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A gradual neural-network algorithm for jointly time-slot/code assignment problems in packet radio networks

机译:分组无线网络中时隙/代码联合分配问题的渐进式神经网络算法

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A gradual neural network (GNN) algorithm is presented for the jointly time-slot/code assignment problem (JTCAP) in a packet radio network in this paper. The goal of this newly defined problem is to find a simultaneous assignment of a time-slot and a code to each communication link, whereas time-slots and codes have been independently assigned in existing algorithms. A time/code division multiple access protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots with specific codes. GNN seeks the time-slot/code assignment with the minimum number of time-slots subject to two constraints: (1) the number of codes must not exceed its upper limit and (2) any couple of links within conflict distance must not be assigned to the same time-slot/code pair. The restricted problem for only one code is known to be NP-complete. The performance of GNN is verified through solving 3000 instances with 100-500 nodes and 100-1000 links. The comparison with the lower bound and a greedy algorithm shows the superiority of GNN in terms of the solution quality with the comparable computation time.
机译:针对分组无线网络中的时隙/代码分配联合问题(JTCAP),提出了一种渐进神经网络(GNN)算法。这个新定义的问题的目的是找到同时分配时隙和代码给每个通信链路,而时隙和代码已经在现有算法中独立分配。无冲突通信采用时/码分多址协议,在该协议中,以固定长度的时隙重复发送具有特定代码的数据包。 GNN寻求时隙/代码分配,但要考虑两个约束:最少时隙数:(1)代码数量不得超过上限;(2)冲突距离之内的任何两个链接都不得分配到相同的时隙/代码对。已知仅一个代码的受限问题是NP完全的。通过解决具有100-500个节点和100-1000个链接的3000个实例来验证GNN的性能。与下限和贪婪算法的比较显示了在解决方案质量和可比的计算时间方面,GNN的优越性。

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