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  • Can the BMS Algorithm Decode Up to Errors? Yes, but with Some Additional Remarks

    Shojiro SAKATA  Masaya FUJISAWA  

     
    LETTER-Coding Theory

      Vol:
    E93-A No:4
      Page(s):
    857-862

    It is a well-known fact that the BMS algorithm with majority voting can decode up to half the Feng-Rao designed distance dFR. Since dFR is not smaller than the Goppa designed distance dG, that algorithm can correct up to errors. On the other hand, it has been considered to be evident that the original BMS algorithm (without voting) can correct up to errors similarly to the basic algorithm by Skorobogatov-Vladut. But, is it true? In this short paper, we show that it is true, although we need a few remarks and some additional procedures for determining the Groebner basis of the error locator ideal exactly. In fact, as the basic algorithm gives a set of polynomials whose zero set contains the error locators as a subset, it cannot always give the exact error locators, unless the syndrome equation is solved to find the error values in addition.

  • Implementation of SS No. 7 Functions in a Large-Capacity Switching Node with Distributed Configuration

    Etsuo MASUDA  Hideo SHIMBO  Katsuyuki KAWASE  Masanori HIRANO  

     
    PAPER-Switching

      Vol:
    E83-B No:12
      Page(s):
    2635-2647

    Methods for implementing SS7 functions are proposed for a large-capacity decentralized switching node; they satisfy the condition of hiding distributed configurations from adjacent nodes. First, line accommodation and acquisition methods are clarified for a large-capacity switching node in which multiple modules are used to realize trunk circuits and SS7 signaling links. Two methods are then proposed for allocating SS7 functions within the switching node. One distributes the functions over multiple circuit-switched modules (distributed allocation) while the other centralizes the functions in dedicated signaling modules (centralized allocation). We quantitatively evaluate both methods in terms of node scale versus the number of modules and signaling links required, the inter-module data transfer rate required, and the node traffic handling capacity when a particular module fails. From the evaluation results, we show that the distributed allocation should be employed for small-scale nodes and the centralized allocation for large-scale nodes. We also show the effectiveness of a method for avoiding a characteristic problem that arises when a particular module fails. Finally, we implement an experimental system as an example.