Wireless design has a high complexity because of the random characteristics and the shared nature of wireless medium. A cell planning example based on WiMAX standard, even though it is not based on automatic cell-based planning. A complete synthesis of automatic cell planning process is presented. The model uses a multiobjective function, built by a weighted sum of functions, each one representing signal coverage, capacity, system growth capabilities, and cost. The decision variables are channel assignment, sites location, and transmission power. Because of the nonlinear characteristics of this model, author uses genetic algorithms to solve it.
There is a general description of the optimization problem related to wireless network design. It presents a simplified model for global system for mobile communications (GSM) cell planning based on the activation or deactivation of a set of candidate base stations. There are other models based on multiobjective functions. Authors solve the problem by iteratively changing the transmission power used by base stations to guarantee signal reception and interference reduction. They use heuristic techniques and artificial intelligence algorithms in the solution process. Automatic cell planning based on artificial intelligence algorithms. Makes a comparison among different techniques, showing a better performance of tabu search over the other techniques. The variation of the height of antennas and the transmission power by using genetic algorithms, but they do not consider capacity criteria. Particle swarm optimization is used with an optimization criteria similar to that.
Two heuristic techniques to solve high complexity nonlinear optimization problems are tabu search and simulated annealing . Use tabu search to solve an integer linear programming problem. A design process which is similar to ours. It uses simulated annealing to choose active base stations from a set of candidate base stations. A similar problem is solved using simulated annealing too.
Previous references are oriented to cellular networks to provide voice services. WiMAX networks support different adaptive modulation and coding (AMC) schemas according to link quality. It also defines different types of connections ranging from a circuit-like access to a completely random access. In WiFi networks, there are different link conditions as in WiMAX, but there are not different types of flows. Most of the references for the design of WiFi networks, use the position of access points, their transmission power, and the channel assignment as the decision variables.There is a simple but illustrative description of the problems involved in wireless LAN design. A genetic algorithm to solve channel assignment in Wireless LAN Networks.The algorithm modifies transmission power of fixed access points to react to changes in user traffic requirements. The model described uses a heuristic search model to provide coverage and a minimum data rate at test points. Solve the joint problem of access points location and channel assignment.There is a good description of Wireless LAN Network planning. They use a penalty function to avoid placing access points near each other, to increase the probability of a posterior feasible channel assignment solution. The objective function is a weighted sum of a coverage variable, an interference mitigation variable, and a QoS variable. Authors use tabu search to solve the problem.
Consider the problem of locating relay nodes to improve access point covered area. The decision variables are user nodes and relay nodes location. PMP and multihop topologies can be mixed, where an IEEE 802.16 wireless mesh network interconnects a cell-based IEEE 802.11 access network. In more dynamic scenarios, the design process must prepare a feasible scenario for operation. Discuss several issues for channel allocation and transmission scheduling and a connection admission control and a transmission power control for WiMAX networks is presented.
