IMPROVEMENT OF PROPOGATION MODEL USING CONSTRAINT SATISFACTION ALGORITHM PROBLEM
The subject of mobile communication brings with it a whole host of complex design issues. The mobile radio channel places the fundamental limitations on the performance of wireless communication systems. The wireless radio channels are extremely random and do not offer easy analysis.
Thus, modeling the radio channel has been historically one of the most difficult parts of the mobile radio system design. Radio wave propagation models are extremely important in radio network planning. This is because of the fact that an accurate propagation model enables efficient prediction of the coverage area of a cell and efficient resource reallocation in the form of better coverage prediction at the desired area. A certain model is usually applicable to limited distances and other parameter values.
A propagation model is set of mathematical expressions and algorithms used to predict radio channel characteristics for a given environment.
Algorithms are widely used to formulate and solve conceptual and design problems. An algorithm is an effective method with a definite list of well-defined instructions for completing a task, which when given an initial state, will proceed through a well-defined series of successive states and eventually terminating in an end-state. Thus, an algorithm is nothing but simply, "a well-defined computational procedure that transforms input into output."
Algorithms can be expressed in many kinds of notation, including natural languages, pseudocode, flowcharts, and programming languages. Natural language expressions of algorithms tend to be verbose and ambiguous, and are rarely used for complex or technical algorithms.
Pseudocode and flowcharts are structured ways to express algorithms that avoid many of the ambiguities common in natural language statements, while remaining independent of a particular implementation language. Programming languages are primarily intended for expressing algorithms in a form that can be executed by a computer.
One such algorithm widely used in mobile communication is "Constraint Satisfaction Problem" algorithm. The algorithm demands the given problem to be first converted into a "constraint satisfaction problem" and then solution is sought out by using algorithm popularly known as "constraint satisfaction problem algorithm".
A Constraint Satisfaction Problem (CSP) is defined in terms of three parameters:
- na set of variables,
- nfor each variable, a finite set of values i.e. domain,
- nand a set of constraints.
In general, every propagation model also involves a number of variables such as frequency, base station antenna height, mobile station antenna height, distance between the base and mobile antenna, etc. Each of these variables have specified domain i.e. range of values. All the models predict mean path loss as a function of different variables such as frequency, distance between transmitting and receiving antenna, antenna heights, etc.
Thus, propagation models can also be viewed as a Constraint Satisfaction Problem. Each variable has a well-defined domain for a particular mobile environment under consideration and the constraint can be defined on the term representing the path loss.
Motivation: Constraint Satisfaction Problem (CSP) has been used extensively in many design issues problems such as:-
- frequency assignment problem,
- area coverage problems,
- optimum base station location.
Field of work:
The utility of CSP in the propagation modeling was yet to be explored.
To comprehensively study various propagation models used in different environments.
To represent the propagation models such as Hata-Okumura, Walfisch-Ikegami model in terms of Constraint Satisfaction Problems and solve them using simple heuristic algorithm such as Backtracking.
To compute the path loss and analyze the results for path loss dependency on frequency, distance, antenna heights for Okumura-Hata and Walfisch-Ikegami model.
To check the performance of Okumura-Hata, Walfisch-Ikegami model at higher range of frequencies.