CN109104200B - 近似参数自适应 - Google Patents

近似参数自适应 Download PDF

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CN109104200B
CN109104200B CN201810636877.8A CN201810636877A CN109104200B CN 109104200 B CN109104200 B CN 109104200B CN 201810636877 A CN201810636877 A CN 201810636877A CN 109104200 B CN109104200 B CN 109104200B
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channel parameters
adaptive algorithm
input signal
detector
channel
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CN109104200A (zh
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M·马罗
J·贝洛拉多
V·B·阿什
R·阿胡贾
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Seagate Technology LLC
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Abstract

本发明提供了一种装置，所述装置可包括被配置为使用一组信道参数处理输入信号的电路。所述电路可以使用第一自适应算法产生第一组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数。所述电路可以进一步基于所述第一组信道参数，并且基于使用所述第一自适应算法生成的第三组信道参数和使用所述第二自适应算法生成的第四组信道参数之间的关系来近似第二自适应算法的第二组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数。此外，所述方法还可包括使用所述第二组信道参数作为所述一组信道参数来执行对所述输入信号的所述处理。

Description

近似参数自适应
发明内容
在某些实施例中，一种装置可包括被配置为使用一组信道参数处理输入信号的电路。该电路可以使用第一自适应算法产生第一组信道参数，以供电路使用作为处理输入信号时的一组信道参数。该电路可以进一步基于第一组信道参数，并且基于使用第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系近似第二自适应算法的第二组信道参数，以供电路使用作为处理输入信号时的一组信道参数。此外，该方法还可包括使用第二组信道参数作为一组信道参数来执行对输入信号的处理。
在某些实施例中，一种系统可包括被配置为使用一组参数处理输入信号的信道电路，以及被配置为使用第一自适应算法产生第一组信道参数以供电路使用作为处理输入信号时的一组信道参数的自适应电路。该自适应电路可以被进一步配置为基于第一组信道参数，并且基于使用第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系来近似第二自适应算法的第二组信道参数，以供信道电路使用作为处理输入信号时的一组信道参数。此外，自适应电路可以被配置为将第二组信道参数输出到信道电路，以用作处理输入信号时的一组信道参数。
在某些实施例中，一种方法可包括通过被配置为使用一组信道参数并且使用第一自适应算法处理输入信号的电路生成第一组信道参数，以供电路使用作为处理输入信号时的一组信道参数。该方法还可包括基于第一组信道参数，并且基于使用第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系近似第二自适应算法的第二组信道参数，以供电路使用作为处理输入信号时的一组信道参数。另外，该方法还可包括使用第二组信道参数作为一组信道参数执行对输入信号的处理。
附图说明
图1是根据本公开的某些实施例的包括近似的参数自适应的通信信道的框图；
图2是根据本公开的某些实施例的近似的参数自适应方法的流程图；
图3是根据本公开的某些实施例的近似的参数自适应方法的流程图；
图4是根据本公开的某些实施例的包括近似的参数自适应的系统的框图。
具体实施方式
在以下对实施例的详细描述中，参考了形成其部分的附图，并且其中以说明的方式示出。应当理解，所述的各种实施例的特征可以组合，可使用其他实施例，并且可在不脱离本公开的范围的情况下进行结构变化。还应当理解，在不脱离本公开的范围的情况下，本文的各种实施例和示例的特征可以组合、交换或移除。
根据各种实施例，本文所述的方法和功能可被实现为在计算机处理器或控制器上运行的一个或多个软件程序。根据另一个实施例，本文所述的方法和功能可被实现为在计算设备(例如，使用磁盘驱动器的个人计算机)上运行的一个或多个软件程序。包括但不限于专用集成电路、可编程逻辑阵列和其他硬件设备的专用硬件具体实施同样可被构造为实现本文所述的方法和功能。此外，本文所述的方法可被实现为包括指令的计算机可读存储介质或设备，所述指令在被执行时使得处理器执行所述方法。
本公开总体上涉及参数自适应，并且在一些实施例中，本公开可涉及基于由第二自适应算法产生的参数的变化针对第一自适应算法近似参数自适应。
某些系统诸如电气、电子、电机驱动、处理或其他系统可接收感兴趣的信号，并且根据参数处理该信号。例如，通信系统或磁记录存储系统的读取信道可以利用自适应参数来处理输入信号。在一些系统中，检测器可以基于均衡样本序列和自适应参数来生成数据序列。自适应组件可包括用于自适应检测器的参数的各种自适应功能或算法。例如，自适应参数可以是软输出维特比算法(SOVA)检测器(例如，有限脉冲响应滤波器(FIR)或SOVA检测器的数据相关FIR(DDFIR))的滤波器电路的系数或抽头，或者是参数诸如SOVA检测器的分支方差或分支偏差。
自适应算法类型的示例可以包括最小误码率(MBER)自适应、最小均方误差(MMSE)自适应、最小均方自适应、递归最小二乘(RLS)自适应，以及类似的自适应算法。
在一些实施例中，可以使用第一自适应算法来在至少一组情形下(例如，在正常操作下)调整一组自适应参数。在至少某些其他情形下，可以基于由第一自适应算法产生的自适应参数集合与由第二自适应算法产生的自适应参数集合之间的预定关系，使用第二自适应算法针对第一自适应算法近似自适应参数集合。例如，可以在第一自适应算法可能不适合处理或其中第一自适应算法可能提供错误更新的情形下执行此类近似。
这种系统的一个示例将在下文参照图1进行论述。
参见图1，示出了包括近似参数自适应的通信信道的框图，并且该通信信道通常被指定为100。系统100可以包括可以联接至均衡器104的模数转换器(ADC)102。均衡器104可以联接至检测器106和自适应组件108。检测器106可以联接至解码器HO和自适应组件108。自适应组件108可连接至检测器106。另外，解码器110可以包括来自通信信道100的输出并且被连接到自适应组件108。自适应组件108可包括用于存储生产数据112或者访问存储在单独存储器(未示出)中的生产数据112的存储器。
ADC 102、均衡器104、检测器106、自适应组件108和解码器HO中的每一者可以是独立电路、片上系统(SOC)、固件、处理器或未列出的其他系统，或它们的任何组合。
如下面详细讨论的，在图1所示的实施例中，调整的参数是检测器106的参数。尽管本文的论述利用读取信道的检测器的参数作为示例，但本发明所公开的技术和系统也可应用于其他电路或参数。根据本公开，许多变型形式对于本领域的普通技术人员而言将是显而易见的。
在操作中，ADC 102可以以一定间隔对连续时间信号x(t)114进行采样，并且可以对该信号进行量化以产生数字化的样本序列x116。
均衡器104可以接收数字化样本序列x116，并且生成均衡样本序列y 118。在一些示例中，均衡器可以操作以缩短信道的码间干扰(ISI)长度或存储器。此外，均衡器102可用于吸收随时间发生的输入信号或噪声统计中的变化。一般来讲，均衡器104可以向检测器106产生一致的已知输入信号，使得检测器106可以基于自适应组件提供的系数或参数p124估计与信号x(t)114相对应的数据序列108。
检测器106可以操作以基于均衡样本序列y 118和由自适应组件108提供的参数或系数p 124确定(或估计)与信号x(t)114相对应的比特值的数据序列bo 120。具体地讲，可以将参数p124提供给检测器用于估计数据序列bo 120。在一些实施例中，数据序列bo 120可以表示每个比特值为0或1的概率。值124可以表示为这些概率的比率的对数，并且可被称为对数似然比或LLR。检测器106可基于信道响应的信息(例如，针对每个可能的写入/传输的数据模式的预期信道输出)来生成LLR值。在一些示例中，检测器108可采用软输出维特比算法(SOVA)。
所生成的数据序列bo 120可以被传递到解码器110，如果数据序列bo120被成功解码，该解码器可以生成解码数据b 122，或者可以生成表示每个比特是0或1的概率的外部信息或EXT(未示出)。解码器110可以基于使用的代码的结构生成解码数据b 122或者EXT。虽然为了便于说明而未示出，但是在一些具体实施中，可以将EXT返回检测器(例如，用于作为由检测器106和解码器HO执行的迭代解码过程的一部分)。
自适应组件108可以操作以接收均衡样本序列y 118、数据序列bo 120和解码数据b 122，并且调整检测器106的参数124。
更具体地讲，检测器106可以是SOVA检测器。在一些实施例中，可以在正常操作中使用最小误码率(MBER)自适应来调整SOVA检测器的参数124。更具体地讲，可以在正常操作中在扇区上运行SOVA检测器参数的MBER自适应，其中在该操作中，解码器HO可以正确解码写入数据(例如，由此重新产生写入数据并将b 122提供给自适应组件)。解码数据b 122可以连同存储的均衡样本序列y 118(或它们的子集)一起被反馈到MBER自适应。
在MBER自适应可能不适合或其中MBER自适应可能提供错误更新的情况下，可基于由LMS自适应算法产生的自适应参数集合，并且基于MBER产生的自适应参数集合和由LMS产生并作为生产数据112存储的自适应参数集合之间的预定关系确定近似的更新MBER参数。例如，当解码器未能对写入数据(b 122)进行解码并且将使用可包括位错误(例如，决策引导的自适应)的检测器判定数据(bo 120)执行自适应时，MBER自适应可能不适合，或者检测器参数的MBER自适应可能提供错误更新。当执行决策引导的自适应时，MBER成本函数可能导致自适应系数以加剧决策错误的方式移动，这可能进一步降低检测器性能。也可使用其他因素或条件触发近似的MBER参数生成。例如，当感兴趣的扇区在经过阈值次数的迭代解码迭代之后未能解码并且具有来自相邻扇区的完全不同的噪声统计时(其中在相邻扇区上的自适应可能无用)，可以触发近似的MBER参数生成。
可以在制造期间或在字段的训练过程期间生成制造数据112。就硬盘驱动器或其参数在介质上变化的其他设备而言，可针对每个存储单元(例如，扇区、页面等)或针对可对应于轨道组的较大区域或区(这是下面讨论的示例中的情况)而生成生产数据112。在生成制造数据期间，可以使用已知的写入数据生成MBER和LMS参数集合。可以存储生成的参数集合、关于参数集合之间关系的信息(例如，差值)或两者。
在操作中，当解码器HO未能对存储在扇区中的数据进行解码时，可使用检测器判定120生成当前LMS参数集合。然后，在一些实施例中，在训练中生成并作为制造数据112存储的MBER和LMS参数集合之间的差可被添加到当前LMS参数以生成近似MBER参数。
下面给出了具体地针对分支偏差将该过程应用于示例类型的SOVA检测器参数。
对数域维特比或SOVA中的分支度量值可采取以下形式：
Figure BDA0001701802830000051
其中σ2可以是分支方差，z可以是该分支的DDFIR输出(或者无数据相关滤波情况下的FIR)，并且Zt可以是分支偏压。利用LMS自适应中的MMSE成本函数，Zt＝zavg＝E[z](例如，分支均值的前提可以是该分支为正确分支)。然而，在存在复杂噪声统计的情况下，利用MBER成本函数进行调整可能会使zt与zavg分离，以最小化BER。
在制造期间，对于每个区域，可以确定MBER参数集合Zt和测量的MMSE参数集合zavg。制造参数集合可以表示为zt (m)和zavg (m)。
当解码器HO未能在字段操作中解码特定扇区时，更新的MBER参数可允许对扇区进行解码。例如，如果解码失败是由于磁道挤压造成的，则如果使用了该扇区的更新MBER参数，则扇区可能会解码。但是，可能不会使用MBER自适应，因为自适应将基于可能包含错误的检测器判定。相反，自适应组件108可以使用检测器判定生成或调整当前MMSE参数
Figure BDA0001701802830000061
自适应组件然后可以使用当前MMSE参数zavg (f)近似更新的MBER参数：
zt (f)≈zt (m)+(zavg (f)-zavg (m))
由于对于制造参数集合和字段内错误恢复参数集合而言MBER偏差与MMSE偏差之间的差值可能相同，因此这可能是有效的。由于Zt(m)和zavg (m)存储在制造过程的制造数据112中，在从检测器判定和LMS算法计算zavg (f)之后自适应组件可执行近似。在一些实施例中，LMS自适应可以利用来自解码器的总信息(例如，LDPC分片的总信息)，其可以从检测器判定和外部信息中导出。
尽管上述示例响应于解码失败来计算近似参数，但是实施例不限于此。例如，响应于触发条件(例如，解码器HO未能将扇区解码三十(30)次)，自适应组件108可以以动态和连续的方式生成近似参数，并且基于检测器106或允许使用近似参数的自适应组件108中的模式选择使用该近似参数。
参见图2，示出了近似参数自适应的方法的流程图，该方法通常指定为200。更具体地讲，流程图200可以是制造或训练操作，以生成如上文相对于图1详细描述的制造数据112(例如，对于当前区)。
在202，系统可以接收当前区的连续时间输入信号的多个样本以及相应的已知数据。然后，系统可以在204处基于连续时间输入信号生成数字化样本序列。在206处，系统可以基于数字化样本生成均衡采样序列。
接下来，系统可以在208处基于均衡样本序列和可更新的MBER检测器参数产生基于MBER的输入信号的数据序列估计。以在210处，系统可基于均衡样本序列和可更新的MMSE检测器参数产生基于MMSE的输入信号的数据序列估计。在一些实施例中，解码器可以针对基于MBER的估计和基于MMSE的估计中的一个或多个执行解码操作。
在212处，系统可以基于已知值和MBER估计对MBER检测器参数执行基于MBER的自适应过程。然后，在214处，系统可以基于已知值和MMSE估计对MMSE检测器参数执行基于MMSE的自适应过程。
然后，在216处，系统可确定MBER检测器参数和MMSE检测器参数是否已稳定。如果参数已稳定，则在218处，系统可存储当前区的基于MBER的检测器参数和基于MMSE的检测器参数(例如，作为生成数据112)。在一些实施例中，系统还可以确定并存储MBER检测器参数与MMSE检测器参数之间的关系数据(例如，差值)。除此之外或另选地，当利用区参数时可生成一些或全部关系数据。如果参数尚不稳定，则系统可返回到208以进行另外的自适应操作。虽然未示出，但在一些实施例中，当参数尚未稳定时，也可以重复另外的操作诸如操作202至206中的一个或多个操作，或者可以对新样本执行稳定性确定，直到参数对于该区的数据样本总体上已稳定。
参见图3，示出了近似参数自适应的方法的流程图，该方法通常指定为300。更具体地讲，流程图300可以是在读取或接收操作期间的检测、解码和近似参数自适应，并且可以如上文相对于图1所详述的那样执行。
在操作中，在302处，系统可以例如针对当前数据扇区接收连续时间输入信号的多个样本。在304处，系统可以基于连续时间输入信号例如使用ADC生成数字化样本序列。接下来，在306处，系统可以基于数字化样本生成均衡采样序列。
在308处，系统可基于均衡样本序列和可更新的MBER检测器参数(例如，使用SOVA检测器)产生输入信号的数据序列估计。然后，可以在310处例如使用LDPC解码器对检测器估计来执行解码操作。
在312处，系统可以确定解码器是否成功。如果是，系统可以停止当前扇区的操作，输出解码的数据序列，并且在314处，使用MBER自适应基于成功解码的数据序列更新基于MBER的检测器参数。如果解码器不成功，则系统可以在316处确定当前扇区的解码尝试的阈值次数是否已经失败。如果不是，则系统可以返回到308以进行附加检测和解码尝试。如果对于当前扇区已经发生阈值次数的解码失败，则系统可以在318处开始近似参数自适应。更具体地讲，在318处，系统可以通过使用基于MMSE的检测器参数执行检测操作，对基于MMSE的检测器估计上执行解码并且使用MMSE自适应更新基于MMSE的检测器参数，以调整当前扇区的基于MMSE的检测器参数。
接下来，在320处，系统可以根据基于MMSE的检测器参数，并且根据当前区的MBER检测器参数和基于MMSE的检测器参数的先前值的关系数据来近似更新的MBER检测器参数。例如，可以通过将先前生成并存储的MBER检测器参数和基于MMSE的检测器参数之间的差值添加至当前扇区的当前生成的基于MMSE的检测器参数，以生成近似的更新MBER检测器参数，如以上关于图1所讨论的。
然后，在322处，系统可执行检测器操作以基于均衡样本序列和近似的更新MBER检测器参数产生输入信号的数据序列估计，并且基于使用近似的更新MBER检测器参数生成的估计来执行解码操作(例如，可以使用近似的更新MBER检测器参数执行一个或多个迭代检测和解码操作)。虽然未示出，但如果322处的解码操作成功，则可以针对当前扇区输出解码结果，并且可以基于近似的更新MBER检测器参数和MBER检测器参数中的一者或两者使用成功解码结果执行MBER自适应。如果322处的解码操作不成功，则可以触发其他恢复操作，或者过程300可以以错误状态终止。
针对方法200和300列出的所有步骤都可应用于具有自适应参数的系统。如上所述，其他自适应算法可代替MBER和MMSE，并且这些处理可用于其他电路例如解码器、均衡器、ADC等等的参数。根据本公开，许多其他变型形式将是显而易见的。用于执行该方法中的操作的组件和电路可以是分立的，或者集成到片上系统(SOC)或其他电路中。此外，这些步骤可以在处理器(例如，数字信号处理器)中执行、在软件中实现、经由固件实现或通过其他手段来执行。
参见图4，示出了包括近似参数自适应的系统的框图，并且该系统通常被指定为400。系统400可以是数据存储设备(DSD)的示例，并且可以是系统100的示例性具体实施。DSD 416可以任选地连接到主机设备414并且可从该主机设备移除，该主机设备可以是具有存储数据的设备或系统，诸如台式计算机、膝上型计算机、服务器、数字视频录像机、影印机、电话、音乐播放器、未列出的其他电子设备或系统，或者它们的任何组合。数据存储设备416可经由基于硬件/固件的主机接口电路412与主机设备414进行通信，该主机接口电路可包括允许DSD 416与主机414物理连接和断开连接的连接器(未示出)。
DSD 416可包括可以是可编程控制器的系统处理器402以及相关联的存储器404。系统处理器402可以是片上系统(SOC)的一部分。缓冲器406可以在读取和写入操作期间临时存储数据，并且可包括命令队列。读取/写入(R/W)信道410可以在对数据存储介质408进行写入操作期间对数据进行编码，并且在从数据存储介质进行读取操作期间对数据进行重构。数据存储介质408被示出和描述为硬盘驱动器，但也可以是其他类型的磁介质，诸如闪存介质、光学介质或其他介质，或者它们的任何组合。
RAV信道410可以一次接收来自多于一个数据存储介质的数据，并且在一些实施例中，还可以同时接收诸如来自读取头的多于一个输出的多个数据信号。例如，具有二维磁记录(TDMR)系统的存储系统可具有多个读取或记录元件，并且可以同时或几乎同时从两个轨道进行读取。多维录音(MDR)系统可以接收来自多个源的两个或更多个输入(例如，记录头、闪存、光学存储器等)。R/W信道410可组合多个输入并提供单个输出，如本文的示例所述。
框418可实现系统和方法100、200和300的系统和功能中的全部和部分。在一些实施例中，框418可以是集成到R/W信道410中的独立电路，被包括在片上系统、固件、软件或它们的任何组合中。
本文所述的说明、示例和实施例旨在提供对各种实施方案的结构的一般理解。这些说明并非旨在用作采用本文所述结构或方法的装置和系统的所有元件和特征的完整描述。在查看本公开后，许多其他实施例对于本领域技术人员而言可以是显而易见的。可通过本公开利用并得到其他实施例，使得可在不脱离本公开的范围的情况下进行结构和逻辑替换和变化。例如，附图和以上描述提供了可改变的架构和电压的示例，诸如系统的设计要求。此外，虽然在本文中已说明和描述了具体实施例，但应当理解，被设计为实现相同或相似目的的任何后续布置可以替代所示的具体实施例。
本公开旨在覆盖各种实施例的任何和全部后续改型或变型。在查看说明书后，上述示例的组合以及本文中未具体描述的其他实施例对于本领域技术人员而言将是显而易见的。此外，图示仅仅是代表性的，可能未按比例绘制。图示中的某些比例可能被放大，而其他比例可能被缩小。因此，本公开和附图被认为是例示性的，而非限制性的。

Claims (20)

1.一种用于近似参数自适应的装置，包括：
电路，所述电路被配置为使用一组信道参数处理输入信号，所述电路被进一步配置为：
使用第一自适应算法产生第一组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数；
基于所述第一组信道参数，并且基于使用所述第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系来近似所述第二自适应算法的第二组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数；以及
使用所述第二组信道参数作为所述一组信道参数执行对所述输入信号的所述处理。
2.根据权利要求1所述的装置，还包括所述电路还包括检测器，所述检测器使用所述一组信道参数执行对所述输入信号的所述处理。
3.根据权利要求2所述的装置，还包括所述检测器是软输出维特比算法(SOVA)检测器，并且所述一组信道参数是所述SOVA检测器的分支偏差。
4.根据权利要求2所述的装置，还包括所述第一自适应算法是最小均方误差MMSE自适应算法，并且所述第二自适应算法是最小误码率MBER自适应算法。
5.根据权利要求1所述的装置，还包括存储器，所述存储器存储使用所述第一自适应算法生成的所述第三组信道参数、以及使用所述第二自适应算法生成的所述第四组信道参数。
6.根据权利要求1所述的装置，还包括所述电路还包括检测器，所述检测器使用所述一组信道参数执行对所述输入信号的所述处理，所述电路被进一步配置为：
使用由所述第二自适应算法生成的第五组信道参数作为处理所述输入信号时的所述一组信道参数执行所述输入信号的处理，以产生检测结果；
确定由所述检测器使用所述第五组信道参数产生的一个或多个检测结果的解码已失败阈值次数；以及
至少部分地响应于所述确定所述解码已经失败阈值次数，使用所述第二组信道参数作为所述一组信道参数执行对所述输入信号的所述处理。
7.根据权利要求6所述的装置，还包括所述电路被配置为：
确定由所述检测器使用所述第二组信道参数产生的一个或多个检测结果的解码已成功；以及至少部分地响应于所述确定所述解码已成功，使用所述第二自适应算法，使用所述成功解码结果调整所述第二组信道参数。
8.根据权利要求1所述的装置，还包括所述电路还包括检测器，所述检测器使用所述一组信道参数执行对所述输入信号的所述处理，所述电路被进一步配置为：
使用所述第二自适应算法生成的第五组信道参数作为处理所述输入信号时的所述一组信道参数执行对所述输入信号的处理，以产生检测结果；
确定由所述检测器使用所述第五组信道参数产生的一个或多个检测结果的解码在小于失败阈值次数后成功；以及
至少部分地响应于所述确定所述解码已成功，使用所述第二自适应算法，使用所述成功解码结果调整所述第五组信道参数。
9.根据权利要求1所述的装置，还包括：
存储器，所述存储器存储使用所述第一自适应算法生成的所述第三组信道参数、以及使用所述第二自适应算法生成的所述第四组信道参数；
所述输入信号是从磁存储介质的扇区读取的回读信号；并且
所述第三组信道参数和所述第四组信道参数对应于包括所述扇区并且在制造过程期间生成的所述磁存储介质的区域，所述制造过程包括回读写入包括所述扇区的所述磁存储介质的所述区域的已知数据。
10.一种用于近似参数自适应的系统，包括：
信道电路，所述信道电路被配置为使用一组参数处理输入信号；自适应电路，所述自适应电路被配置为：
使用第一自适应算法产生第一组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数；
基于所述第一组信道参数，并且基于使用所述第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系来近似所述第二自适应算法的第二组信道参数，以供所述信道电路使用作为处理所述输入信号时的所述一组信道参数；以及
将所述第二组信道参数输出到所述信道电路，以用作处理所述输入信号时的所述一组信道参数。
11.根据权利要求10所述的系统，还包括所述信道电路是软输出维特比算法(SOVA)检测器，并且所述一组信道参数是所述SOVA检测器的分支偏差。
12.根据权利要求11所述的系统，还包括所述第一自适应算法是最小均方误差MMSE自适应算法，并且所述第二自适应算法是最小误码率MBER自适应算法。
13.根据权利要求11所述的系统，还包括：
存储器，所述存储器存储使用所述第一自适应算法生成的所述第三组信道参数、以及使用所述第二自适应算法生成的所述第四组信道参数；
所述输入信号是对应于磁存储介质的扇区的数字化样本序列；并且
所述第三组信道参数和所述第四组信道参数对应于包括所述扇区并且在制造过程期间生成的所述磁存储介质的区域，所述制造过程包括回读写入包括所述扇区的所述磁存储介质的所述区域的已知数据。
14.根据权利要求13所述的系统，还包括：
ADC电路，所述ADC电路被配置为基于对应于所述扇区的回读信号生成一个或多个ADC样本；
均衡器电路，所述均衡器电路被配置为接收ADC样本并均衡所述ADC样本以生成所述数字化样本序列；和
解码器，所述解码器被配置为接收使用所述一组参数处理所述输入信号的所述SOVA检测器的输出，并且对所述SOVA检测器的所述输出执行解码。
15.根据权利要求10所述的系统，还包括所述信道电路是检测器，并且所述自适应电路被进一步配置为：
将第五组信道参数输出到所述信道电路，以用作处理所述输入信号时的所述一组信道参数以产生一个或多个检测结果，所述第五组信道参数使用所述第二自适应算法产生；
确定使用所述第五组信道参数对由所述检测器产生的所述一个或多个检测结果的解码已失败阈值次数；以及
至少部分地响应于所述确定所述解码已经失败阈值次数，使用所述第二组信道参数作为所述一组信道参数执行对所述输入信号的所述处理。
16.根据权利要求10所述的系统，还包括所述信道电路是检测器，并且所述自适应电路被进一步配置为：
将第五组信道参数输出到所述信道电路，以用作处理所述输入信号时的所述一组信道参数以产生一个或多个检测结果，所述第五组信道参数使用所述第二自适应算法产生；
确定使用所述第五组信道参数对由所述检测器产生的所述一个或多个检测结果的解码在小于失败阈值次数后已成功；以及
至少部分地响应于所述确定所述解码已成功，使用所述第二自适应算法，使用所述成功解码结果调整所述第五组信道参数。
17.一种用于近似参数自适应的方法，包括：
通过被配置为使用一组信道参数并且使用第一自适应算法处理输入信号的电路产生第一组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数；
基于所述第一组信道参数，并且基于使用所述第一自适应算法生成的第三组信道参数和使用第二自适应算法生成的第四组信道参数之间的关系来近似所述第二自适应算法的第二组信道参数，以供所述电路使用作为处理所述输入信号时的所述一组信道参数；以及
使用所述第二组信道参数作为所述一组信道参数执行对所述输入信号的所述处理。
18.根据权利要求17所述的方法，还包括电路包括软输出维特比算法(SOVA)检测器，所述SOVA检测器使用所述一组信道参数作为所述SOVA检测器的分支偏差以执行所述输入信号的所述处理。
19.根据权利要求18所述的方法，还包括所述第一自适应算法是最小均方误差MMSE自适应算法，并且所述第二自适应算法是最小误码率MBER自适应算法。
20.根据权利要求17所述的方法，还包括：
所述输入信号是对应于磁存储介质的扇区的数字化样本序列；
所述第三组信道参数和所述第四组信道参数对应于包括所述扇区并且在制造过程期间生成的所述磁存储介质的区域，所述制造过程包括回读写入包括所述扇区的所述磁存储介质的所述区域的已知数据；并且
所述第三组信道参数与所述第四组信道参数之间的所述关系是差值。
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