Adjacent Channel Constraints Research Articles

Fuzzy call admission control combined with distributed dynamic channel assignment and reassignment for cellular mobile systems

In a cellular mobile system (CMS), the service area is divided into cells, each of which has numerous channels, which are shared by two types of call - new calls and handoff calls. Giving a higher priority to handoff calls than new calls is common practice. However, giving too much priority to handoff calls will result to excessive blocking of new calls. This work firstly proposes a distributed dynamic channel assignment and reassignment (DDCAR) scheme to satisfy three types of constraint - co-channel constraint (CCC), adjacent channel constraint (ACC), and co-site constraint (CSC), simultaneously. The purpose is to minimize the number of available channels that become unavailable for the assignment to new calls and to maximize the number of unavailable channels that become available for release when calls complete. To provide a higher priority to handoff calls, a fuzzy call admission control (FCAC) scheme, combined with the DDCAR, is proposed herein for implementation at the base station. A 7 × 7 CMS with two traffic patterns is employed as the test example. The test results reveal that the FCAC scheme significantly reduces the dropping probability of handoff calls at the cost of increasing the blocking probability of new calls to an acceptable level.

Constrained Channel Assignment in Multi-channel Wireless Mesh Network

A wireless mesh network is a multi-hop network consisting nodes called mesh routers and mesh clients. In the network, communication between a pair of nodes happens when both nodes share a same channel. Channel assignment is an application in graph theory on the vertex coloring. The channels are allocated in such a way to minimize the bandwidth with the constraints of avoiding the electromagnetic interference. In this paper, we focus on the channel allocation for multi-channel which considers adjacent-channel constraint, cochannel and cosite constraint. The minimum number of the channels that are used in the network with minimum completion time shows the effectiveness of the work.

New resource management strategy for wireless cellular networks

In this paper, the fuzzy Hopfield neural network (FHNN) is proposed for channel assignment in wireless cellular system. Each channel is regarded as a data sample and every cell is taken as a cluster. Channels are adequately distributed to the dedicated cells while satisfying the interference constraints such as co-site constraints, adjacent channel constraints, and co-channel constraints. The goal is to avoid the interference and serve the expected traffic, which is to minimize used spectrum. Moreover, interference prediction is a delicate task; it depends on the details of the traffic assumptions. The FHNN guarantees that the neural network will skip local minima, and in all cases will converge to the optimum arrangement of the channels. Simulation results show that the FHNN can provide an alternative approach of solving this class of channel assignment problems.

Design of an efficient channel block retuning

Cellular networks must be updated very often. Due to technical and economical reasons, the complete channel retuning of an urban network has to be done in several steps. The objective is then to define the steps in such a way that the increase in the interference level is minimum, and do not let it go further than a given threshold of minimum quality for the cellular network. We propose a greedy heuristic and an ascent---descent method including a Tabu Search module to retune a network in a given number of steps. All constraints of the channel assignment problem are taken into account (co-channel, adjacent channel, minimum channel spacing on antennas) as well as retuning constraints such as the number of steps and limits on the maximum number of cells which can be retuned at each step. Co-channel and adjacent channel constraints are expressed through compatibility matrices produced with a discretization of the signal-to-interference ratio. Numerical results are presented on a Bell Mobilite urban network of 359 cells.

Cellular radio channel assignment using a modified Hopfield network

The channel-assignment problem is important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal channel-assignment problem has become increasingly important. A new channel-assignment algorithm using a modified Hopfield (1985, 1986) neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. Also, a new technique to escape the local minima is introduced. 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. To escape the local minima, if the number of assigned channels are less than the required channel numbers (RCNs), one or more channels are assigned in addition to already assigned channels such that the total number of assigned channels is the same as the required number of channels in the cell even though the energy is increased. Various initialization techniques, which use the specific characteristics of frequency-assignment problems in cellular radio networks, such as cosite constraint (CSC), adjacent channel constraint (ACC), and cochannel constraint (CCC), and updating methods are investigated. In the previously proposed neural-network approach, some frequencies are fixed to accelerate the convergence time. In our algorithms, no frequency is fixed before the frequency-assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and without fixing frequencies in any cells, has better performance results than the results reported previously utilizing fixed frequencies in certain cells.

Channel assignment in cellular radio using genetic algorithms

The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Recently Genetic Algorithms (GAs) have been proposed as new computational tools for solving optimization problems. GAs are more attractive than other optimization techniques, such as neural networks or simulated annealing, since GAs are generally good at finding an acceptably good global optimal solution to a problem very quickly. In this paper, a new channel assignment algorithm using GAs is proposed. The channel assignment problem is formulated as an energy minimization problem that is implemented by GAs. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested. In this algorithm, the cell frequency is not fixed before the assignment procedures as in the previously reported channel assignment algorithm using neural networks. The average generation numbers and the convergence rates of GAs are shown as a simulation result. When the number of cells in one cluster are increased, the generation numbers are increased and the convergence rates are decreased. On the other hand, with the increased minimal frequency interval, the generation numbers are decreased and the convergence rates are increased. The comparison of the various crossover and mutation techniques in a simulation shows that the combination of two points crossover and selective mutation technique provides better results. All three constraints are also considered for the channel assignments: the co-channel constraint, the adjacent channel constraint and the co-site channel constraint. The goal of this paper is the assignment of the channel frequencies which satisfied these constraints with the lower bound number of channels.