January 15 2007  月曜日

本文探讨了Scheme语言中amb运算符的不同实现方式,并深入解释了Monad的概念及其在Haskell中的应用。通过对比两种不同的编程模式,文章阐述了Monad如何帮助组织和抽象程序流程。

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  I almost forgot how to write a "amb" operator, a non-determination operator, in scheme.  Yes,
there is not only one way to implement "amb".  As matter of a fact, as I known, there are three
ways, two of them used macro.  I felt the following implementation easy to understand.

(define (now-fail)
  "amb exhauted!")

(define-syntax amb
  (syntax-rules ()
    ((_ e ...) (let ((old-fail now-fail))
                 (call/cc (lambda (sk)
                            (call/cc (lambda (fk)
                                       (set! now-fail
                                             (lambda ()
                                               (set! now-fail old-fail)
                                               (fk 'fail)))
                                       (sk e)))
                            ...
                            (now-fail)))))))

  Yesterday I browsed some articles on Monads.  What is monad?  It seem that none can give a
comfortable answer, even professors or experts.  In Haskell, a monad can adapted the following
operators definition.

data M a = ...

return :: a -> M a

(>>=) :: M a -> (a -> M b) -> M b

  The last operator is also called as "bind" operation.  It combind some functions which all
deal with a value of a Monad type.  There are three laws of a monad must be obeied.  They can
make sure that a monad can run correctly.

  1. (return x) >>= f == f x
  2. m >>= return == m
  3. (m >>= f) >>= g == m >>= (/x f x >>= g)

  It awaked up my memory about the laws of roborts.  From the above type definition and laws,
we can know how to write a monad.  There are a number of tutorial of monads.  I am not going to
study it deeply.  I just simulated a simple monad in scheme.

  By the way, list is also a monad in scheme or Haskell.  The "return" operator will make a list,
like [a].  The (>>=) is same "concatMap f [a]".

  The monad seems a kind of combining programming.  It separated the data and a sequence of algorithm.
A advantage of it is that we can add or append many computations as we wish.  Another advantage is
also obvious that each algrithm can modify respectively.

  There is a common application of a monad, I/O APIs.  No matter what a port, a file pointer, a file
description, or a stream, they all can be called as monads.  If you are familiar with the I/O package
of Java, you can feel easy to understand though it has not explicit (>>=) operator.

  If you are not foolish, I believe that you have found the relation between (>>=) and "decorative pattern".
In my opinion, because some OOP languages have not the featurings of lambda express, of passing a lambda
express as argument, such as Java, they have to adapted other ways, such as "decorative pattern", to
implement the (>>=) operator.

  Note that every function to take a value of monad must return a instance of a monad.  In other words,
the function must make a monad using a original or new value.  It is a key point that the (>>=) can combine
these functions.  The first law and second law have telled us the (>>=) operator combine actually a monad
and a computation function base on that monad.  The (>>=) must can get the value of a monad and feed it into
a monad's computation function.  These functions like a group of sieves to filter a kind of type data.  To
wrap a value into a monad type make the "bind" operator omitted the detail in a monad.

  The third advantage is also obvious.  Some algorithms, especially common algorithms, can be reused.  OK,
I can give my answer about what is monad in pan-system language.  That is just a trick of classification.
Usually, reusefully, flexibility is why people classification, especially in computation algorithm.  It also
can improve efficiency in a way, especially manual coding.  But at some case, such as hardware restriction,
or efficiency is the first quesion must be considered, classification must be proper.

  Everthing is good is also bad. 

内容概要:本文详细探讨了基于MATLAB/SIMULINK的多载波无线通信系统仿真及性能分析,重点研究了以OFDM为代表的多载波技术。文章首先介绍了OFDM的基本原理和系统组成,随后通过仿真平台分析了不同调制方式的抗干扰性能、信道估计算法对系统性能的影响以及同步技术的实现与分析。文中提供了详细的MATLAB代码实现,涵盖OFDM系统的基本仿真、信道估计算法比较、同步算法实现和不同调制方式的性能比较。此外,还讨论了信道特征、OFDM关键技术、信道估计、同步技术和系统级仿真架构,并提出了未来的改进方向,如深度学习增强、混合波形设计和硬件加速方案。; 适合人群:具备无线通信基础知识,尤其是对OFDM技术有一定了解的研究人员和技术人员;从事无线通信系统设计与开发的工程师;高校通信工程专业的高年级本科生和研究生。; 使用场景及目标:①理解OFDM系统的工作原理及其在多径信道环境下的性能表现;②掌握MATLAB/SIMULINK在无线通信系统仿真中的应用;③评估不同调制方式、信道估计算法和同步算法的优劣;④为实际OFDM系统的设计和优化提供理论依据和技术支持。; 其他说明:本文不仅提供了详细的理论分析,还附带了大量的MATLAB代码示例,便于读者动手实践。建议读者在学习过程中结合代码进行调试和实验,以加深对OFDM技术的理解。此外,文中还涉及了一些最新的研究方向和技术趋势,如AI增强和毫米波通信,为读者提供了更广阔的视野。
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