The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Algoritma pengoptimuman gabungan baru yang dipanggil "Rangkaian neural berperingkat (GNN)" dibentangkan untuk masalah penjadualan siaran lengkap NP dalam rangkaian radio paket (PR). Rangkaian PR menyediakan perkhidmatan komunikasi data kepada satu set nod yang diedarkan secara geografi melalui saluran radio biasa. Protokol capaian berbilang pembahagian masa (TDMA) digunakan untuk komunikasi bebas konflik, di mana paket dihantar dalam pengulangan slot masa panjang tetap yang dipanggil kitaran TDMA. Memandangkan rangkaian PR, matlamat GNN adalah untuk mencari kitaran TDMA dengan masa tunda minimum bagi setiap nod untuk menyiarkan paket. GNN untuk N-nod-M-slot masalah kitaran TDMA terdiri daripada rangkaian saraf dengan N
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Salinan
Nobuo FUNABIKI, Junji KITAMICHI, "A Gradual Neural Network Algorithm for Broadcast Scheduling Problems in Packet Radio Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 5, pp. 815-824, May 1999, doi: .
Abstract: A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NP-complete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the N-node-M-slot TDMA cycle problem consists of a neural network with N
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_5_815/_p
Salinan
@ARTICLE{e82-a_5_815,
author={Nobuo FUNABIKI, Junji KITAMICHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Gradual Neural Network Algorithm for Broadcast Scheduling Problems in Packet Radio Networks},
year={1999},
volume={E82-A},
number={5},
pages={815-824},
abstract={A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NP-complete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the N-node-M-slot TDMA cycle problem consists of a neural network with N
keywords={},
doi={},
ISSN={},
month={May},}
Salinan
TY - JOUR
TI - A Gradual Neural Network Algorithm for Broadcast Scheduling Problems in Packet Radio Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 815
EP - 824
AU - Nobuo FUNABIKI
AU - Junji KITAMICHI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1999
AB - A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NP-complete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the N-node-M-slot TDMA cycle problem consists of a neural network with N
ER -