Viterbi decoder and including a first filter may be included. The combined demodulation filter may further include a second signal path between the input and the Viterbi decoder, the second signal path being a linear finite impulse for generating a channel impulse response estimate for the co-channel interference signal. A response (FIR) modeler may be provided. Furthermore, the third signal path can be between the input and the Viterbi decoder and can include a whitened matched filter to generate a channel impulse response estimate for the desired signal.

Description

(Field of Invention)
The present invention relates to wireless communication systems, such as cellular communication systems, and more particularly to filtering received wireless signals to reduce unwanted interference.

(background)
Cellular communication systems continue to gain popularity and have become an integral part of both personal and business communications. A cellular telephone allows a user to place and receive voice calls almost anywhere. However, with the increasing number of cellular telephone users, there are greater problems for wireless communication devices and network providers. One such problem is to deal with interference caused between multiple cellular devices operating in a given geographic region. Cellular devices communicate with cellular base stations using a common or shared wireless communication channel (ie, frequency). However, in some cases, signals using the same channel between other devices and the base station can cause significant degradation or even outage by the handheld device relative to the desired signal from the base station. Such interference is referred to as co-channel interference.

  Due to the increasing load on cellular communications infrastructure, various single antenna interference cancellation (SAIC) approaches have been investigated to meet the demands of the Downlink Advanced Receiver Performance (DARP). This effort has been standardized by the third generation mobile communication system and the third generation partnership project (3GPP).

  One SAIC technique that has been investigated is based on joint demodulation of the desired and interfering sequences. Generally speaking, this approach begins with a standard least square (LS) estimate of a static channel profile for the propagation channel and interferer. A modified Viterbi decoder is then used, in which half of the status bits represent the user sequence and the other half represent the interferor. The combined branch metric is minimized and the estimated sequences for the desired signal and the interference signal are used in a least mean square (LMS) algorithm to update the channel estimates for both the desired propagation channel and the interference propagation channel. It is done.

  The 3GPP concept has been considered for the application of joint demodulation in synchronized wireless networks. For example, see Non-Patent Document 1. This overlaps the desired signal and the main interferer's base station synchronization data sequence (ie, training sequence) data, which also allows estimation of the CIR using previously known techniques. This is a more limited case where it is necessary to assume This more limited case also requires the assumption that the interferer is dominant for all bursts.

However, in asynchronous network applications, the training sequence of the interference signal may not overlap with the training sequence of the desired signal, which makes CIR estimation problematic. Thus, further development may be desirable to put into practice and implement joint demodulation techniques in both synchronous and asynchronous networks.
“Feasibility Study on Single Antenna Interference Cancellation (SAIC) for GSM Networks”, 3GPP TR45.903 version 6.0.1, release 6, European Telecommunications Standards 4

Detailed Description of Preferred Embodiments
The present description will be made with reference to the accompanying drawings, in which preferred embodiments are shown. However, many different embodiments can 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 communication device is described herein that includes a housing and a wireless transmitter and receiver received by the housing. In particular, the wireless receiver may include a combined demodulation filter to reduce co-channel interference between the desired signal and the co-channel interference signal. The combined demodulation filter is a first signal path between an input unit that receives a desired signal and a sample of a co-channel interference signal, a Viterbi decoder, and the input unit and the Viterbi decoder, The signal path may include a first signal path comprising a first filter. The combined demodulation filter further includes a second signal path between the input and the Viterbi decoder, the second signal path being a linear finite impulse response (FIR) for generating a channel impulse response estimate for the co-channel interference signal. ) May have a modeler. Furthermore, the third signal path is between the input and the Viterbi decoder and may 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 interference signal may each include a training sequence, and the combined demodulation filter is a training sequence locator that is upstream of the second and third paths and downstream of the input. May further be included. Further, the third signal path may include a CIR estimator of the desired signal upstream of the whitening matched filter that generates a channel impulse response (CIR) estimate of the desired signal. Further, the first filter may be a first finite impulse response (FIR) filter.

  The second signal path may include a first adder and a second adder connected downstream of the first adder. The second signal path further includes a remodulator between the CIR estimator of the desired signal and the first adder, the remodulator comprising the CIR estimator and the first adder of the desired signal. In cooperation with, a re-modulated desired signal training sequence may be subtracted from the desired signal and co-channel interference signal samples, thereby generating an interference signal estimate. Further, the linear FIR modeler may include a blind interference and CIR estimator and a second FIR filter downstream of the blind interference and CIR estimator. The Viterbi decoder may also create an iterative bit sequence hypothesis tree in an iterative manner.

  In a wireless communication receiver, a joint demodulation filtering method for reducing co-channel interference between a desired signal and a co-channel interference signal is performed using a first signal path including a first filter. And filtering received samples of the co-channel interference signal. The method uses a second signal path comprising a linear finite impulse response (FIR) modeler to generate a channel impulse response estimate for a co-channel interference signal and a whitening matched filter. Generating a channel impulse response estimate for the desired signal using the three signal paths. Further, the decoding operation is based on filtered received samples of the desired signal and co-channel interference signal, channel impulse response estimation for the co-channel interference signal, and channel impulse response estimation for the desired signal using a Viterbi decoder. Can be executed.

  Referring first to FIGS. 1 and 2, a combined demodulation filter 10 according to an exemplary embodiment receives a desired signal and a sample of co-channel interference signals from, for example, an antenna of a wireless communication device (eg, a mobile cellular device). The input unit 11 is exemplarily included. That is, the combined demodulation filter 10 can be advantageously implemented in a wireless receiver of a mobile wireless communication device. As will be appreciated by those skilled in the art, the various components of the combined demodulation filter 10 can be implemented using software modules and processing circuitry such as a digital signal processor (DSP), but other implementations are possible. Is possible. Exemplary components of a mobile cellular device in which the combined demodulation filter 10 can be used can be further discussed below with reference to FIG.

  The combined demodulation filter 10 further includes a first signal path 12 between the input unit 11 and the Viterbi decoder, which further includes a Viterbi decoder 30 and a first filter 46. In the exemplary embodiment shown in FIG. 2, the first filter 46 may be a finite impulse response (FIR) filter, such as, for example, a matched filter. The second signal path 13 is included between the input unit 11 and the Viterbi decoder 30. Second signal path 13 illustratively includes a linear FIR modeler 15 that generates a channel impulse response estimate for the co-channel interference signal. Further, the third signal path 14 is illustratively connected between the input unit 11 and the Viterbi decoder 30. As discussed further below, the third signal branch illustratively includes a whitened matched filter 44 that generates a channel impulse response estimate for the desired signal.

  Additional components of the exemplary combined demodulation filter 10 illustrated in FIG. 2 are now briefly identified, followed by a description of the various functions of the components. As described above, for example, in a cellular GSM-based network, the desired signal and the co-channel interference signal each include a training sequence. The combined demodulation filter 10 illustratively includes a training sequence locator 20 for a desired signal upstream of the second path 13 and the third path 14 and downstream of the input 11. The third signal path 14 illustratively includes a desired signal CIR estimator 22 upstream of the whitening matched filter 44 that produces the desired signal CIR estimate.

  The second signal path 13 also includes a first adder 26, a second adder 34 connected downstream of the first adder, and a CIR estimator 22 and a first adder for the desired signal. A re-modulator 24, which, in cooperation with the CIR estimator and first adder of the desired signal, re-samples from the desired signal and co-channel interference signal samples. Subtract the training sequence of the modulated desired signal, thereby generating 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 of the blind interference and CIR estimator 28, and the second FIR filter 42 also receives input from the whitening matched filter 44. The second adder 34 also receives blind interference and the output of the CIR estimator 28 as shown.

  The second signal path 13 includes a residual noise power (Pn) sample offset block 32 between the first adder 26 and the second adder 34 and a second addition, as will be discussed further below. A significant interferer component (Pif) sample offset block downstream of the instrument and a Pif / Pn determination block 38 downstream of the Pif sample offset block are illustratively included. Mixer 40 is downstream of Pif sample offset block 38 and also receives the output of second FIR filter 42 as shown. The output of the mixer 40 and the output of the whitening matched filter 44 are supplied to the Viterbi decoder 30 in the same manner as the output of the first FIR 46.

  The operation of the combined demodulation receiver 10 will now be described in further detail. As described above, the joint demodulation (JD) receiver 10 can be advantageously used in wireless communication systems such as, for example, cellular base stations and mobile cellular communication devices. Generally speaking, joint demodulation uses an estimate for the channel impulse response (CIR) for the desired signal and the dominant interferer associated therewith. For the GSM implementation discussed below, it is assumed that the dominant interferer is a GMSK modulated signal that conforms to the GSM specification.

  The combined modulation approach described herein is applicable to both synchronized and unsynchronized networks in that this technique uses “blind” interferer data and channel estimation techniques rather than making the above assumptions. possible. Once the CIR is estimated, a two-dimensional (join) adaptive Viterbi state structure can be used in the equalizer to estimate data for both the desired signal and the interferor.

  The simulation of this combined demodulation technique shows that the raw symbol error rate and frame error rate for 12.2-rate AMR FS speech is approximately 0 dB carrier-to-interference (C / I) and C greater than 10 dB C / I. / I proved improvement. In the simulation, a new combined least square based technique was used for channel offset positioning and desired interferer CIR estimation. As noted above, this approach is tied to blind estimation of interferer data (ie, no a priori knowledge of interferer data).

  This combined demodulation approach, as discussed further below, is the ability of this approach to provide a relatively high gain (ie, very low signal when limited a priori knowledge about the interferer is available). This approach may be particularly advantageous in terms of the ability to receive at a noise to noise ratio (SNR). However, the complexity of the Viterbi algorithm (VA) may also increase (depending on the state used to model the interferor), resulting in some additional processing requirements and channel / data estimator complexity. Can be a factor in the implementation of software or hardware.

  With respect to test configurations, the system level block error rate (BLER) simulator has been extended to support all of the interferer models / scenarios used by the 3GPP DARP workgroup. This extension also allows new interferer models to be developed as needed. The simulation was performed using Matlab.

  The joint demodulation approach assumes that the dominant interference component can be modeled as a noisy output of a finite impulse response (FIR) (unknown) with unknown binary random input (interferer) data. In the case of the dominant GMSK modulation interferer, this assumption is true even in the presence of further weaker interference signals that are treated as residual noise. Furthermore, this approach can be applied to other types of interfera modulation using the modeling assumptions described above.

  Referring again to FIG. 2, the steps involved in the combined 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), the remodulated desired training sequence is removed from the input samples, An interferer signal estimate is formed (block 24). In addition, “blind” estimation of the interferer CIR and data is performed based on the interferer signal estimation in blocks 26, 28. Next, as discussed further below, a desired / interferer channel estimate that results in combined least squares using the desired training sequence and estimated interferer data is performed at block 30.

  Further, the foregoing steps can be repeated (or performed in vectorized form) at multiple input sample offsets (since the timing offset varies). As such, the offset that yields the least residual noise power (Pn) can be selected, and at blocks 32, 34, 36 and 38, whether the model applies (ie, significant interferer component (Pif) is detected). A decision can be made. If the model applies, demodulation is performed using a combined demodulation (multidimensional state) Viterbi algorithm that estimates and removes interference in combination with estimation of the desired signal data (block 30).

As will be appreciated by those skilled in the art, initially, the desired channel impulse response was estimated using a conventional training sequence correlation (ie, “channel-voice”) method. At low C / I levels, the least squares method provides an initial desired channel impulse response estimate by multiplying the input samples by a constant (pre-computed) matrix (A ^H A) ⁻¹ A ^H Where A is the convolution matrix of the desired signal training sequence.

  With respect to estimating the interferer, the above SAIC feasibility study assumes a synchronous network model. More specifically, this model assumes that the training sequence of the interfering signal is aligned with the training sequence of the desired signal within a −1 to +4 symbol offset. In this case, the interferer channel impulse response is obtained by removing the (remodulated) training sequence of the desired signal from the received samples, and then the training sequence correlation technique (or the training sequence data is known, so the least squares ).

  However, blind channel and data estimation and demodulation techniques are used to expand the applicability of the combined demodulation approach for the case of asynchronous networks where the interferer data is unknown during the desired signal training sequence. For background on this point, see Seshadri, IEEE Trans. on Communications, Vol. 42, no. 2/3/4, pages 1000-1011, the title "Joint Data and Channel Estimating Using Blind Trellis Search Techniques", and Danishgaran et al., IEEE Communications Letters, vol. 2, no. 11, November 1998, pages 307-309, the title "Blind Estimate of Output Labels of SIMO Channels Based on a Novel Clustering Algorithm" is referenced.

  One particular difficulty in performing blind interferer estimation is the very small number of “observable” interferer (ie, noisy) samples in the desired signal training sequence window. is there. As a reference, the sequence window is derived from the desired training sequence length (for this embodiment, the training sequence length is 26 as defined by the GSM05 series standard) to the desired signal CIR length ( 5 is selected by this simulation, but as defined by the GSM standard, other numbers between 1 and 7 can be selected by the channel model), and 1 is added, ie this embodiment In this case, 26−5 + 1 = 22.

  This approach uses an algorithm that combines the concept of vector quantization and sequential decoding of convolutional codes. The algorithm is based on two assumptions. That is, (1) the interferer signal can be modeled by a linear finite impulse response (FIR) source (block 28), and (2) the interferer signal is left with additional white (ie, uncorrelated) Gaussian noise. (After removing the estimated desired signal) and (FIGS. 1 and 26).

  With these two assumptions, the algorithm creates an iterative bit sequence hypothesis tree iteratively. For each new bit added to the bit sequence hypothesis, the hypothesis calculates a new FIR state (or codebook index, as will be apparent to those skilled in the art of vector quantization) and the FIR output for that state ( To estimate the codebook value), all input samples corresponding to the same state in a particular sequence are averaged. Bit sequence distortion is the residue after removing the FIR output of the sequence from the input sample (FIGS. 2, 36). After maintaining the W (search width parameter) bit sequence with the lowest distortion, each sequence is extended by another 0/1 bit, yielding two new sequences (2W total) and regenerating the FIR output of each sequence. The estimation process is repeated, and then keeps the W sequence with the least distortion (eg, half of the sequence). When the length of the sequence reaches the number of available interferer signal samples (22 as described above, 22 for this embodiment), the sequence with the lowest distortion is selected from the W candidates.

  This above algorithm provides initial interferer data and channel impulse response estimation for desired signal estimation and interferer channel estimation with subsequent combined least squares. At C / I levels below 5 dB, the CIR position (offset) and the estimation of CIR values for the desired and interferers are affected by the cross-correlation of the desired and interferer data sequences. However, joint least squares channel estimation is possible using pre-obtained interferer data estimation, which removes this cross-correlation as follows (ie cancels this cross-correlation): ):

ここで、ｓは、所望の訓練シーケンスウィンドウ時の入力サンプルを含み（前述のとおり２６−５＋１＝２２）、Ａ（ＮｘＬｈ）およびＢ（ＮｘＬｇ）は、所望のデータシーケンス畳み込み行列およびインターフェアラデータシーケンス畳み込み行列（Ａは既知でかつ一定であり、Ｂはインターフェアラに対する推定）であり、ｈおよびｇは、それぞれ所望の信号のＣＩＲおよびインターフェアラのＣＩＲであり、それらは、ｈの長さであるＬｈ（この実施形態において５）およびｇの長さであるＬｇ（この実施形態に対して３が選ばれる）を用いて上記の等式を解くことによって得られる。

Where s contains the input samples during the desired training sequence window (26-5 + 1 = 22 as described above) and A (NxLh) and B (NxLg) are the desired data sequence convolution matrix and interferer data sequence convolution. Matrix (A is known and constant, B is an estimate for the interferer), h and g are the CIR of the desired signal and the CIR of the interferer, respectively, and they are Lh (this In the embodiment, it is obtained by solving the above equation using 5) and Lg which is the length of g (3 is chosen for this embodiment).

  Once the desired channel impulse response estimate and the interferer channel impulse response estimate are available, a two-dimensional state Viterbi algorithm can be applied. For Euclidean distance metrics, a whitened discrete time model filter (WMF) is calculated from the estimated desired CIR (block 44). The calculation is also applied to the interferer CIR, and the three (Lg) largest resulting taps are used to form an interfera codebook (ie, a set of possible interferer channel FIR outputs). Of course, other numbers of taps Lh and Lg may be used in some embodiments.

  The resulting desired signal codebook and interferer codebook are passed to the combined demodulation Viterbi algorithm. The returned soft decision metrics include forward and backward recursion using the odd / even state minimum metric difference at each stage (not the path) as the soft decision value and sign.

  Referring now to FIG. 3, as will be understood by those skilled in the art, a typical at 50 km vehicle speed (TU-50) in the 1950 MHz band without the use of frequency hopping and the use of the interferer model DTS1. Simulated results for TCH-AFS 12.2 rate speech for urban fading distribution are shown. C / I is the average carrier-to-interference ratio.

  Dashed lines 50 and 51 represent the SER (symbol error rate) and FER (frame error rate) of a conventional GMSK receiver. Dashed lines 53 and 54 represent the performance of the SAIC-JD receiver described above. Solid lines 55 and 56 represent the performance of a more complex SAIC-JD receiver, according to an exemplary embodiment of the present invention, where the interferer brand vector quantization is recursive least squares (RLS). ) Performed with updates, while interferer symbol sequence hypotheses are formed and evaluated. As will be appreciated by those skilled in the art, the performance plot shows that both SAIC-JD receivers provide a significant improvement over conventional receivers in high interference environments.

  After removing the desired (ie, estimated) samples, the amount of residual “noise” power remaining in the training sequence window of the desired signal is, in some embodiments, a test of the “fit” of the model. Can be used. If removing the estimated interferer does not significantly reduce the residual power, a non-interfering signal model is selected and vice versa.

  A joint demodulation filtering method for reducing co-channel interference between a desired signal and a co-channel interference signal will now be described with reference to FIG. Beginning at block 60, received samples of the desired signal and the co-channel interference signal are filtered at block 61 using the first signal path 12 comprising the first filter 46. The method generates a block impulse response estimate for a co-channel interfering signal using a second signal path 13 comprising a linear finite impulse response (FIR) modeler 15 at block 62; , Further using a third signal path 14 comprising a whitened matched filter 44 to generate a channel impulse response estimate for the desired signal. Further, at block 64, based on the filtered received samples of the desired signal and co-channel interference signal, the channel impulse response estimate for the co-channel interference signal, and the channel impulse response estimate for the desired signal using the Viterbi decoder 30, A decoding operation may be performed, so that the illustrated method ends (block 65).

  One example of a handheld mobile wireless communication device 1000 that may be used in accordance with system 20 is further described in the following example with reference to FIG. 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 can be utilized instead. Processing device 1800 is contained within housing 1200 and is coupled between keypad 1400 and display 1600. The processing device 1800 controls the operation of the display 1600 along with the overall operation of the mobile device 1000 in response to the operation of keys on the keypad 1400 by the user.

  The housing 1200 may be stretched vertically or may take other sizes and shapes (including clamshell housing structures). The keypad may include mode selection keys or other hardware or software for switching between text input and telephone input.

  In addition to the processing device 1800, other parts of the mobile device 1000 are schematically illustrated in FIG. These include communication subsystem 1001, short-range communication subsystem 1020, keypad 1400 and display 1600, as well as other input / output devices 1060, 1080, 1100 and 1120, and memory devices 1160, 1180 and Various other device subsystems 1201 are included. The mobile device 1000 is preferably a bidirectional RF communication device having a voice communication function and a data communication function. Furthermore, the mobile device 1000 preferably has a function of communicating with other computer systems via the Internet.

  Operating system software executed by processing device 1800 is preferably stored in a persistent storage device, such as flash memory 1160, but other types of storage devices, such as read only memory (ROM) or similar storage elements. Can be stored. Further, system software, specific device applications, or portions thereof may be temporarily loaded into volatile storage devices such as random access memory (RAM) 1180. Communication signals received by the mobile device may also be stored in the RAM 1180.

  In addition to the operating system functionality of the processing device 1800, the processing device 1800 allows the software application 1300A-1300N to run 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 at the time of manufacture. In addition, a Personal Information Manager (PIM) application can be installed at the time of manufacture. The PIM is preferably capable of organizing and managing data items such as emails, calendar events, voice mails, appointments and task items. The PIM application is also preferably capable of sending and receiving data items via the wireless network 1401. Preferably, the PIM data items are seamlessly integrated, synchronized and updated via the wireless network 1401 using corresponding data items of device users stored or associated with the host computer system.

Communication functions including data communication and voice communication are performed via the communication subsystem 1001, and possibly via the short-range communication subsystem. Communication subsystem 1001 includes a receiver 1500, a transmitter 1520, and one or more antennas 1540 and 1560. In addition, the communication subsystem 1001 also includes a processing module, such as a digital signal processor (DSP) 1580, and a local oscillator (LO) 1601. The particular design and implementation of the communication subsystem 1001 depends on the communication network that the mobile device 1000 is intended to operate on. For example, the mobile device 1000 is designed to work with a Mobitex ^™ , Data TAC ^™ or General Packet Radio Service (GPRS) mobile data communication network and includes AMPS, TDMA, CDMA, WCDMA, PCS, GSM, EDGE, etc. It may include a communication subsystem 1001 that is also designed to work with any of a variety of voice communication networks. Other types of data and voice networks, both discrete and integrated types, may also be utilized with mobile device 1000. The mobile device 1000 may also conform to other communication standards such as 3GSM, 3GPP, UMTS.

  Network access requirements vary with the type of communication system. For example, in the Mobitex and DataTAC networks, mobile devices are registered with the network using a unique personal identification number or PIN associated with each device. However, in GPRS networks, network access is associated with device subscribers or users. Thus, GPRS devices require a subscriber identity module, commonly referred to as a SIM card, in order to operate in a GPRS network.

  When the necessary network registration or activation procedure is completed, the mobile device 1000 may send and receive communication signals via the communication network 1401. Signals received from communication network 1401 by antenna 1540 are routed to receiver 1500, which provides 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 communication functions such as demodulation and decoding. In a similar manner, a signal transmitted to network 1401 is processed (eg, modulated and coded) by DSP 1580 and then fed to transmitter 1520 for digital-to-analog conversion, frequency up-conversion, filtering, and amplification. And transmitted to the communication network 1401 (or a plurality of networks) via the antenna 1560.

  In addition to processing communication signals, the DSP 1580 provides control of the receiver 1500 and transmitter 1520. For example, the gain added to the communication signals at receiver 1500 and transmitter 1520 can be controlled to accommodate by an automatic gain control algorithm implemented in DSP 1580.

  In the data communication mode, received signals, such as text messages or web page downloads, are processed by the communication subsystem 1001 and input to the processing device 1800. The received signal is then further processed by the processing device 1800 and output to the display 1600, or alternatively to some other auxiliary I / O device 1060. The device user may also have some auxiliary I / O devices 1060 such as keypad 1400 and / or touchpad, rocker switch, thumb-wheel, etc., or some other type of input. The device can be used to compose data items such as email messages. The configured data item may then be transmitted via the communication network 1001 via the communication subsystem 1001.

  In the voice communication mode, the overall operation of the device is substantially similar to the data communication mode except that the received signal is output to the speaker 1100 and the transmitted signal is generated by the microphone 1120. . Alternative voice I / O subsystems or audio I / O subsystems, such as voice message recording subsystems, may also be implemented in device 1000. The display 1600 can also be used in a voice communication mode, for example, to display caller identification, duration of a voice call, or other voice call related information.

The short range communication subsystem allows communication between the mobile device 1000 and other similar systems or devices that do not necessarily have to be similar devices. For example, the short-range communication subsystem may include infrared devices and associated circuitry and components, or Bluetooth ^™ communication modules, and provide communication with similarly usable systems and devices.

  Many modifications and other embodiments will occur to those skilled in the art having the benefit of the teachings presented in the foregoing description and the associated drawings. Accordingly, it will be understood that various modifications and embodiments are intended to be included within the scope of the appended claims.

FIG. 1 is a schematic block diagram of an exemplary single antenna interference cancellation (SAIC) capable of using a combined demodulation global system (GSM) for a mobile communication receiver according to the present invention. FIG. 2 is a schematic block diagram of an exemplary embodiment of the combined demodulation receiver of FIG. 1 shown in more detail. FIG. 3 is a graph of simulated performance results for a SAIC coupled demodulation receiver according to the present invention and a typical GMSK receiver according to 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 interference signal in accordance with the present invention. FIG. 5 is a schematic block diagram of an exemplary wireless communication device in which the combined demodulation receiver of FIG. 1 may be used.

Claims (20)

A wireless communication device,
A housing;
A wireless transmitter and a wireless receiver housed by the housing,
The wireless receiver comprises a combined demodulation filter for reducing co-channel interference between a desired signal and a co-channel interference signal, the filter comprising:
An input for receiving samples of the desired signal and the co-channel interference signal;
A Viterbi decoder;
A first signal path between the input and the Viterbi decoder, the first signal path comprising a first filter;
A second signal path between the input and the Viterbi decoder, wherein the second signal path is a linear finite impulse response (FIR) for generating a channel impulse response estimate for the co-channel interference signal. ) A second signal path comprising a modeler;
A third signal path between the input and the Viterbi decoder, the third signal path comprising a whitened matched filter for generating a channel impulse response estimate for the desired signal A device comprising: a third signal path;   The desired signal and the co-channel interference signal each include a training sequence, and the combined demodulation filter includes a training sequence locator that is upstream of the second and third paths and downstream of the input. The wireless communication device of claim 1, further comprising:   The desired signal CIR estimator upstream of the whitened matched filter to generate a channel impulse response (CIR) estimate of the desired signal. Wireless communication devices.   The wireless communication device of claim 1, wherein the first filter comprises a first finite impulse response (FIR) filter.   The wireless communication device according to claim 1, wherein the second signal path includes a first adder and a second adder connected downstream of the first adder.   The second signal path is between the CIR estimator of the desired signal and the first adder, and in cooperation with the CIR estimator of the desired signal and the first adder; 6. The remodulator of claim 5, further comprising a remodulator that subtracts a training sequence of the remodulated desired signal from the samples of the desired signal and the co-channel interference signal, thereby generating an interference signal estimate. Wireless communication devices.   The wireless communication device of claim 1, wherein the linear FIR modeler comprises a blind interference and CIR estimator and a second FIR filter downstream of the blind interference and CIR estimator.   The wireless communication device of claim 1, wherein the wireless receiver comprises a cellular receiver. A wireless communication device,
A housing;
A wireless transmitter and a wireless receiver housed by the housing,
The wireless receiver comprises a joint demodulation filter for reducing co-channel interference between a desired signal and a co-channel interference signal, the desired signal and the co-channel interference signal each including a training sequence; The filter is
An input for receiving samples of the desired signal and the co-channel interference signal;
A Viterbi decoder;
A first signal path between the input and the Viterbi decoder, the first signal path comprising a first finite impulse response (FIR) filter;
A second signal path between the input and the Viterbi decoder, wherein the second signal path is a linear finite impulse response (FIR) for generating a channel impulse response estimate for the co-channel interference signal. ) A second signal path comprising a modeler;
A third signal path between the input and the Viterbi decoder, the third signal path comprising a whitened matched filter for generating a channel impulse response estimate for the desired signal A third signal path;
A training sequence locator upstream of the second path and the third path and downstream of the input.   10. The desired signal CIR estimator upstream of the whitening matched filter to generate a channel impulse response (CIR) estimate of the desired signal, the third signal path. Wireless communication devices.   The wireless communication device according to claim 9, wherein the second signal path includes a first adder and a second adder connected downstream of the first adder.   The second signal path is between the CIR estimator of the desired signal and the first adder, and in cooperation with the CIR estimator of the desired signal and the first adder; 12. The remodulator of claim 11, further comprising a remodulator that subtracts a training sequence of the remodulated desired signal from the sample of the desired signal and the co-channel interference signal, thereby generating an interference signal estimate. Wireless communication devices.   The wireless communication device of claim 9, wherein the linear FIR modeler comprises a blind interference and CIR estimator and a second FIR filter downstream of the blind interference and CIR estimator. A method for reducing co-channel interference between a desired signal and a co-channel interference signal at a receiver of a wireless communication device, the method comprising:
Filtering received samples of the desired signal and the co-channel interference signal using a first signal path comprising a first filter;
Generating a channel impulse response estimate for the co-channel interference signal using a second signal path comprising a linear finite impulse response (FIR) modeler;
Generating a channel impulse response estimate for the desired signal using a third signal path comprising a whitened matched filter;
Using a Viterbi decoder, the filtered received samples of the desired signal and the co-channel interference signal, the channel impulse response estimate for the co-channel interference signal, and the channel impulse response estimate for the desired signal And performing a decoding operation based on the method.   The desired signal and the co-channel interference signal each include a training sequence, further comprising executing a training sequence location that is upstream of the second and third paths and downstream of the input. The method according to claim 14.   15. The desired signal CIR estimator upstream of the whitened matched filter to generate a channel impulse response (CIR) estimate of the desired signal, the third signal path. the method of.   The method of claim 14, wherein the first filter comprises a first finite impulse response (FIR) filter.   The method of claim 14, wherein the second signal path comprises a first adder and a second adder connected downstream of the first adder.   The second signal path is between the CIR estimator of the desired signal and the first adder, and in cooperation with the CIR estimator of the desired signal and the first adder; The remodulator further comprising: a remodulator that subtracts a training sequence of the remodulated desired signal from the sample of the desired signal and the co-channel interference signal, thereby generating an interference signal estimate. the method of.   15. The method of claim 14, wherein the linear FIR modeler comprises a blind interference and CIR estimator and a second FIR filter downstream of the blind interference and CIR estimator.

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