Abstract:
In a recording/playback system, increased information is achieved by 4 level biased magnetic recording where the maximum amplitude 4 level recording signal drives the medium&#39;s magnetization into a nonlinear region of its transfer function. The bias does not eliminate distortion at the maximum signal input level, however the system&#39;s signal to noise ratio is improved due to an increase in the amplitude of the playback signal resulting from the increased recording level. The nonlinear mapping capability of a neural network provides equalization of playback signals distorted due to the record/playback nonlinearity. The 4 level recorded signals provide a factor of 2 in information storage compared to binary recording, and quadrature amplitude modulation (QAM) combined with the 4 level recording technique provides an additional factor of 2, for a factor of 4 in the information content stored.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to signal recording and playback equalization, and in particular to recording and equalization of signals of a magnetic recorder. 
     2. Description Relative to the Prior Art 
     In order to attain higher transfer densities in the recording of digital magnetically recorded data, the trend has been towards ever increasing pulse packing on the medium. Typically the digital recording uses some form of binary encoding, such as NRZI, where binary 1&#39;s are represented as transitions from one state of magnetization of the medium to another state, and 0&#39;s are represented by no change in magnetization at specific clock times. The two states are usually the plus and minus saturation states of the medium, and the time of magnetization reversal during a transition is immaterial. Accordingly, the hysteresis of the medium does not directly affect the recording or recovery of the data in straight binary recording. 
     However, increasing the pulse packing of the medium leads to signal distortion on playback of the signal. The crowding together of the transitions causes peak shifts in the recovered signals, and adjacent transitions are no longer readily identifiable on playback., This is due to non-linearities arising from the mutual interference of sequentially recorded pulses on the magnetic medium, and becomes more severe as the recording density is increased. 
     It has been known in the prior art to use a neural network to represent nonlinear maps as part of a signal equalizer in a binary recording/playback system at high packing densities. U.S. Pat. No. 5,361,327 issued in the name of Takahashi discloses a magnetic disk drive system in which a computer simulated neural network is used to derive the parameter values of an equivalent “hard wired” neural network employed as a waveform equalizer. Similarly, Padden in U.S. Pat. No. 5,594,597 teaches the use of a neural network in a magnetic playback system to equalize a playback signal subject to playback non-linearities due to interference between crowded pulses. Padden applies his disclosure to a magnetic disk recording system. Such systems require varying the recording density as a function of the radius to maintain constant density recording, and also requires control of a phase locked loop to maintain constant recorded wavelength as a function of the radial location of the recorded zone. It will be appreciated that the above patents disclose magnetic recording systems in which only the two signals levels characteristic of saturation binary recording are recorded on the magnetic medium. The information content of such binary recording is limited by the use of two recording levels, and, as previously stated, by the non-linearities introduced by pulse crowding. In the binary recording systems of the prior art, the only way to increase information content is by higher density recording with a concurrent severe loss of signal-to-noise ratio and increase in the problem of playback equalization due to pulse crowding. 
     SUMMARY OF THE INVENTION 
     The present invention teaches the use of multilevel, high density magnetic recording to allow storage of quantized data with increased information content. The use of 4 level recording increases the information content by a factor of 2 compared to the binary recording of the prior art. The invention utilizes biased magnetic recording to eliminate the hysteresis present in the magnetic medium, thereby providing a single valued input/output transfer function necessary in a multilevel recording system. While the transfer function is single valued, it is linear only over the range from 0 magnetization to about −10 db below the saturation level. If recording is restricted solely to the linear range, then the signal to noise ratio (SNR) is correspondingly decreased. To utilize the maximum SNR available for recording, recording at levels into the non-linear region of the transfer function is permitted, and a nonlinear map having the ability to approximate a wide variety of multidimensional non-linear transformations is implemented to provide the waveform equalization necessary to recover the recorded data. In the preferred embodiment of this invention, realization of this map, representing the distortions of the communication channel, is realized using a neural network. The invention is disclosed using both 4 level amplitude modulation for an improvement by a factor of 2 in information content over straight binary recording, and by a combination of 4 level amplitude modulation and phase modulation in a quadrature amplitude modulation (QAM) configuration which provides a corresponding improvement by a factor of 4 over straight binary recording. More levels of recording both in amplitude and in phase are possible depending upon the signal-to-noise ratio attainable in the recorder system. 
     While the invention has been described in terms of magnetic recording, it will be appreciated that the teachings of the invention may be applied to recording and playback of quantized information from other media capable of supporting multiple level analog signal recording, such as or thermoplastic tape recording. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention will be described with respect to the drawings of which: 
     FIG. 1 is a graph of magnetization of a recording material as a function of write current with ac bias, known in the art, 
     FIG. 2 is a block diagram of the recording circuitry of the invention, 
     FIG. 3 is a drawing of a binary coded sequence and its representation as 4 level signals recordable by the invention, 
     FIG. 4 is a drawing illustrating an example of recording magnetization of the medium in accordance with the invention, 
     FIG. 5 is a drawing illustrating the playback signal for the recorded example of FIG. 4, 
     FIG. 6 a  is a drawing of a neuron circuit, known in the art, 
     FIG. 6 b  is a drawing of a reduced form of the neuron circuit of FIG. 6 a,    
     FIG. 7 is a drawing of a neural network, known in the art, 
     FIG. 8 is a graph of a neuron output, known in the art, 
     FIG. 9 is a block diagram of a playback and equalization network in accordance with the invention, 
     FIG. 10 is a drawing of an array of signal constellation in a QAM system, known in the art, 
     FIG. 11-13 b  are drawings useful in understanding a second embodiment of the invention, 
     FIG. 14 is a block diagram of the circuits of the second embodiment of the invention, 
     FIGS. 15-16 are curves related to playback of signals of the second embodiment of the invention, and 
     FIGS. 17-1 to  17 - 9  are computer generated diagrams of the block diagram of the equalizer of the invention shown in FIG.  9 . 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     As is known in the art, the hysteresis of a magnetic recording medium can be eliminated by the addition of a high frequency bias signal to the signal record current. Additionally, the medium is also linearized over part of its range by the use of bias. The bias frequency is typically four to twenty times the band-edge frequency of the channel, and has an amplitude much larger than the signal current. A bias frequency of 2.5 Mhz is suitable for the recording of a bit rate signal of 300 Khz. 
     To set the channel for both linearity and maximum information capacity, the bias level and signal level are iteratively increased for maximum signal to noise ratio (SNR) at a 1%-3% distortion level. Under these conditions the channel is optimally set for linear recording, but the channel can be driven to 10 db more output at the price of increased distortion. As seen in FIG. 1, the output, which is proportional to the magnetization M RB  ( 12 ), is a linear function of the biased write current I W  up to level  16  in the positive direction or  16 ′ in the negative direction, after which points an additional 10 db in dynamic range of the output is available at increased distortion until the saturation levels  14 ,  14 ′ are reached. 
     The invention is broadly disclosed as the recording of quantized signal levels, and a first embodiment of the invention is implemented as the recording of a digital input stream. Consider the recording of a binary data sequence  18 , for example, 10011010001101, as a 4 level code, (FIG.  3 ). The sequence  18  is applied (FIG. 2) to a gating circuit  20  under control of clock  22 , and the sequence  18  is partitioned into 2 bit groups, i.e., 10, 01, 10, 00, 11, 01, which then feed a D/A converter  24 . As shown in FIG. 3 each two bit group, depending upon its value, is converted into 1 to 4 voltage amplitudes resulting in the 4 level quantized waveform  26 . The waveform  26  has been biased to provide 4 equi-spaced levels as follows: 
     
       
         
               
               
               
             
               
               
               
               
             
           
               
                   
                 TABLE I 
               
               
                   
                   
               
               
                   
                 Digital Value 
                 Voltage 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 00 
                 −1.5v 
                   
               
               
                   
                 01 
                 −0.5v 
               
               
                   
                 10 
                 0.5v 
               
               
                   
                 11 
                 1.5v 
               
               
                   
                   
               
             
          
         
       
     
     The waveform  26  is applied to the write circuit  28  where the bias signal from the bias source  32  is linearly added to the write current I W  driving the record head  30 . Also recorded at the beginning of each data sequence are synchronization signals from a synch generator  25 . Referring to FIG. 4 the 4 level bipolar input waveform  26  varies the write current I W  through the record head  30  to provide corresponding magnetization waveform  26 ′ on the magnetic tape  34 . (In the drawings, different but related elements are identified by the same reference character, albeit that the different elements are distinguished by primes.) On playback of the tape  34 , (FIG.  9 ), the output of a playback head  35  and associated amplifier  36  is approximated by the waveform  38  of FIG.  5 . 
     The playback signal  38  is equalized by a non linear map realized here as a neural network capable of a wide range of multidimensional nonlinear transformations to generate the output values equivalent to input data. Such neural networks, known in the art, consist of artificial “neurons” interconnected in layers. A typical non-linear model of a neuron, shown in FIG. 6 a , accepts inputs y p , each of which is weighted by a synaptic weight w kp , and the weighted inputs are then fed to a summing junction Σ ( 40 ). A fixed input y 0  with a weighing function w kO  is also applied to the summing junction  40  as an offset or bias amount. The output of the summing junction  40  is applied to an activation function  42 , which limits the output amplitude of the neuron. This activation function is approximated in the present invention by a sigmoid function having the form plotted in FIG. 8, and is represented by the expression y=(1+e −V ) −1 , where y is the output level of the activation function, and V is the output of the summing junction  40  feeding the activation function  42 . 
     A reduced form of the schematic of the neuron shown in FIG. 6 a  is illustrated in FIG. 6 b , and is useful when the focus of attention is directed to signal flow from neuron to neuron. In this representation, the neuron is represented as a single node, the entire neuron, including the summing junction and the activation function, being shown as a shaded circle with input lines directed to the circle and an output line directed away from the circle. The weighing values for the input signals, i.e. the w&#39;s of FIG. 6 a , are assumed to be incorporated into this schematic representation of the neuron of FIG. 6 b.    
     Referring to FIG. 7, a neural network  46  using the representation of FIG. 6 b  consists of a “hidden” layer of neurons  48 ,  50 ,  52 ,  54  and an output layer of neurons  56 , 58 . The network  46  is configured to accept eight input signals, Y 1 -Y 8  as will be explained below, and a threshold signal y 0 , for a total of 9 inputs. Each input, y 0 -y 8  feeds each one of the “hidden” layer neurons  48 , 50 , 52 , 54 , and at each neuron the inputs are “synaptically weighted” i.e. assigned weighing values, as described above. The outputs of the hidden layer  48 ,  50 ,  52 ,  54  are input to the neurons  56 ,  58  which are the output layer neurons, and are synaptically assigned weights in a similar manner. In addition to the outputs from each of the 4 hidden layer neurons, the neurons  56 , 58  have fixed inputs w 20  which set their threshold levels, for a total of 10 synaptically adjusted inputs. Therefore the total number of synaptic weights for the neural network (reference no. 46) is 36+10=46 weights. The outputs of the neurons  56 ,  58  are the outputs of the neural network  46 . 
     The neural network must first be trained by a learning process which establishes the synaptic weights and biases for each neuron. Typical random input vectors having known binary values are converted to equivalent 4 level signals and recorded. The resultant analog playback signals are digitized and applied to the neural network as inputs. The neural network generates equalized output signals that are compared to the applied known binary input sequences. The error between the inputs and the neural network response is driven to a minimum by an “error back-propagation” algorithm through the assignment of appropriate synaptic weights to each neuron. The use of this algorithm to establish synaptic weights for a neural network is known in the prior art, and is described in the book “Neural Networks” by Simon Haykin, MacMillan College Publishing Company, New York, 1994, pp.47-49. 
     The playback signal  38  of FIG. 5, both during the training process and for the data recovery process, is transformed before application to the neural network in the following way. Referring to FIG. 9, a playback head  35  feeds signal from the recorded tape  34  to a preamp  36  whose output  38  is digitized by an analog to digital converter  60  at a rate controlled by a sampling frequency generator  62 . This operation is the inverse of the digital to analog conversion performed prior to recording. The analog output  38  is digitized and partitioned to provide 2 bit groups in accordance with Table I, supra. 
     The recorded data begins with a synchronizing pattern followed by, i.e., thirty equilevel transitions. A synchronization detector  61  starts data recovery, i.e., thirty transitions after detecting synchronization. The digitized signal is also applied to a phase lock loop  64  operated from a reference frequency generator  66 . For binary input data having 600 kilo-transitions per second, (600 Khz bit rate), the sampling frequency generator  62 , and the reference frequency generator  66  were both adjusted for pulse output rates of 1.2 Mhz, i.e. repetition rates of 0.8333 microseconds. 
     A resampling circuit  68  creates sets of vectors from the digitized input signals which serve as inputs to the neural network equalizer  46 ′. These vectors consist of components which constitute a sequence of samples of the input signal having proper phase and the necessary time delay between samples. For an input sequence A(m)=z(mτ), the resampling circuit  68  generates and stores a sequence of vector components: x(mτ−φ), x(mτ+Δ−φ), x(mτ+2Δ−φ), x(mτ+3Δ−φ), x(mτ+4Δ−φ), x(mτ+5Δ−φ), x(mτ+6Δ−φ), x(mτ+7Δ−φ): where φ is the phase established by the PLL  64  and τ and Δ are the sampling time interval and time delay, i.e. each 0.8 microseconds in the example given above. The required number of vector components equals the number of transitions in each block of the input sequence, and the value of each vector component is stored in the resampling circuit  68  to be simultaneously applied as parallel inputs to the neural network. The above enumerated 8 vector components are collectively designated by the reference character  70 . 
     The vector components  70  are applied to the corresponding inputs y′ 1  to Y′ 8  of the neural network  46 ′ having the same neuron configuration as FIG.  7 . The neuron threshold sources are input via terminals w′ 10  and w′ 20 . By use of the “error back-propagation” algorithm known in the art, the 46 synaptic weights (including the weights assigned to the thresholds) are calculated in the training phase, and these weights are then stored to configure the neurons for equalization of the sequences of playback data. The training data contains 1950 samples, each sample consisting of an 8 dimensional input vector and a 2 dimensional output vector. The output vector is compared to the appropiate 2 bits of the original recorded binary signal value, and the difference is the error signal which drives the “error back-propagation” algorithm. 
     The neural network output consists of two floating point values corresponding to two independent binary bits. The following table summarizes the neural network voltage outputs and the correspondence to the original binary bit values. 
     
       
         
               
               
               
             
           
               
                   
               
               
                 Input Level 
                 Neural Network Output 
                 Bit Combination 
               
               
                   
               
             
             
               
                 0 
                 (0.1, 0.1) 
                 (0,0) 
               
               
                 1 
                 (0.1, 0.9) 
                 (0,1) 
               
               
                 2 
                 (0.9, 0.1) 
                 (1,0) 
               
               
                 3 
                 (0.9, 0.9) 
                 (1,1) 
               
               
                   
               
             
          
         
       
     
     During the data recovery process, the output value of each component of the neural net is compared to a level of 0.5 in a standard “slicer” circuit. If it is less than 0.5 the output equals 0, and if it is greater the output equals 1. 
     Following the above teachings, data was recorded and played back attaining an error rate of 2×10 −5 . 
     In a second embodiment of the invention, the four level amplitude modulation of the first embodiment is combined with phase modulation in a QAM configuration. This allows the compression and magnetic recording of 16 four bit groups within the bandwidth that would resolve 2 bits of straight binary recording. It is known in the art, that organizing the data into a square array constellation is an optimal configuration for QAM encoding of a channel. The following example demonstrates the organization of such a constellation. 
     Consider the recording and playback of the following binary sequence by means of QAM encoding: 111011010000011101001011. 
     For convenience of notation the following abbreviated designation of sequential arrangement of two contiguous digits is employed: 
     00→−1 
     01→1 
     10→−3 
     11→3 
     Using this convention, and dividing the above binary sequence into four bit segments, the above binary sequence translates into the following double digit combinations: 
     (3,−3) (3,1) (−1,−1) (1,3) (1,−1) (−3,3) 
     Notice that order is important both within and without the parentheses, that is, for example, (3,−3) is 1110, and these are the first four bits of the original binary sequence. Referring now to FIG. 10, an array comprised of 16 sub-squares, i.e.  72 , is the locus of the coordinates of the data points representing the constellation to be recorded. With respect to the orthogonal coordinate axes labelled I and Q, the centers of the sub-squares are at distances along the axes of d and  3   d  units. Thus the above pairs of data enclosed in parentheses may be plotted as I, Q Cartesian coordinates, as shown in FIG.  10 . These points  74 , 76 , 78 , 80 , 82 , 84  are to be recorded in the proper time sequence to preserve the timing of the original binary data. Each point,  74 - 84 , e.g.  74 , consists of two coordinates, an I (in phase ) coordinate specified as  74   I , and a quadrature coordinate specified as  74   Q . 
     Recording is effected by means of four sinusoidal signals, represented in FIG. 11, as I 3 , I 1 , Q 3 , Q 1  where the I and Q sine waves are of the same frequency, but are shifted in phase by 90 degrees with respect to each other. Also, the I 3  and Q 3  signals have three times the amplitude of the I 1  and Q 1  signals corresponding to the amplitudes of the signal levels to be recorded. Referring to FIG. 12, the signals of FIG. 11 are generated from a base frequency generator  86  whose output is I 3 , which is also applied to a ⅓ attenuator circuit  88 , and whose output drives a buffer  90 , having as output the signal I 1 . The output of sine wave generator  86  is also applied to a 90 degree phase shifter  92  feeding a buffer  94 , whose output is I 3 . The output of buffer  94  is also applied to a ⅓ attenuator circuit  96  which feeds a buffer whose output is the Q 1  sine wave. Hence, two sets of sine wave signals are generated; the first set consists of two in-phase signals of relative amplitudes 3:1, and the second set consisting of two in-phase signals of relative amplitudes 3:1, where the second set of signals are shifted in phase with respect to the first set by 90 degrees. It is to be noted that the sine waves of FIG. 13 b , have a definite time relationship to the transitions of the waveform of FIG. 13 a . Synchronization of these waveforms on the record side is maintained by use of conventional techniques. 
     Referring to FIG. 13 a , the I and Q signals of FIG. 10 are to be recorded with the relative amplitudes and the time sequence illustrated. Recording is effected by sequential selection of one of the signals I 3 , I 1 , Q 3 , Q 1  (FIG. 13 b ) to drive the write circuit  28  (FIG. 2) in 1:1 correspondence with the amplitudes of the I,Q signals shown in FIG. 13 a . A synchronizing sequence including a burst of the I 3  signal is also recorded which will allow recovery of I 3 , I 1 , Q 3 , Q 1  on playback. 
     Referring to FIG. 14, a playback head  35 ′ feeds a preamp  36 ′ having an output  38 ′. This output also is monitored by a synch detector  100  which gates out the playback I 3  signal from the recorded synch. A phase lock loop  102  is driven by this signal and runs at the rate of the I 3  which is recovered in circuit  104 . The I 3  feeds generator  106  which is identical in construction and performance to the circuitry of FIG. 11, and generates the 4 signals, I′ 3 , I′ 1 , Q′ 3 , Q′ 1 . These signals have the phase relationships illustrated in FIG. 11, and are in the proper phase relationship to the recorded I,Q signals. 
     The playback waveform FIG. 15, is sampled by the sample and hold circuit  108  at the peaks of the recovered I′ and Q′ signals, shown in FIG.  16 . At time  110 , the I′ signal is at a peak, and the playback signal is sampled ( 110 ′). The comparator  108  determines that the sample at  110 ′ has the amplitude I 3  and is positive. Hence, the first readback I value is +3. The next sampling occurs at the peak of Q′  112 , and the value of the playback signal  38 ′ ( 112 ′) has the amplitude Q 3 , and is negative as determined in the sample and hold/comparator  108 . Hence the first I,Q readouts are (3,−3), in agreement with the constellation of FIG.  10 . This process continues at each I′ and Q′ peak, and by sampling and holding the playback signals  38 ′ reproduces the 4 level waveform shown in FIG. 13 a . The output of the sample and hold/comparator feeds the A/D converter  60 ′ and the processing of the playedback information then follows the identical procedure through the corresponding remaining circuits  68 ′,  46 ″ as explained above in connection with FIG.  9 . 
     Referring to the circuits of FIG. 9, it is to be noted that the described circuitry, except for the magnetic tape recorder/reproducer and the A/D converters, was “breadboarded” using the program entitled “Labview” developed by National Instrument Co., Austin, Tex. on a PC digital computer. This program accepts the digital inputs from the A/D converter  60  and then simulates the operation and interconnections between the PLL  64 , resampling circuit  68  and neural network  46 ′. The program employs the error back-propagation algorithm to calculate the synaptic weights for the neurons of the neural network, and using these values provides the equalization and recovery for signals playedback from the magnetic tape transport. These weight values may also be used in a “hardwired” implementation of the equalization circuit of FIG. 9 using fixed electronic components, and where the weighing is performed by selected component adder networks at each neuron. 
     FIG. 17 is a “Labview” generated equivalent circuit diagram showing the interconnection of simulated elements making up the equalizer of the invention. 
     The invention has been described in detail with particular reference to preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. For example, depending upon the signal-to-noise ratio available in the system, more than 4 signal levels may be recorded with a corresponding increase in size of the neural network for playback equalization. An 8 level system would use a neural network providing 3 outputs generating 3 output bits rather than the 2 outputs described above for the 4 level system.