Patent Publication Number: US-7593848-B2

Title: Auto-correlators with DC and CW cancellation

Description:
BACKGROUND 
   Classic auto-correlation techniques are applied in order to detect symbols and/or other meaningful information in a corresponding data stream. Auto-correlation is performed between a presently received wireless signal and a delayed (sampled) version of that signal stored in memory. Generally, such auto-correlation techniques exploit the periodicity structure of the wireless signal during acquisition of an orthogonal frequency-division multiplexing access (OFDMA) signal. In particular, such cyclic prefix based auto-correlators are used during acquisition of a WiMAX downlink signal, as one non-limiting example. As used herein, “WiMAX” and “802.16” respectively refer to signaling standards as defined by the Institute of Electrical and Electronics Engineers (IEEE), Inc., Piscataway, N.J., USA. In particular, the term “WiMAX” refers to IEEE standard 802.16e, as defined in year 2005. The signal periodicity of interest stems from the presence of a cyclic prefix guard interval inherent to the signaling protocol (e.g., WiMAX, 802.16, etc.). In such a case, the distance between correlated signal samples is one orthogonal frequency-division multiplexing access symbol interval, excluding the guard interval. 
   However, a problem is known to exist under classical auto-correlation strategies. Interference can render the reliable identification of symbols within the received and acquired wireless signal difficult, or in extreme cases, impossible. Various forms of interference as of concern here can be classified as either continuous wave (CW) or direct current (DC) in nature. Continuous wave interference is generally in the form of a constant (or intermittent) radio frequency carrier having no modulation, or modulation that is inconsequential with respect to the sought-after signal (e.g., a WiMAX or 802.16 down link signal, etc.). Direct current interference is typically in the form of a constant, non-oscillating electromagnetic field. In any case, classical auto-correlation of such interference-laden signal samples can result in an unwanted bias that fouls symbol detection and/or identification, rendering wireless communication under the situation difficult, impossible, or unreasonably slow as multiple signaling attempts are required. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  illustrates a signal diagram in accordance with one exemplary operating environment. 
       FIG. 2  illustrates a signal diagram in accordance with another operating environment. 
       FIG. 3  illustrates a process flow diagram in accordance with one embodiment. 
       FIG. 4  illustrates an exemplary signal diagram in accordance with one embodiment. 
       FIG. 5  illustrates an exemplary signal diagram according to known techniques. 
       FIG. 6  illustrates an exemplary signal diagram corresponding to  FIG. 5 . 
       FIG. 7  illustrates a system in accordance with one embodiment. 
   

   DETAILED DESCRIPTION 
   Underlying Concepts 
   Classic auto-correlation techniques are well known and are used during radio frequency signal acquisition and subsequent symbol detection. These classic techniques can yield problematic and sometimes unusable results when operating in relatively low signal-to-noise ratio environments. Such problems associated with classic auto-correlation procedures are usually compounded when sources of interference are present. Solutions to these problems are desirable and contemplated by the subject matter herein. 
   An improved auto-correlation algorithm is represented as: 
   
     
       
         
           
             
               
                 
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   wherein: x[n] is an incoming signal sample, CP is the length of a cyclic prefix, N is the number of symbols, and NFFT is the FFT size (also being the distance between correlated signal samples). 
   Equation 1 above improves the classical auto-correlator by “overlapping and adding” the results with a symbol length modulo. This approach improves the signal-to-noise ratio (SNR) of the auto-correlator results by a factor equal to the number of summed (i.e., accumulated) symbols. The numerator of Equation 1 expresses the auto-correlation operation, whereas the denominator expresses normalization by energy (i.e., power) of the signal samples. 
   Results of this improved auto-correlation operation—referred to as “overlap-and-add” symbols (or values)—are stored in a memory of (NFFT+CP) samples. Such a memory (or other suitable storage) as just discussed is referred to herein as an “overlap-and-add memory”. A search is then performed over the memory to identify a maximum absolute peak, as compared to a predetermined passing threshold, in order to validate the present of a signal. Frequency estimation may be performed by way of the angle of the auto-correlation peak (i.e., the peak or maximum within the auto-correlated signal sample data set). 
   Consideration is given to  FIG. 1 , which depicts an exemplary signal diagram  20 . The diagram  20  illustrates twenty orthogonal frequency-division multiplexing access symbols  22  within a single frame. The symbols  22  are depicted in favorable signal-to-noise ratio conditions, and with little or no interference present.  FIG. 1  is understood to represent a sequential diagram of auto-correlator output without overlap-and-add. 
   Consider now  FIG. 2 , which depicts another exemplary signal diagram  40 . The diagram  40  includes a plurality of auto-correlated signal samples (or data points)  42  that have been derived using overlap-and-add technique consistent with Equation 1 above. Typically, the auto-correlated samples (i.e., data)  42  would be resident in a memory or other storage of a device configured to perform the overlap-and-add auto-correlation procedure. 
   In any case, the diagram  40  of  FIG. 2  illustrates a summation of twenty auto-correlations per symbol, wherein the cyclic prefix length is two hundred fifty-six samples. The samples  42  of diagram  40  include a peak  44 , as well as a zone of samples  46  exemplary of substantially lesser correlated value (essentially zero). The data  42  of  FIG. 2  is typical of a WiMAX signal acquisition performed in a generally ideal, low-noise environment. 
   When continuous wave interference is present in a signal acquisition setting, the received signal may be represented as:
 
 y[n]=x[n]+e   j2πΔfn   +ν[n]   (Eq. 2)
 
   wherein: y[n] is the received signal sample, x[n] is a transmitted signal and ν[n] is an additive (unwanted) noise. Direct current interference is a special case, wherein Δf=0. Auto-correlation under these interference-laden circumstances renders a result that may be expressed as: 
   
     
       
         
           
             
               
                 
                   
                     
                       
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   wherein: y[n] and x[n] are as defined above in regard to Equation 2, and CP and NFFT are as defined above in regard to Equation 1. 
   It is noted that the second term of Equation 3 above, specifically: 
                     ∑     l   =   0       CP   -   1       ⁢     ⅇ     j   ⁢           ⁢   2   ⁢           ⁢   π   ⁢           ⁢   Δ   ⁢           ⁢     f   ·   NFFT           ;           (     Ex   .           ⁢   1     )               
is a vector with an angle of (2πΔf·NFFT), and an amplitude of CP. This vector is referred to herein as an interference vector. The interference vector, if not removed, adds a bias to the auto-correlation peak and angle, and may damage both frequency estimation and signal validation performance, as these are typically done.
 
   The interference vector (Expression 1) is constant and is added to the auto-correlation frame at every sample n=0, 1, 2, . . . , (NFFT+CP−1). If this interference vector could be subtracted from the overlap-and-add memory, the unwanted bias would be removed and the interference problems attributable thereto would be solved. Attention is now directed to such solutions. 
   Exemplary Methods 
     FIG. 3  is a flowchart  100  that describes a method in accordance with one embodiment. While the flowchart  100  describes particular methodical acts and order of execution, it is to be understood that the method of flowchart  100  is contemplated to be suitably varied, broadly applicable, and is not limited as specifically presented. Thus, other embodiments contemplated herein may be configured and/or performed wherein selected acts represented by the flowchart  100  are modified and/or omitted, and/or other acts not specifically depicted therein are executed. 
   At  102 , a plurality of wireless signal samples is acquired by known acquisition techniques. The source wireless signal is presumed to be a WiMAX protocol signal, but other signal sources may also be used in accordance herewith. Such acquisition techniques typically include analog-to-digital conversion by known means, etc. In any case, a plurality of discrete data samples is derived by way of the acts at  102 . 
   At  104 , the acquired signal samples are auto-correlated in accordance with overlap-and-add technique. In one embodiment, this auto-correlation is performed consistent with Equation 1 above. In another embodiment, another suitable auto-correlation procedure is applied. The results of the overlap-and-add auto-correlation are maintained as values in a memory (not shown) or other storage media, wherein each value corresponds to an acquired signal sample. 
   At  106  of  FIG. 3 , the auto-correlated samples (i.e., values) within the memory, also referred to as a memory picture, are divided into a plurality of segments of CP length each. CP, as used here and above, is the cyclic prefix length of the symbols represented by (i.e., essentially, encoded within) the acquired signal samples. Typically, several segments are derived by this act. Such division into segments is performed with the understanding that one, or a maximum of two, of the segments will include the auto-correlation peak (e.g.,  44  of  FIG. 2 ) and that all other segments will include the interference vector plus noise. 
   At  108 , the sample values within each segment are averaged. Thus, a plurality of averages is derived in count-correspondence with the number of segments. It has been discovered that an average over a segment that includes only the interference vector will result in the interference vector plus noise, whereas the average of the segment(s) including the peak will contain a different result. The averages are characterized by an angle and amplitude as described above in regard to interference vector Expression 1. 
   At  110  of  FIG. 3 , the averages from  108  above are compared. Specifically, the angle and amplitude of each segment average is compared to those of the other segments. Also, a predetermined threshold value is used in the comparison in order to identify (or designate) two or more of the segments as being “similar”. A plurality of similar segments is thus defined—typically, all but the one or two segments corresponding to the peak auto-correlated value. Those segments identified as similar comprise the interference vector (e.g., Expression 1) corresponding to the continuous wave and/or direct current interference borne by the original signal samples. 
   At  112 , an additional averaging of the similar segments identified at  110  above is done in order to isolate, or derive the interference vector (i.e., angle and amplitude) specifically effecting the signal sample data set under scrutiny. Also, this additional averaging step tends to reduce noise content within the auto-correlated samples of the similar segments. 
   At  114 , the interference vector, as derived at  112  above, is subtracted from each of the overlap-and-add memory values corresponding to the original signal sample set. This subtraction step removes the bias present in the auto-correlated signal samples and solves the impairment introduced by the original continuous wave and/or direct current interference. At this point, a plurality of corrected signal samples has been derived, wherein the effects of the original interference have been mitigated. 
   It is anticipated herein that the method of the flowchart  100 , or other embodiments contemplated under these teachings, may be implemented by way of a variety of means. In one non-limiting example, a computer-readable media comprises a program code that causes a processor, or plurality of processors, to effect (i.e., cause) corrective procedures on auto-correlated signal samples so as to derive corrected signal sample data. In another non-limiting example, auto-correlated signal samples are corrected by way of a suitably configured electronic circuit. In yet another example, one or more integrated circuit devices are configured so as to correct auto-correlated signal samples in accordance with the present procedures. In still another example, a system includes a device configured to correct auto-correlated signal sample data in accordance with the embodiments presented herein. These and any number of other suitable means may be configured and used to derive corrected, auto-correlated signal samples by way of the methods presented herein. 
   Hypothetical Example 
   In the interest of understanding, a hypothetical application case is now considered. For purposes of example, assume that a WiMAX signal has been acquired and auto-correlated wherein NFFT (the distance between auto-correlated signal samples)=1024, and CP (the cyclic prefix length)=256. It is further assumed for sake of example that a transmitted signal is frequency shifted by Δf=0.4 of the sub-carrier spacing as compared to the received signal. Further assume that a direct current (DC) interferer with an equal root mean square (RMS) level is present. In this exemplary case, the received signal may be written as:
 
 y[n]=x[n]·e   j2π0.4/NFFT·n +DC+ν[ n]   (Eq. 4)
 
   wherein: y[n], x[n] and NFFT are as defined above in regard to Equations 1 and 2, and ν[n] is an additive Gaussian noise. 
   In reference to exemplary Equation 4 above, the angle of the auto-correlation peak is used to extract the frequency deviation, which is equal to 0.4 is this case. The interference vector is subtracted by averaging over three segments of two hundred fifty-six samples each. 
     FIG. 4  depicts a signal diagram  200  in accordance with one embodiment. The diagram  200  shows the results of overlap-and-add auto-correlation after subtracting the averaged (i.e., derived) interference vector over the three segments corresponding to the hypothetical example introduced above. The diagram  200  includes a plurality of corrected, auto-correlated signal samples  242 , an auto-correlation peak  244 , and a zone  246  of near-zero auto-correlated samples, all plotted as respective amplitude values. As depicted, the diagram  200  illustrates a corrected auto-correlated sample data set that comprises virtually no effects from the direct current interference vector of the hypothetical example above. 
     FIG. 5  depicts a signal diagram  300  in accordance with auto-correlation techniques not inclusive of the corrective procedures contemplated herein (i.e., method of the flowchart  100  of  FIG. 3 , etc.). The diagram  300  includes a plurality of auto-correlated signal samples  342  plotted as respective amplitude values, wherein the effects of the hypothetical direct current interference vector are present. It is noted that the peak auto-correlation value  344  is greatly attenuated, while sample  342  values within the three segments  350 ,  352  and  354  are actually boosted. Thus, an undesired outcome has been realized and the direct current interference has essentially ruined the auto-correlated signal sample set of diagram  300 . Under such conditions, identification of a preamble symbol, data symbols, etc., within the auto-correlated samples  342  would be very difficult or impossible. 
     FIG. 6  depicts a signal diagram  400  corresponding to the diagram  300  of  FIG. 5 .  FIG. 6  includes a plurality of auto-correlated signal samples  442  plotted as respective phase values. The diagram  400  includes the three segments  350 ,  352  and  354  in time-synchronization to those of  FIG. 5 . As depicted the signal samples  442  have not been corrected in accordance with the procedures contemplated herein. The phasing information of the auto-correlated signal samples  442  is indicative of the direct current interference of the hypothetical example presented above. This phasing information is exploited by the present embodiments when averaging the sample data so as to isolate the interference vector. 
   When the respective diagrams of  FIGS. 4 ,  5  and  6  are compared, it is apparent that the procedures contemplated herein perform well to correct overlap-and-add auto-correlated signal samples so that peak data is not subject to attenuation, and the effects of interference are mitigated (i.e., cancelled). The corrected data enables reliable symbol detection within the original wireless signal stream, resulting in stable and expeditious communication. 
   Exemplary System 
     FIG. 7  depicts an exemplary system  500  according to another embodiment. System  500  is intended to exemplify but one of any number of possible systems inclusive of means and/or methods provided herein. Thus, the example system  500  is understood to be illustrative and non-limiting in its overall teachings. 
   The system  500  includes a server  502  and a plurality of client computers  504 . The server  502  and client computers  504  are respectively defined and operative in any suitable known way. Thus, the server  502  and clients  504  are understood to be broadly applicable to any number of respective tasks. The system  500  also includes a network  506 . The server  502  and the client computers  504  are coupled to, and are in communication with each other by way of, the network  506 . The network  506  may be defined by any suitable known network topology including, but not limited to, a local-area network (LAN), a wide-area network (WAN), etc. Furthermore the network  506  may be defined by or in communication with the Internet. 
   The system  500  may further include a laptop computer  508 , a personal digital assistant (PDA)  510  and/or a cellular phone  512 . Each of the elements  508 ,  510  and  512  is respectively defined and configured in accordance with known topologies. For purpose herein, the laptop  508 , PDA  510  and cellular phone  512  are understood to include bidirectional wireless communication functionality in accordance with WiMAX 802.16 protocols. Thus, the laptop computer  508 , personal digital assistant  510  and cellular phone  512  are collectively referred to as wireless devices  514 . 
   As also depicted in  FIG. 7 , the system  500  includes a WiMAX transceiver  520 , coupled in communication with the network  506 . The transceiver  520  includes a processor  522 , a memory  524 , a receiver  526 , a transmitter  528  and a battery  532 . The battery  532  is electrically coupled to one or more of the elements  522 - 528 . Various respective functions of the receiver  526  and the transmitter  528  are under the control of the processor  522 . In turn, the processor  522  executes one or more program codes (not explicitly shown) stored either internally and/or within the memory  524 . Thus, the elements  522 - 528  of the transceiver  520  are electrically coupled so as to function as a cooperative entity. 
   The transceiver  520  further includes corrector  530  functionality. The corrector may be coupled to the battery  532  as desired. The corrector  530  is implemented and configured consistent with the balance of the transceiver  520  functions so as to correct overlap-and-add auto-correlation data consistent with such procedures as described above. In one embodiment, the corrector  530  is provided as a program code (storable within memory  524 , etc.) executable by the processor  522 . In another embodiment, the corrector  530  is implemented as a dedicated-function electronic circuit within the transceiver  520 . In yet another embodiment, the corrector  530  is provided as a program code on computer readable media such as, for example, floppy disk, CD-ROM, swappable/installable firmware, etc., and is downloaded to/installed within the transceiver  520 . In one or more embodiments, the corrector  530  is an integral portion of the auto-correlation means of the transceiver  520 . In any event, the corrector  530  may be implemented within the transceiver  520  by way of these and any number of other suitable means, and exhaust elaboration is not required for purposes herein. 
   The exemplary system  500  of  FIG. 7  typically operates as follows. Any of the server  502 , client computers  504  and/or wireless devices  514  may communicate and cooperate by way of WiMAX-complient radio frequency signaling. It is assumed, for purposes of this example, that the processor  522  executes a corresponding program code (not shown) so as to perform overlap-and-add auto-correlation on acquired WiMAX wireless signals, storing the auto-correlated signal samples in memory  524 . The transceiver  520  then utilizes the corrector  530  to correct the auto-correlated signal samples stored in memory  524  in accordance with the procedures herein. In one non-limiting embodiment, the corrector  530  is configured to perform the method of the flowchart  100  of  FIG. 3 . In any case, corrected auto-correlation signal samples are derived by way of the corrector  530 . 
   In one non-limiting example, the transceiver  520  is able to readily and efficiently identify and process WiMAX wireless signals during an Internet surfing session by the laptop computer  508  in the presence of a direct current interference source. In another non-limiting example, the corrector  530  enables the transceiver to detect preamble symbols within wireless signals transmitted by the personal digital assistant  510 , despite a continuous wave interference source (not shown) in near proximity to the transceiver  520 . These and countless other operational scenarios may be performed by the system  500 , wherein the corrector  530  of the transceiver  520  serves to expedite and improve the reliability of WiMAX wireless communications by mitigating the effects of continuous wave and/or direct current interferences. 
   CONCLUSION 
   Embodiments and methods presented herein provide solutions to problems encountered during known auto-correlation of wireless signals samples, such as those acquired from orthogonal frequency-division multiplexing access signals, WiMAX signals, IEEE 802.16-compliant signals, etc. Generally, interference from continuous wave and/or direct current interference sources is cancelled, or removed, by way of new signal sample correction techniques. These techniques provide for improved auto-correlators that exploit cyclic prefix redundancy of a WiMAX (or other) signal, yielding corrected results over known methods and means. 
   In turn, significantly enhanced performance can be realized in cases where overlap-and-add auto-correlation; is use during acquisition of a WiMAX signal. Such improved performance applies to both signal acquisition and frequency estimation. Additionally, the present embodiments can measurably simplify hardware implementation costs. This improved performance speeds communication between wireless entities, as symbol detection failures and/or the need to repeat packet transmissions is essentially eliminated. 
   Although the embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claimed subject matter.