unlike conventional linearization schemes , which linearize either the tx or the rx , one embodiment of the invention linearizes the compression induced by both the transmitter 202 and the receiver 204 of a tx - rx radio link , by application of a post - compensation , decompression technique within the receiver 204 , as described in connection with fig2 . this can save implementation cost as compensation circuitry within the receiver 204 alone can handle the distortion of compression from both the transmitter 202 and the receiver 204 . for the “ decompression ” feedback loop , an error is computed that is reduced or preferably driven to zero , and the signal coming out of the analog to digital converter ( adc ) 206 is decompressed 208 . one embodiment of the invention is used with orthogonal frequency - division multiplexing ( ofdm ). ofdm can be used in many applications and with many interface standards , such as , but not limited to , wi - fi ( ieee 802 . 11 ), wimax ( ieee 802 . 16 ), and the like . the distortion compensation techniques are typically embodied in a receiver front end . the nonlinearity of a typical radio frequency ( rf ) receiver , as well as most other data communication links , is of a compressing nature . that is , the small signal gain of the system decreases as the input signal level increases . embodiments of the invention receive the output of the adc 206 and remap the signal according to the remapping function post ( adc [ n ]), which can be implemented in the de - compress block 208 . the remapping function post [.] is an estimate of n − 1 [. ], which is the true inverse of the channel nonlinearity . fig4 illustrates an example of an implementation for the remapping function post [.] in a receiver . while illustrated in the context of a relatively low order polynomial , the skilled artisan will appreciate that the principles and advantages described herein are applicable to higher order polynomials as well . as shown in fig4 , the remapping function post [.] can be divided into two pieces : post [ x ]= x + c ( x − b ) m ; x ≧ b ( eq . 4 ) in equation 4 , a relatively small number of parameters remain that are to be adjusted in a feedback loop : border b , coefficient c , and / or exponent m . note that “ p ” ( fig7 ) can refer to one or more of the parameters in the set of b , c , and m . in one embodiment , the system fixes or predetermines b and m , and adjusts c in a feedback loop as will be described later in connection with fig7 . a broad range of values can apply , and these values can also scale with a range for x . in one example with a 10 - bit system , the value of m is about 2 . 5 , the value of b is around 100 , and the value of c is around 20 × 10 − 6 . these values can be used for initialization of the process . of course , there are other functions that can implement the decompression . for example , the decompression function can be implemented with a lookup table , a multiplier , or the like . other techniques will be readily determined by one of ordinary skill in the art . a feedback loop uses the computation of an error . one embodiment of the invention examines the statistics of the decompressed signal , and compares these statistics to reference values . an example of one such statistic is the peak - to - average power ratio ( par ) of a signal . for example , in the case of orthogonal frequency - division multiplexing ( ofdm ) transmission , the e [ par ( ofdm )], i . e ., the expected value of the par of an ofdm signal , is typically a function of the number n of ofdm symbols examined , i . e ., e n [ par ]. in equation 5 , peak n ( ofdm ) represents the peak par value over n ofdm symbols , and rms n ( ofdm ) represents the root mean square ( rms ) par value over n ofdm symbols . it is worth noting that the par as n →∞ ( where n is the number of ofdm symbols included in the calculation ) for most ofdm systems is a function of the number of sub - carriers used , and has as an upper limit expressed by equation 6 . however , most systems use some form of scrambling that further reduces the upper limit expressed in equation 6 . the average par can be computed as a function of the number of symbols and this number can be used as the reference in the error generation . an example of an average or mean par ( vertical axis ) versus a number n ofdm symbols ( horizontal axis ) is shown in fig5 . as illustrated in fig5 , the average or mean par of the signal carrying the symbols can be expected to vary over the number of symbols evaluated . in one embodiment , an expected value for par is based on the number of symbols evaluated , e . g ., based on a predetermined number n of symbols such as 4 , and a determined par , e . g ., a measured par , based on the signal as received is compared to the expected par . the resulting error is then used as an input in an adaptive feedback loop to adjust one or more decompression parameters for improved linearity of the decompressed signal . it should be noted that a single ofdm symbol itself typically encodes for a relatively large number of samples of data . for example , a single ofdm symbol can encode for 1000 samples of data , such as encountered with 10 - bit encoding . one embodiment of the adaptive radio decompression system is illustrated in fig6 . an output x of an analog to digital converter ( adc ) is provided as an input to a decompression function , represented by the curve to the upper right . an output post [ x ] of the decompression function is provided as an input to a functional block for measuring the peak - to - average ratio ( par ), which measures the par of the output post [ x ] over the applicable number n of symbols evaluated . the number of ofdm symbols evaluated at a time can vary in a broad range . in one example , the number n evaluated is 4 ofdm symbols . other applicable values for n can be used and will be readily determined by one of ordinary skill in the art . the par is determined by evaluating the symbols in the symbol range and using the equation for par expressed in equation 5 . for example , the peak value can correspond to the highest power value of the signals forming the symbols for the symbol range . the average can be determined by calculating an rms power value of these signals . the average par can be used as an expected par e n [ par ] and is retrieved from , for example , a memory table 602 . the memory table 602 is represented by the graph towards the lower left of fig6 . the number n of symbols evaluated at a time can be used as an index ( horizontal axis ) to look up an expected average par value . for example , with n equal to 4 symbols , the average par should be about 8 . 2 decibels ( db ). the measured par 604 and the expected par 606 are compared as represented by the summation block 608 ( the average par is inverted for the summation ). the difference , or error , is then provided as an input to a block 612 for updating the parameter p ( e . g ., b , c , or m , fig4 ) for adaptively updating the decompression function 614 as indicated by the block to the upper right . the decompression function 614 is implemented by , for example , the remapping function expressed in equation 4 . the decompression can reduce the effects of the nonlinearity of compression . for example , such decompression techniques can be used to improve the bit error rate ( ber ) of a compressed signal . an example of the process is also illustrated in the flowchart of fig7 . in one embodiment , the digital output x of the adc 206 is used as an index for a lookup table that is updated with the decompression function 614 , and the retrieved value stored in the lookup table ( corresponding to an output of the decompression function ) provides a decompressed digital output for decision circuitry , such as slicers . fig7 is a flowchart that generally illustrates a process for adaptive radio decompression according to one embodiment of the invention . it will be appreciated by the skilled practitioner that the illustrated process can be modified in a variety of ways without departing from the spirit and scope of the invention . for example , in another embodiment , various portions of the illustrated process can be combined , can be rearranged in an alternate sequence , can be removed , and the like . in addition , portions of the process can be implemented by hardware , by software ( including firmware ), or by a combination of both hardware and software . the software can be executed by a microprocessor , microcontroller , signal processor , or the like . the process begins by initializing 710 the decompression parameter p . the decompression parameter p represents the one or more parameters that are being adaptively adjusted . of course , even parameters that stay fixed should be initialized . the process then proceeds to capture ( state 720 ) n ofdm symbols , e . g ., capture data for n ofdm symbol periods from a signal carrying n ofdm symbols . in one embodiment , the value of n is predetermined , e . g ., 4 ofdm symbols . the sample data for the n ofdm symbols can be retained , for example , in a memory device such as ram . the process then proceeds to decompress 730 the received data according to the values of the parameters and the decompression function . for example , the decompression function described in connection with equation 4 can be used . the process then proceeds to determine 740 the peak - to - average power ratio ( par ) for the decompressed samples that make up the n ofdm symbols . the determined par is based on actual data . the determined par can be determined by , for example , application of equation 5 . the process then proceeds to determine 750 an error between the determined par obtained in state 740 and an expected par e n [ par ], such as from the memory table 602 ( fig6 ). in one embodiment , the determined par obtained in state 740 is filtered before it is used in the error computation . for example , the sample data can be filtered prior to the determination of the par . this can reduce incorrect adjustments due to interference and the like . the process then proceeds to adjust 760 the one or more parameters p based on the determined error . for example , in one embodiment , the system holds parameters b and m fixed , and adaptively adjusts parameter c asp . the parameter μ accounts for the update rate of the feedback loop and will vary depending upon the frequency of update of the feedback loop . the process can then return from the adjustment state 760 to the capturing state 720 as appropriate for another adaptive adjustment of the parameters . many variations will be apparent to one of ordinary skill in the art . for example , instead of using an expected or average par , an actual par can be calculated ( reconstructed ) from the final digital ofdm symbols after the decision circuitry , such as , after slicing from soft symbols to hard symbols . while illustrated above in the context of adjusting parameter c for parameter p , while keeping parameters b and m fixed , the principles and advantages described herein are applicable to the adjustment of the other parameters . for example , in one embodiment , for the adaptive adjustment of parameter p , parameter b ( equation 4 ) can be adjusted while parameters c ( equation 4 ) and m ( equation 4 ) remain fixed . in one embodiment , for the adaptive adjustment of parameter p , parameter m ( equation 4 ) can be adjusted while parameters b ( equation 4 ) and c ( equation 4 ) remain fixed . combinations of these parameters can also be adaptively adjusted in the state 760 ( fig7 ). in one embodiment , when combinations of these parameters are used , the adjustments to the parameters in the state 760 should adjust the decompression monotonically with the vector index such that the error adjustment is stable for the vector defined by the adjusted parameters . in addition , higher order polynomials can be used , and these polynomials can have additional parameters for adaptive adjustment . there is a class of advanced embodiments which can make use of the gain setting of variable gain receiver . the linearity of a receiver can vary with the gain setting of the variable gain stages of the receiver , and in one embodiment , the adaptively adjusted parameter can correspond to a gain setting for a variable gain amplifier ( vga ). the feedback loop described in fig6 and 7 can adjust for nonlinearities that vary with the gain of a variable gain amplifier . in one embodiment , rather than estimating the average par from the number of symbols received , the transmitter 202 ( fig2 ) provides the actual average par information to the receiver 204 ( fig2 ), which then reads and uses the actual average par information . because the transmitter 202 transmits the actual ofdm symbols , the transmitter 202 can compute the actual average par information over selected ofdm symbols . for example , the actual average par information can be provided in a header or a payload of a data packet , which is then converted to analog by a dac 210 ( fig2 ) and then transmitted . the par computation can be performed by , for example , an auxiliary circuit within an ofdm multiplexer . in addition , an indication of the n symbols over which the actual average par is calculated can also be transmitted . an embodiment of the invention can reduce the cost or increase the performance of wireless or wireless physical layer ( phy ) ics . furthermore , it allows a valuable design trade - off to be made : increase noise performance at the expense of linearity performance , since linearity can be compensated later . various embodiments have been described above . although described with reference to these specific embodiments , the descriptions are intended to be illustrative and are not intended to be limiting . various modifications and applications may occur to those skilled in the art .