Prioritized channel assignment in a cellular radio network

Dimensioning procedures for prioritized channel assignment in a cellular radio network are considered. Under the cutoff priority discipline, the prioritized channel assignment procedures for a single cell and multicell system are formulated as nonlinear discrete capacity allocation problems. Exact incremental algorithms which efficiently solve the proposed problems are devised. They are based on the properties of the blocking probabilities of new calls and handoff calls. Given the number of available frequency channels together with the arrival rates and the grade of service (GOS) for both types of calls in each cell, algorithm SP1 generates an optimal channel assignment which ensures priority for handoff calls. Given the arrival rates and distinct GOSs for new and handoff calls, algorithm SP2 finds the minimum number of channels required in each cell. Algorithm MP extends algorithm SP1 to a multicell system and provides the prioritized channel assignment for all calls in the system. The algorithms are very fast and are appropriate for the fair allocation of frequency channels among cells     

Image restoration using a modified Hopfield network

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.     

A compact dynamic channel assignment scheme based on Hopfield networks for cellular radio systems

In this paper, a new channel assignment strategy named compact dynamic channel assignment (CDCA) is proposed. The CDCA differs from other strategies by consistently keeping the system in the utmost optimal state, and thus the scheme allows to determine a call succeeding or failing by local information instead of that of the whole network. It employs Hopfield neural networks for optimization which avoids the complicated assessment of channel compactness and guarantees optimum solutions for every assignment. A scheme based on Hopfield neural network is considered before; however, unlike others, in this algorithm an energy function is derived in such a way that for a neuron, the more a channel is currently being allocated in other cells, the more excitation the neuron will acquire, so as to guarantee each cluster using channels as few as possible. Performance measures in terms of the blocking probability, convergence rate and convergence time are obtained to assess the viability of the proposed scheme. Results presented show that the approach significantly reduces stringent requirements of searching space and convergence time. The algorithm is simple and straightforward, hence the efficient algorithm makes the real‐time implementation of channel assignment based on neural network feasibility. Copyright © 2008 John Wiley & Sons, Ltd.     

A neural network parallel algorithm for channel assignment problemsin 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     

Superresolution algorithms for a modified Hopfield neural network

The authors describe the implementation of a superresolution (or spectral extrapolation) procedure on a neural network, based on the Hopfield (1982) model. They show the computational advantages and disadvantages of such an approach for different coding schemes and for networks consisting of very simple two-state elements as well as those made up of more complex nodes capable of representing a continuum. It is demonstrated that, with the appropriate hardware, there is a computational advantage in using the Hopfield architecture over some alternative methods for computing the same solution. The relationship between a particular mode of operation of the neural network and the regularized Gerchberg (1974) and Papoulis (1975) algorithm is also discussed     

Fixed channel assignment in cellular radio networks using amodified genetic algorithm

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem     

Distributed channel assignment strategies using coexistenceetiquettes for land based radio environment

The performance of two distributed channel assignment algorithms which use coexistence etiquettes for point to point links are considered by the authors. It has been found that selecting channels prior to transmission at the transmitter as opposed to the receiver, supports greater offered traffic with the additional advantage of a less complex protocol     

Capacity allocation and channel assignment in cellular radio systems using reuse partitioning

Reuse partitioning is a technique for providing more efficient spectrum reuse in cellular radio systems. A cell in such a system is divided into concentric zones, each associated with an overlaid cell plan. For these schemes an optimum channel assignment strategy is proposed. Numeric examples show capacity improvements of about 50% compared to conventional systems.<>     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

Cellular communication schemes using generalized fixed channel assignment and collision type request channels

Schemes for accessing the message channels of base stations in a cellular communication system are considered. Detailed analysis is given for a fixed channel assignment scheme with collision type request channels. The probability Pfthat a request fails to secure a message channel is considered as a performance criterion. That fraction of bandwidth allocated to request channels, which minimizes the failure probability Pf, is determined as well as the optimum Pf. Both are plotted as a function of bandwidth utilization S. Sensitivity of system performance to various parameters is considered. A new fixed channel assignment (FCA) scheme, in which a user can be served by any of the nearest k base stations, has been considered and compared with the standard (k = 1) FCA scheme. A mixed scheme with two message types has also been considered. One message type can be served by any of the nearest three base stations but the other type can be served only by the nearest.     

Optimal radio channel assignment through a new binary dynamic simulated annealing algorithm

The radio channel assignment problem (CAP) is classified as an NP-complete binary optimization problem, which creates the need for faster, yet optimal optimization algorithms to reduce the time of computation when solving such a complex problem. Simulated annealing (SA), a powerful optimal combinatorial search algorithm, was found to be very suitable for CAP. This paper extends the standard capabilities of SA and proposes a new CAP-oriented, quicker binary SA, the binary dynamic SA (BDSA) algorithm, as part of a newly proposed radio channel assignment approach. Simulation results proved that the proposed BDSA has very fast convergence as a stand-alone algorithm and even faster convergence with the newly proposed radio channel assignment approach. © 1998 John Wiley & Sons, Ltd.     

Power control supported dynamic channel assignment in cellular radio systems

Transmitter power control is an effective technique to reduce co-channel interference and increase system capacity for cellular radio networks. Dynamic channel assignment can also be used to improve spectrum efficiency and thus increases the system capacity. This paper investigates channel assignment algorithms which combine dynamic channel assignment with signal-to-interference ratio balanced power control and proposes a new algorithm. Simulation results show that this new scheme can greatly reduce the channel reassignment rate and still maintain satisfactory performance. It may thus be used as an effective channel assignment scheme in cellular mobile systems. © 1997 by John Wiley & Sons, Ltd.     

Adaptive code symbol assignment in a rateless coded multichannel cognitive radio network

Because of its robustness to packet loss and adaptivity to channel conditions, rateless codes have been used in cognitive radio networks to improve the secondary system performance. In this paper, we investigate an adaptive code symbol assignment scheme for the secondary user (SU) in a multichannel cognitive radio network based on rateless coding. In particular, the SU transmitter first encodes its information data through rateless coding and then assigns the unceasingly generated code symbols adaptively to each available channel obtained by spectrum sensing. Thanks to the forward incremental redundancy provided by rateless codes, it is unnecessary for the SU receiver to request the retransmission of lost symbols and the code symbols collected from any channel at any time contribute to the final data recovery. With an alternating channel occupancy model of the primary user (PU), we conduct a weight enumerator analysis to derive the optimal number of code symbols to be assigned to each available channel, thus to maximize the system throughput while protecting PU from interference. Both theoretical analysis and numerical results demonstrate the good performance of our proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

一种针对超短波电台接入网的混合信道接入方案

为超短波电台接入网提供了一种混合信道接入方案,方案借鉴了民用接入技术的一些先进理念,并结合军用跳频电台网络的特殊业务应用需求,采用了一种静态TDMA、动态TDMA和频分多址FDMA的混合信道接入策略,实现了多用户多业务传输的QOS保障,满足了军用业务的特殊传输需求。     

无线 Mesh 网络部分重叠信道分配综述

无线Mesh网络面临的主要挑战是由共信道干扰导致的网络容量下降问题,仔细规划部分重叠信道的使用能减少甚至消除共信道干扰。在介绍部分重叠信道基本概念的基础上,重点从单播和多播2个角度对国内外部分重叠信道分配研究领域的成果进行了分类总结与分析;深入剖析部分重叠信道分配中的难点与挑战,并展望了未来的研究方向,以期促进无线Mesh网络向“最后一公里”接入的理想解决方案演进。     

Object recognition using multilayer Hopfield neural network

An object recognition approach based on concurrent coarse-and-fine matching using a multilayer Hopfield neural network is presented. The proposed network consists of several cascaded single-layer Hopfield networks, each encoding object features at a distinct resolution, with bidirectional interconnections linking adjacent layers. The interconnection weights between nodes associating adjacent layers are structured to favor node pairs for which model translation and rotation, when viewed at the two corresponding resolutions, are consistent. This interlayer feedback feature of the algorithm reinforces the usual intralayer matching process in the conventional single-layer Hopfield network in order to compute the most consistent model-object match across several resolution levels. The performance of the algorithm is demonstrated for test images containing single objects, and multiple occluded objects. These results are compared with recognition results obtained using a single-layer Hopfield network.     

Block truncation coding using Hopfield neural network

The authors extend the analysis of the block truncation coding (BTC) algorithm using a Hopfield neural network (HNN). They show that its performance is suboptimum (in the mean square error sense) and that alternative (non-neural network) BTC algorithms are available with virtually the same performance.<>     

Improvement on performance of modified Hopfield neural network forimage restoration

By analyzing the same inequality ||u*||(1)=/<(1/2)trace(T), the authors conclude that a severely blurred image is generally restored less accurately than a mildly blurred one by the modified Hopfield neural network. This conclusion is the opposite of the statement made in Paik and Katsaggelos (1992). The authors also propose an improved new algorithm. Simulation results show that the SNRs of the images restored by the algorithm are higher by 3 to 8 db than those restored by the algorithm in Paik and Katsaggelos and the streaks in the restored images are obviously suppressed by the algorithm.     

Channel assignment for cellular radio using simulated annealing

The channel assignment problem, i.e. the task of assigning the channels to the radio base stations in a spectrum-efficient way, is an NP-complete optimization problem occurring during design of cellular radio systems. Previously, this problem has been solved by graph coloring algorithms. An alternative approach is presented. The problem is solved using simulated annealing, which is a general approach to combinatorial optimization. The algorithm has been successfully applied to practical radio network planning situations. One major benefit of the approach consists in the enhanced flexibility it gives to the engineer