Abstract:
A wireless device with multiple antennae and configured to support orthogonal frequency-division multiplexed (OFDM), multiple-input multiple-output (MIMO) communications of a wireless local area network (LAN) among multiple wireless devices over a shared wireless communication medium on a shared communication channel. The wireless access device comprises: a mixed-mode MIMO detector configured to allocate a mix of linear and non-linear MIMO detectors among received OFDM sub-channels of the shared communication channel, thereby reducing a level of complexity associated with reception of MIMO streams exclusively via non-linear MIMO detectors.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of prior filed co-pending Provisional Applications No. 61/477,008 filed on Apr. 19, 2011 entitled “Efficient Channel Ordering for Near Optimum MIMO Detection” and No. 61/494,823 filed on Jun. 8, 2011 entitled “Method &amp; Apparatus for Tone Discriminated Near-Optimum MIMO Decoding” which are incorporated herein by reference in their entirety as if fully set forth herein. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of Invention 
     The field of the present invention relates in general to Wi-Fi wireless access points (WAP) and devices for wireless local area networks (LAN). 
     2. Description of the Related Art 
     Wireless local area networks (LAN) such as home or office networks are increasingly set up and serviced using a device called a Wireless Access Point (WAP). The WAP couples all the devices of the wireless LAN, e.g. computers, printers, televisions to one another and to the Cable or Subscriber Line through which Internet, video, and television is delivered to the home or business. Most WAPs implement the IEEE 802.11 standard which is a contention based standard for handling communications among multiple competing devices for a shared wireless communication medium. The WAP controls access to the shared communication medium using a multiple access methodology identified as Collision Sense Multiple Access (CSMA). CSMA is a distributed random access methodology first introduced for home wired networks such as Ethernet for sharing a single communication medium, by having a contending communication link back off and retry access to the line if a collision is detected, i.e. if the line is in use. Collisions however are not easily detected in wireless home network communications, and therefore a competing links are subject to a further refinement identified as Collision Avoidance, in one form of which a link sends a request over the wireless medium which only elevates to actual data transmission if clearance to send is received from the target device. Thus the contention based protocol for wireless home networks is identified as Collision Sense Multiple Access/Collision Avoidance (CSMA/CA). 
     Typically wireless LAN employ the a multi-tone modulation protocol known as orthogonal frequency division multiplexing (OFDM) in which multiple communication sub-channels or tones are used to transmit data. Typically, each tone has the same constellation or modulation protocol as every other tone. Additionally, bit loading and power mask may also be identical. 
     To improve bandwidth a wireless device such as a WAP or station may be equipped with multiple antennas and accompanying functionality for Multiple-input and multiple-output, or MIMO communication links. MIMO uses multiple antennas at both the transmitter and receiver to improve communication performance, via either spatial multiplexing of multiple communication streams or diversity of a single stream. MIMO is an important part of modern wireless communication standards for homes such as IEEE 802.11n. Further optimization of transmissions may from multiple antennae may be achieved with beam forming. Optimization includes performing matrix operations on transmitted data from each antenna to compensate for variations in the channel between each source antenna on the WAP and a corresponding target antenna on the target wireless device. 
     What is needed is a WAP with improved capability for MIMO detection. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method and apparatus for a wireless device with multiple antennae and configured to support orthogonal frequency-division multiplexed (OFDM), multiple-input multiple-output (MIMO) communications of a wireless local area network (LAN) among multiple wireless devices over a shared wireless communication medium on a shared communication channel. The wireless access device comprises: a mixed-mode MIMO detector configured to allocate a mix of linear and non-linear MIMO detectors among received OFDM sub-channels of the shared communication channel, thereby reducing a level of complexity associated with reception of MIMO streams exclusively via non-linear MIMO detectors. 
     The invention may be implemented in hardware, firmware or software. 
     Associated methods and means are also claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features and advantages of the present invention will become more apparent to those skilled in the art from the following detailed description in conjunction with the appended drawings in which: 
         FIGS. 1A ,  1 B are combined hardware block and data structure diagrams of multiple-input and multiple-output (MIMO) communications exploiting diversity of a single stream and spatial multiplexing of multiple streams respectively; 
         FIG. 2  is an exploded combined hardware block and data structure diagram of  FIG. 1B  showing individual orthogonal frequency division multiplexed (OFDM) sub-carriers of the MIMO communications; 
         FIG. 3A  is a graph showing relative signal resolution capability of representative MIMO detectors; 
         FIG. 3B  is a graph showing relative signal resolution capability of a representative mix of selected linear and non-linear MIMO detectors shown in  FIG. 3A  among selected OFDM sub-carriers; 
         FIG. 3C  is a graph showing relative signal resolution capability of a representative mix of selected linear and non-linear MIMO detectors shown in  FIG. 3A  among selected OFDM sub-carriers under different channel modulation protocols; 
         FIG. 4  is a detailed hardware block diagram of a wireless device incorporating the mixed-mode MIMO detector of the current invention; and 
         FIG. 5  is a process flow diagram of processes associated with mixed-mode MIMO detection in accordance with another embodiment of the current invention. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     The main advantage of Orthogonal Frequency Domain Multiplexing systems (OFDM) is it&#39;s separation of information into separate and independent bins or orthogonal frequency sub-channels. This separation of the frequency bins will allow channel estimation and mitigation of any channel induced imperfection to happen on per channel basis. In fact, the sub-channel is chosen with coherency in mind in an OFDM system. Given that each sub-channel is relatively narrowband and coherent, one can pick the right algorithm to decode each sub-channel based on apriori knowledge or estimate of the sub-channel. The ‘right algorithm’ will be the one that gives the best compromise between performance and complexity for the given sub-channel. For example, for a sub-channel that is going through an AWGN channel, the ‘right algorithm’ will be to use a ZF or MMSE but for another sub-channel where the received streams are highly correlated the ‘right algorithm’ in the sense of providing the best performance would be MAP. 
     For a MIMO system of M T  transmit antenna and M R  receive antenna, the received signal per sub-channel can be expressed as:
 
 y=Hs+n  
 
     where H is M R  by M T  channel matrix and s is the transmitted OFDM signal. The transmit signal can be estimated using an MIMO detector. This detector estimates the symbol that minimizes an error metric assuming no apriori information about the transmitted bits/symbols via the following equation.
 
 s=Wy  
 
     For zero forcing (ZF) and maximum mean square error (MMSE) MIMO detectors the equalizer MIMO estimator (W) are given as follows respectively
 
 W   ZF =( H   H   H ) −1   H   H  
 
     
       
         
           
             
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     Alternatively, more complex and better performing non-linear MIMO detection can be used for estimating the transmitted data, for example, a Maximum Likelihood (ML) MIMO detector pick the estimate of transmit signal such that the estimate minimizes the error for all data in a sub-channel as below. 
     
       
         
           
             
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     These MIMO detection methods provide varying performance with different degrees of complexity with the better performing ones generally being more complex. There are also simplifications of the algorithms which generally come at some performance cost. For example, k-best algorithm with K=10 is less complex than K=16 but at the cost of performance. 
       FIGS. 1A ,  1 B are combined hardware block and data structure diagrams of multiple-input and multiple-output (MIMO) communications exploiting diversity of a single stream and spatial multiplexing of multiple streams respectively in a wireless local area network (LAN) formed between communication devices  102  and  114 . Typically communications comport with the IEEE 802.11* standard. 
     In  FIG. 1A  wireless communication devices  102  and  114  are shown. Communications are typically simplex, and in the example shown wireless communication device  102  is transmitting the same data on each of four antennae  104  to wireless communication device  114  which receives the communicated data on each of four antenna  110 . In the example shown in  FIG. 1A  a single input data stream “S 1 ” is copied into the transmit packets  100 A for redundant transmission from a corresponding one of the four antenna. Data is transmitted in symbols corresponding to a phase and or amplitude offset with respect to a carrier signal. The amount of time required to transmit a symbol is identified as a symbol interval. Additionally, orthogonal frequency division multiplexing (OFDM) is used to inject the transmitted data onto contiguous tones or sub-channels each of which  106 A,  106 B,  106 B,  106 D is shown for a corresponding one of the antennae. Each antennae transmits the same data, as every other antennae with the data split between the tones or sub-channels transmitted by each antennae. Data transmitted in this redundant manner concurrently from multiple spatially separated antennae exploits the spatial diversity to improve communications. On the wireless device  114  the received data including any noise “N”  112  introduced by the receiver components is subjected, in Mixed-mode MIMO detector  116 , to an appropriate MIMO detection, in this case Maximal Ratio Combining (MRC) or some other linear MIMO detector process to retrieve the transmitted data. Periodically the transmitter and receiver exchange channel models “H”  120 A which characterize the channel  108 A between transmitter and receiver in order to adjust or improve communications there between. This channel model is a matrix the coefficients of which correspond to the differences in the transmit paths between the subject transmit and receive antennae. The dimensions of the matrix correspond to the number of transmit and receive antennae. Matrix H has relatively uniform coefficients which indicate a high degree of correlation between all paths, corresponding to a “line of sight” channel  108 A which supports communication without intervening walls or objects. 
     In  FIG. 1B  the wireless communication devices  102 ,  114  have detected a communication channel  108 B which has relatively high degree of scattering brought about by the walls and objects within the encompassing home or business structure. Communications are again simplex, and again exploits multiple antennae but with a significant difference in the data transmitted. The environmental scattering which might initially seem dis-advantageous is instead exploited using a second MIMO methodology identified as spatial multiplexing in which different data is sent from each antennae and further from each of the sub-channels associated therewith and the differences in each communication path from transmit to receive antennae are exploited to detect the originally transmitted data. 
     In the example shown wireless communication device  102  is now transmitting four different streams “S 1-4 ” of data on each of the four antennae  104  to wireless communication device  114  which receives the communicated data on each of four antenna  110 . The different streams may be derived either from the same source or from different sources such as audio, video or data. In the example shown in  FIG. 1B  a single data source, is split into four unique streams which are bundled into four unique packets  100 B for transmission from a corresponding one of the four antenna. Data is transmitted in symbols corresponding to a phase and or amplitude offset with respect to a carrier signal. The amount of time required to transmit a symbol is identified as a symbol interval which is typically slightly longer for spatially multiplexed data than for the diversity example shown in  FIG. 1A . Again, orthogonal frequency division multiplexing (OFDM) is used to inject the transmitted data onto contiguous tones or sub-channels each of which  106 E,  106 F,  106 G,  106 H is shown for a corresponding one of the antennae. Each antenna transmits the unique data from each packet with the data split between the tones or sub-channels transmitted by each antennae. Data transmitted in this non-redundant manner concurrently from multiple spatially separated antennae exploits the spatial multiplexing to improve communications. On the wireless device  114  the received data including any noise “N”  112  introduced by the receiver components is subject, in Mixed-mode MIMO detector  116 , to an appropriate MIMO detection, in this case to Spherical Decoding (SD) or some other computationally complex non-linear MIMO detector process to retrieve the transmitted data. Periodically the transmitter and receiver exchange channel models “H”  120 B which characterize the channel  108 B between transmitter and receiver in order to adjust or improve communications there between. This channel model is a matrix the coefficients of which correspond to the differences in the transmit paths between the subject transmit and receive antennae. The dimensions of the matrix correspond to the number of transmit and receive antennae. Matrix H has relatively small off diagonal coefficients and a rather prominent set of diagonal components which indicate a low degree of correlation between all paths, a.k.a. uncorrelated data paths, corresponding to an architectural environment  109  with relatively high scattering of communications of “un-correlated or scattered” channel  108 B resulting from intervening walls or objects. 
       FIG. 2  is an exploded combined hardware block and data structure diagram of  FIG. 1B  showing individual orthogonal frequency division multiplexed (OFDM) sub-carriers of the MIMO communications. The individual sub-carriers, a.k.a. tones, of each toneset or carrier  106 E,  106 F,  106 G,  106 H are shown. Each is identified by an index number which in the example shown ranges from “0” to “99”. In a communication channel  108 B with high levels of scattering and corresponding generally un-correlated communication paths, it may nevertheless be the case that certain ones of the sub-carriers or tones are semi-correlated with one another and thus susceptible to MIMO detection which is are as computationally complex as that required for others of the sub-channels. The current invention exploits these differences specifically in the mixed-mode MIMO detector  116  which is one of the receive path components in both wireless devices  102  and  114 . In the embodiment shown the mixed-mode MIMO detector includes a bank or pool of available linear  202  and non-linear  204  detectors and an allocator  200  for determining which received sub-carrier is routed to which detector. The detectors may be implemented in hardware, software or firmware without departing from the scope of the claimed invention. 
     In the example shown in  FIG. 2  for each of the sub-channel sets, a.k.a. tone-sets:  106 E,  106 F,  106 G,  106 H, the signal modulated on each tone having an index “99” follows a direct path from the transmitting antenna to each of the four receive antennae  110 . The corresponding sub-channel matrix  212  H 99  exhibits this relatively high degree of correlation between the transmit paths in terms of the relatively uniform matrix coefficients. 
     Conversely, and by way of example, the signal modulated on each tone having an index “0” follows a less direct path with relatively greater scattering from the transmitting antenna to each of the four receive antennae  110 . The corresponding sub-channel matrix  210  H 0  exhibits this relatively low degree of correlation between the transmit paths in terms of the relatively modest off-diagonal matrix coefficients and the relatively large diagonal coefficients. 
     Sub-channel matrix H 0  indicates that the multiple transmit paths for tone “0” are un-correlated or have a low degree of correlation. Conversely, sub-channel matrix H 99  exhibits relatively higher degree of correlation or is semi-correlated. In an embodiment of the invention the sub-carrier allocator  200  of the mixed-mode MIMO detector allocates, non-linear and relatively complex MIMO detector processes, such as Maximum Apriori (MAP), Spherical Decoding (SD), K-BEST or V-BLAST to MIMO detection for the sub-channel having index “99”, while allocating the linear and relative inexpensive MIMO detector processes, such as Minimum Mean Square Error (MMSE) or Zero Forcing (ZF) to MIMO detection for the sub-channel having index “0”. 
     The benefits to this mixed or blended approach to MIMO detection are a decrease in resources and time required to process received MIMO transmissions. 
       FIG. 3A  is a graph showing relative signal resolution capability of representative MIMO detectors in terms of packet error rate (PER) on the vertical axis and signal-to-noise ratio (SNR) in decibels on the horizontal axis. 
     Generally the non-linear MIMO detection processes are operable at much lower SNR than the less computationally complex linear MIMO detection processes. SNR versus PER is shown for a group of linear MIMO detector processes including: ZF  300  and MMSE  302 . SNR versus PER is also shown for a group of non-linear MIMO detector processes including: K-BEST 10    310 , K-BEST 16    312 , SD  314 , and MAP  316 . The subscript in the case of the KBEST examples referring to the number of nodes “K” retained during the tree search. 
       FIG. 3B  is a graph showing relative signal resolution capability of a representative mix of selected linear and non-linear MIMO detectors shown in  FIG. 3A  among selected OFDM sub-carriers. Spherical Decoding (SD)  314  has the best signal resolution capability but is computationally very expensive particularly as the constellation size supported by the communication channel increases as shown in the following figure. Minimum Mean Square Error (MMSE) has the worst signal resolution capability but is computationally relatively inexpensive and is independent of constellation size. 
     Line  330  shows a 5 db gain in signal resolution capability realized by randomly assigning a mix of linear and non-linear detectors to tones/sub-carriers. In the example shown 50% of the tones/sub-carriers are allocated linear MIMO detection, e.g. MMSE, and the remaining 50% of the sub-carriers are allocated to non-linear MIMO detection, e.g. SD, with the resultant 5 db gain. 
     Intelligent allocation of linear and non-linear MIMO detectors between sub-carriers based on their associated levels of channel correlation produces further benefits on the order of 7 db and with further reduction in computational complexity. 
     Line  332  shows a 7 db gain in signal resolution capability realized by intelligently assigning a mix of linear and non-linear detectors to tones/sub-carriers. In the example shown 75% of the tones/sub-carriers are allocated linear MIMO detection, e.g. MMSE, and the remaining 25% of the sub-carriers are allocated to non-linear MIMO detection, e.g. SD, with the resultant 7 db gain. 
       FIG. 3C  is a graph showing relative signal resolution capability of a representative mix of selected linear and non-linear MIMO detectors shown in  FIG. 3A  among selected OFDM sub-carriers under different channel modulation protocols. The need for MIMO detector mixing is particularly evident at elevated constellation sizes as shown in  FIG. 3C . 
       FIG. 3C  on the left vertical axis shows relative processing MIPS associated with representative linear and non-linear MIMO detection processes discussed above. The horizontal axis shows whether and how that consumption varies as a function of modulation protocol and associated constellation size. Constellation sizes associated with: binary phase-shift key (BPSK), quadrature phase-shift key (QPSK), quadrature amplitude modulation (QAM)  16  and, quadrature amplitude modulation (QAM)  64  are shown. The right vertical axis shows the percent mix between representative linear, e.g. MMSE and non-linear, e.g. K-BEST, MIMO detector processes on the sub-channels of a received spatially multiplexed MIMO data. 
     Generally the linear MIMO detector processes consume less resources and that minimal consumption does not vary with constellation size as shown by the MIPS versus constellation size line  362  for MMSE. Conversely, non-linear MIMO detection processes consume significantly greater resources and that consumption increases exponentially with regard to constellation size as shown for K-Best  364 . 
     Blending linear and non-linear MIMO detectors subject to processing constraints requires a consideration of modulation protocol and associated constellation site in order to maintain constant resource utilization, e.g. MIPS, required for MIMO detection. In the example shown a blend of K-BEST allocated to MIMO detection on 90% of the received sub-channels and MMSE allocated to MIMO detection on the remaining 10% of the received sub-channels is appropriate for the small constellation sizes associated with BPSK. However as constellation size increases to that associated with QAM-64 K-BEST MIMO detection can only be allocated to 5% of the received sub channels and MMSE MIMO detection to the remaining 95% of the sub-channels if resource usage is to be held constant. 
       FIG. 4  is a detailed hardware block diagram of a wireless device  114  shown in  FIGS. 1A ,  1 B and  2 , incorporating the mixed-mode MIMO detector of the current invention incorporating an embodiment of the Mixed-mode MIMO Detector discussed above in  FIGS. 1A ,  1 B and  2 . 
     The MIMO receive path components include antennas  442 A,  442 B,  442 C,  442 D, coupled to Rf module  440  and baseband module  460  of the wireless LAN stage  430 , which implements the IEEE 802.11 standard. 
     The WLAN stage includes four discrete MIMO receive paths. A first MIMO receive path originates with the antenna  442 A, and includes: low noise amplifier (LNA)  444 A, the tunable oscillator  448  and mixer  446 A which down convert the received data channel, for filtration by the channel filter  450 A, conversion in the ADC  452 A, domain conversion in the DFT  462 A, spatial demapping in spatial demapper  464 , demapping in the constellation demapper  470 A, deinterleaving in deinterleaver  472 A, multiplexing in stream multiplexer  474  and decoding and descrambling in the decoder and descrambler  476  which couples to the packet based bus  404 . 
     The second MIMO receive path originates with the antenna  442 B, and includes: LNA  444 B, the shared tunable oscillator  448  and mixer  446 B which down convert the received data channel, for filtration by the channel filter  450 B, conversion in the ADC  452 B, domain conversion in the DFT  462 B, spatial demapping in spatial demapper  464 , demapping in the constellation demapper  470 B, deinterleaving in deinterleaver  472 B, multiplexing in stream multiplexer  474  and decoding and descrambling in the shared decoder and descrambler  476  which couples to the packet based bus  204 . 
     The corresponding third and fourth MIMO receive path components are labeled with the “C” and “D” suffix respectively. 
     The spatial demapper  464  includes a switch fabric  466  and banks of allocable linear and non-linear detectors  468 B and  468 A respectively. The switch fabric  466  under control of the mixed-mode MIMO detector controller  406  processes received sub-channels with a corresponding allocated one of the MIMO detectors  468 A,  468 B the resultant output of which is passed by the switch fabric to the corresponding one of the demappers  470 A-D. 
     The mixed-mode MIMO detector controller includes: a switch fabric controller  408 , an allocator  410 , a channel monitor  412 , a sub-channel correlation monitor  414 , a resource monitor  416  and storage  420 . In operation the channel monitor  412  monitors the received channel do determine MIMO channel parameters such as: stream count, constellation size, modulation protocol, bit loading and power level. The resource monitor  416  monitors processing resource levels. The sub-channel correlation monitor  414  determines the relative or absolute sub-channel correlation levels, e.g. un-correlated, semi-correlated, or highly correlated for each sub-channel. The allocator allocates sub-channels to MIMO detectors based on the information obtained by the monitoring components  412 ,  414 ,  416  as well as information contained in an estimated MIPS lookup table  422  in storage  420  and further in an embodiment of the invention based on channel histories  424  maintained in storage  420  by each of the monitoring components and by the allocator. Generally, the allocator balances available resources against sub-channel MIMO detection requirements to optimize MIMO detection. The switch fabric controller  408  takes the required mix of linear and non-linear MIMO detectors as determined by the allocator  410  and configures the switch fabric  466  of the spatial demapper accordingly. 
     In an embodiment of the invention where the wireless LAN device  114  comprises a wireless access point (WAP) the device also includes a broadband interface  402  for interfacing with a modem  400 . 
       FIG. 5  is a process flow diagram of processes associated with mixed-mode MIMO detection in accordance with another embodiment of the current invention. After startup in process  500  control passed to process  502  in which MIMO channel characteristics/parameters are determined including: data stream count, constellation size, modulation protocol, bit loading and power level. 
     Next, in process  504 - 506  a determination is made as to sub-channel correlation across all sub-channels. In an embodiment of the invention sub-channel correlation is determined by converting the sub-channel matrix into a correlation matrix “R” where R=H n   H *H n . Here “H n H” is the Hermetian of the sub-channel matrix. Next the Eigenvalues for the Correlation matrix are determined. Finally the sub-channels are ordered or ranked based on the disparity of the eigenvalues of each associated correlation matrix. The more disparate the resultant eigenvalues the less correlated the transmit paths for the associated sub-channel. 
     In an alternate embodiment of the invention a less computationally intensive method of ranking the relative correlation of each sub-channel is disclosed. In this embodiment of the invention the prominent column of each sub-channel matrix is determined based on a simple cumulative sum of the first or second norm of each column in each matrix. In each matrix the column with the highest absolute value is the prominent column of that matrix. Next the sub-channels are ordered in descending order of the magnitude of their associated prominent column&#39;s magnitude. Sub-channels the prominent columns of which have the highest cumulative magnitudes have the most highly correlated transmit paths and vice versa. 
     In an embodiment of the invention, non-linear and relatively complex MIMO detector processes, such as MAP, SD, K-BEST or V-BLAST will be allocated to MIMO detection for the sub-channels exhibiting a high degree of correlation, while the linear and relative inexpensive MIMO detector processes, such as MMSE or ZF will be allocated to MIMO detection for the sub-channel having a relatively low degree of correlation. 
     Next in process  508  a determination is made as to available resources e.g. hardware, firmware or software constraints, e.g. processor MIPS, for MIMO detection. Then in process  510  a determination is made as to the requisite mix of linear and or non-linear MIMO detector processes required for optimized MIMO detection given channel parameters and sub-channel correlations across all sub-channels and given available resources. 
     In process  512  sub-channels are allocated to MIMO detectors based on the mix determination of process  510 . Then in process  514  the received communications are subject to the associated MIMO detection after which remaining base band receive path processes are completed on the received MIMO stream(s) in process  516 . 
     Finally, in decision process  518  a determination is made as to the onset of the next symbol interval, or packet interval, in the event of which control returns to process  502  for re-iteration of all the above processes. 
     The components and processes disclosed herein may be implemented as software, hardware, firmware, or a combination thereof, without departing from the scope of the Claimed invention. 
     The foregoing description of a preferred embodiment of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously many modifications and variations will be apparent to practitioners skilled in this art. It is intended that the scope of the invention be defined by the following claims and their equivalents.