Channel assignment in cellular radio

Sivarajan, Kumar N.; McEliece, Robert J.; Ketchum, J.

doi:10.1109/vetec.1989.40173

Cited by 184 publications

(164 citation statements)

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“…Many researchers (Sivarajan, McEliece, and Ketchum, 1989;Gamast and Rave, 1982;Kunz, 1991;Funabiki and Takefuji, 1992;Chan, Palaniswami and Everitt, 1994;Kim et al, 1997;Funabiki, Okutani and Nishikawa, 2000; . ,…”

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confidence: 99%

“…Sivarajan, McEliece and Ketchum (1989) proposed an 0(n2) time sequential heuristic algorithm, based on the first algorithm introduced by Gamst and Rave (1982). Sivarajan, McEliece and Ketchum (1989) applied their algorithm to several problems, where the values of total frequencies in solutions are shown without any actual assignment results (pp.846-850). Kunz (1991)  x)).…”

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A Hopfield Neural Network for Channel Assignment Problem in Cellular Radio Networks

Moradi¹

2011

CIS

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In wireless mobile communication system, radio spectrum is limited resource. However, efficient use of available channels has been shown to improve the system capacity. The role of a channel assignment scheme is to allocate channels to calls or mobiles in such a way as to minimize call blocking or call dropping probabilities, and also to maximize the quality of service. Channel assignment is known to be an NP-hard optimization problem. In this paper, a new channel-assignment algorithm using a modified Hopfield neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. In this algorithm, an energy function is derived, and the appropriate interconnection weights between the neurons are specified. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support if the constraint conditions are violated and receives excitatory support if the constraint conditions are satisfied. The algorithm will be tested by solving seven benchmark problems, where the total number of frequencies varied from 73 to 533. This new algorithm, together with the proposed regular interval initialization and new interconnection weights, has better performance results than the existing algorithms in all of the seven problems.

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A Hopfield Neural Network for Channel Assignment Problem in Cellular Radio Networks

Moradi¹

2011

CIS

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“…Previous approaches to solve CAP can be categorised as approaches using graph theory [6], heuristics [1,7,8,9], local search [10], neural networks [11, 12 ,13], the utilisation of simulated annealing [14] and genetic algorithms [15].…”

Section: Related Workmentioning

confidence: 99%

“…Sivarajan et. al [9], produced eight CAP algorithms by ranking the calls in row-wise ordering or column-wise ordering and the cells in node-degree order or node-colouring order. With each rank, they used a requirement exhaustive strategy and a frequency exhaustive strategy to assign the channel.…”

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Channel assignment optimisation using a hyper-heuristic

Kendall

Mohamad

IEEE Conference on Cybernetics and Intelligent Systems, 2004.

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-The channel assignment problem is a real world problem from the mobile communications industry. The primary objective is to find the minimum frequency bandwidth given different traffic demand distribution within the mobile network. Besides fulfilling the channel demand requirement, we must also consider the minimum channel reuse distance in order to avoid the effect of call interference within the same cell or adjacent cells. Previous approaches have used graph-colouring algorithms, heuristic search, neural networks, simulated annealing and genetic algorithms (GA). In this paper, we propose a hyperheuristic methodology and compare our approach against channel assignment problems from the literature.

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Intelligent Flow Control under a Game Theoretic Framework

Chen

2001

Telecommunications Optimization: Heuristic and Adaptive Techniques

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Each of us is interested in optimization, and telecommunications. Via several meetings, conferences, chats, and other opportunities, we have discovered these joint interests and decided to put together this book.It certainly wasn't easy. What made it difficult is partly the reason for this book in the first place: things are moving fast! As researchers in both industry and academia, we are under constant pressure to keep up with new technologies and opportunities. Part of our day jobs involves developing good algorithms for a steady surge of new problems arising from industry. Another part of our day jobs involves finding realistic problems which might yield to some of the algorithms we have developed, which we particularly like, but which don't work on anything else. The remainder of our day jobs tends to involve lecturing (for two of us), project management (for all of us), and things like exam-setting and marking. Finally, for two of us, childcare takes up a substantial part of our night jobs. Naturally, the book was finished when we worked out how to stuff 46 hours into a single day, and keep that up for a few successive weeks.Seriously, though, telecommunicatons is growing and changing very quickly, and all of this involves the increasing availability of new real-world problems for optimization researchers to address. Telecommunications engineers tend to know all about the problems involved, but are often not aware of developments in computer science and artificial intelligence which might go a long way towards solving those problems. We therefore argue, at the beginning of Chapter 1, that there needs to be more collaboration between the communities, so that both sides can be equipped for the challenges ahead. Indeed, this book is the result of one such collaboration.We have oriented it slightly towards the telecommunications engineer who knows little about the computational techniques, rather than the other way around. This is mainly because it is much easier to describe the techniques than it is to describe the telecommunications issues; the latter is a much larger collection. However, individual chapters go into some detail, often at an introductory level, in describing particular kinds of problem. Also, we provide a brief introduction to the essence of the problems at the beginning of each part.Let us know what you think. In particular, if you are a telecommunications engineer, and have been inspired by a chapter or two in this book to try out some techniques that you otherwise wouldn't have, please get in touch: your story may make it into the next edition!

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Channel assignment in cellular radio

Cited by 184 publications

References 5 publications

A Hopfield Neural Network for Channel Assignment Problem in Cellular Radio Networks

A Hopfield Neural Network for Channel Assignment Problem in Cellular Radio Networks

Channel assignment optimisation using a hyper-heuristic

Intelligent Flow Control under a Game Theoretic Framework

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