Optimal channel assignment in wireless communication networks with distance and frequency interferences
Introduction
Wireless communication is one of the fastest growing areas in telecommunications. In recent years, popularity of mobile phones has resulted in the rapid expansion of the mobile communication industry.
With regard to mobile telephone communication service, the increasing demand for mobile telephone communication service and the finite spectrum allocated to this service led to the proposal of the cellular structure. In the cellular structure, the communication area is partitioned into neighboring hexagons. Wireless mobile networks are usually modeled as hexagonal cell systems.
Within each hexagonal cell, a set of frequencies is assigned to be used. These frequencies should differ sufficiently to prevent interference (called the co-channel interference) on transmitted signals. This is that if a certain set of channels (for upstream or downstream traffic) is assigned to the base station of a particular cell, then that set will not be reassigned to any base station which lies in n belts of cells around the cell considered to prevent co-channel interference which is called the co-channel reuse distance. The choice of n depends on the propagation delay and also on how much interference rejection is desired. Another interference is to be observed on frequencies used by neighboring cells. We call this interference the adjacent interference.
Since radio bandwidth is still a limited resource in the mobile communication industry for mobile multimedia communication, providing optimal utilization of it has become a pressing dilemma for the mobile communication research community. There are two different approaches to the channel assignment problems: fixed channel assignments and dynamic channel assignments. For a fixed channel assignment problem, each cell is assigned a fixed subset of frequencies [1] and [2]. When there is a service call within a cell, an available frequency will be assigned to it. If all frequencies for the cell are used by other users, the call is lost. The decision is made locally inside the cell and the response time is very fast. The dynamic channel assignment approach, however, does not give any frequency to any cell a priori [3].
The assignment of frequencies to calls is done in service time by a central controlled algorithm to make sure that interferences of frequencies do not occur. Since frequencies under the dynamic channel assignment approach are assigned by the central control algorithm to avoid interference, the response time is usually longer than the fixed channel assignment method.
In practice, these two methods are often combined to form a hybrid channel assignment system: each cell is assigned with a fixed number of frequencies and another subset of frequencies is reserved for the dynamic assignment [4], [5]. Therefore, both fixed channel assignment problems and dynamic assignment problems are important for optimal resource allocation in mobile communications.
The channel assignment in wireless communication networks is a significant combinatorial optimization problem that must be solved. Since the combinatorial optimization problem is NP-hard, many different heuristics have been proposed for its solution. It is known that frequency assignment is, in general, NP-hard [6]. Therefore, no polynomial time algorithm is believed to exist for the exact solution. Brute force search can be applied to solve cases when the number of cells is small and the number of frequencies to be assigned is small. The search space of channel assignment grows exponentially with the number of channel base cells. It becomes impractical and even impossible to always find the optimal assignment as the number of cells and the number of frequencies increases.
Heuristic methods are often used in such cases for finding good solutions in an acceptable amount of time. Because of the nature of the fixed channel assignment, the decision is made in advance of the deployment of the assignment of frequencies to the cells. Therefore, the response time will not be affected by the algorithm to find the assignment. It makes sense to spend more effort in finding a suitable solution if not an optimal one. In comparison, the decision for the dynamic assignment is made in run-time. A fast algorithm, in addition to the requirement to find a good solution, is important for it to be practical.
Therefore, we can afford to spend much more time finding a good fixed channel assignment than finding a dynamic one. Therefore, algorithms for fixed channel assignment problems may not have to be as fast than those proposed to solve dynamic channel assignment problems with neural network methods [7], [8], [9], [10] or with a genetic algorithm (GA) [11], [12], [13], [14], [15].
As discussed above, the search space for channel assignment grows exponentially with the network size. Finding an optimal solution is a difficult problem. However, a good solution would reduce interference to communication and fixed channel assignments can reduce the response to users requests. Therefore, it is worthwhile to make an effort to find good approximate solution to the optimal solution.
In this paper, we will minimize or disallow two types of interference: the co-channel interference and the adjacent interference, in order to achieve an optimal channel assignment by using our optimal channel assignment method. In Section 2, we present the model and the objective of channel assignment problems for our study. Then, we present our recursive search algorithm together with the neighborhood improvement structure in Section 3. Experimental results are compared with previous results in Section 4. We conclude in Section 5 with remarks and discussion.
Section snippets
Channel assignment environment
We present a fast method for fixed channel assignment. For ease of illustration of our model, metric evaluation and algorithm, throughout the paper we use the benchmark example introduced originally in [12], of 49 hexagon cells for a cellular wireless mobile network.
We discuss two types of interference conditions for channel assignment: (1) Where the radio signals are assigned to the same channel at the same cell [12]. (2) Where two frequencies of wavelengths close to each other are assigned to
An algorithm for channel assignment
It is an ideal that interferences become zero in the whole system as introduced in Section 2. However, in reality, this cannot always be completely satisfied. There are two major types of approaches to minimize the interference in the whole system. One method is to minimize the maximum interference over all the cells. Another is to minimize the total number of interference on all the cells.
In this paper, we take a combination of these two approaches. We differentiate the priority of two types
Simulation results
To test our fixed channel assignment algorithm, we use Fig. 8 as the simulation environment. The numbers inside cells of Fig. 8 represent the required number of channels for that cell. The total number of channels required is 126. We want to use such a simulation environment where the number of channels assigned will be different from the sum of what is required by individual channels. This would allow us to test to the usefulness of our channel assignment method.
Conclusion
Fixed channel assignment in wireless communication networks is a significant combinatorial optimization problem that must be solved. In this paper, we proposed an algorithm that combined seven-cell assignment and recursive search method for high-speed fixed channel assignment. We considered two types of signal interferences: the co-channel interference within a distance of two cells and the adjacent channel interference within the same and the adjacent cells. We applied recursive search for the
Acknowledgements
This work was supported in part by GRANT-IN-AID FOR SCIENTIFIC RESEARCH (No. 50234175), Japan and (No. 13650440) and a research grant (CityU 1056/01E) of Hong Kong RGC, China.
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