Nobuo Funabiki

A neural network parallel algorithm for channel assignment problems in cellular radio networks

The channel assignment problem involves not only assigning channels or frequencies to each radio cell. but also satisfying frequency constraints given by a compatibility matrix. The proposed parallel algorithm is based on an artificial neural network composed of nm processing elements for an n-cell-m-frequency problem. The algorithm runs not only on a sequential machine but also on a parallel machine with up to a maximum of nm processors. The algorithm was tested by solving eight benchmark problems where the total number of frequencies varied from 100 to 533. The algorithm found the solutions in nearly constant time with nm processors. The simulation results showed that the algorithm found better solutions than the existing algorithm in one out of eight problems. >

A parallel algorithm for broadcast scheduling problems in packet radio networks

A parallel algorithm based on an artificial neural network model for broadcast scheduling problems in packet radio networks is presented. The algorithm requires n*m processing elements for an n-mode-m-slot radio network problem. The algorithm is verified by simulating 13 different networks. >

A three-stage heuristic combined neural-network algorithm for channel assignment in cellular mobile systems

A three-stage algorithm of combining sequential heuristic methods into a parallel neural network is presented for the channel assignment problem in cellular mobile communication systems in this paper. The goal of this NP-complete problem is to find a channel assignment to requested calls with the minimum number of channels subject to interference constraints between channels. The three-stage algorithm consists of: (1) the regular interval assignment stage; (2) the greedy assignment stage; and (3) the neural-network assignment stage. In the first stage, the calls in a cell determining the lower bound on the total number of channels are assigned channels at regular intervals. In the second stage, the calls in a cell with the largest degree and its adjacent cells are assigned channels by a greedy heuristic method. In the third stage, the calls in the remaining cells are assigned channels by a binary neural network. The performance is verified through solving well-known benchmark problems. Especially for Sivarajans benchmark problems, our three-stage algorithm first achieves the lower bound solutions in all of the 13 instances, while the computation time is comparable with existing algorithms.

A gradual neural-network approach for frequency assignment in satellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.

A binary Hopfield neural-network approach for satellite broadcast scheduling problems

This paper presents a binary Hopfield neural network approach for finding a broadcasting schedule in a low-altitude satellite system. Our neural network is composed of simple binary neurons on the synchronous parallel computation, which is greatly suitable for implementation on a digital machine. With the help of heuristic methods, the neural network of a maximum of 200000 neurons can always find near-optimum solutions on a conventional workstation in our simulations.

Comparisons of seven neural network models on traffic control problems in multistage interconnection networks

The performances of seven neural network models for traffic control problems in multistage interconnection networks are compared. The decay term, three neuron models, and two heuristics were evaluated. The goal of the traffic control problems is to find conflict-free switching configurations with the maximum throughput. The simulation results show that the hysteresis McCullock-Pitts neuron model without the decay term and with two heuristics has the best performance. >

A MAXIMUM NEURAL NETWORK APPROACH FOR N-QUEENS PROBLEMS

Abstract. A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N×N chessboard such that no pair of queens commands each other. The maximum neuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N2-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

A neural network approach to topological via-minimization problems

Topological via-minimization (TVM) algorithms in two-layer channels based on the artificial neural network model are presented. TVM problems require not only assigning wires or nets between terminals to one of two layers without an intersection, but also minimizing the number of vias, which are the single contacts between the nets in the two layers. The goal of the algorithm is to embed the maximum number of nets without an intersection. Two types of TVM problems are examined: split rectangular TVM (RTVM) problems and split circular TVM (CTVM) problems. The algorithms require 3n processing elements for the n-net split RTVM problems, and 5n processing elements for the n-net split CTVM problems. The algorithms were verified by solving seven problems with 20 to 80 nets. The algorithms can be easily extended to problems with more than two layers. >

A gradual neural network approach for FPGA segmented channel routing problems

A novel neural network approach called gradual neural network (GNN) is presented for segmented channel routing in field programmable gate arrays (FPGAs). FPGAs contain predefined segmented channels for net routing, where adjacent segments in a track can be interconnected through programmable switches for longer segments. The goal of the FPGA segmented channel routing problem, known to be NP-complete, is to find a conflict-free net routing with the minimum routing cost. The GNN for the N-net-M-track problem consists of a neural network of NxM binary neurons and a gradual expansion scheme. The neural network satisfies the constraints of the problem, while the gradual expansion scheme seeks the cost minimization by gradually increasing activated neurons. The energy function and the motion equation are newly defined with heuristic methods. The performance is verified through solving 30 instances, where GNN finds better solutions than existing algorithms within a constant number of iteration steps.

A parallel algorithm for time-slot assignment problems in TDM hierarchical switching systems

The paper presents a parallel algorithm for time-slot assignment problems in TDM hierarchical switching systems, based on the neural network model. The TDM systems are operated in repetitive frames composed of several time-slots. A time-slot represents a switching configuration where one packet is transmitted through an I/O line. The goal of the algorithm is to find conflict-free time-slot assignments for given switching demands. The algorithm runs on a maximum of n/sup 2//spl times/m processors for m-time-slot problems in n/spl times/n TDM systems. In small problems up to a 24/spl times/24 TDM system, the algorithm can find the optimum solution in a nearly constant time, when it is performed on n/sup 2//spl times/m processors. >