Secondly, The ML algorithm based on CP in being and its tracking performance are analyzed.
分析了现有的基于循环前缀的最大似然估计算法及其对频偏估计的跟踪。
While the MAP algorithm offers better performance than the ML algorithm, the computation is complex and not suitable for hardware implementation.
在相同条件下，最大后验概率译码算法比最大似然译码算法有更低误比特率，但由于计算量和复杂度过大而不适合硬件实现。
These two algorithms relax the constraints of ML algorithm and transform it into a convex problem which can be efficiently solved with a polynomial time.
这两类算法是在ML算法基础上放松约束条件，将问题转化为可在多项式时间内解决的凸优化问题。
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