Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm


Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

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A. Elarfaoui and N. Elalami, "Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm," International Journal of Communications, Network and System Sciences, Vol. 6 No. 11, 2013, pp. 478-483. doi: 10.4236/ijcns.2013.611050.

1. Introduction

In the past years, end user became service oriented and the increased demand of wireless applications, has increased the demand of bandwidth which resulted in spectrum scarcity. The efficient use of licensed spectrum becomes a subject of recent contributions [1]. The Federal Communication (FCC) published a report in November 2002, aiming at establishing new spectrum strategies to resolve the overcrowding bands [2] and allowing SU to use licensed bands accordingly. Proposed strategies enable the unlicensed users to use the licensed frequencies simultaneously with the licensed users as long as they conform to environment constraints. Cognitive Radio is one of the leading technologies to answer the spectrum overcrowding problem. CR concepts are based on intelligent modules to measure and sense unused spectrum as well as adapt radio parameters in manner to avoid or limit interference with other users [3]. Artificial intelligence is mostly the best way used to enhance radio learning capabilities from shared environment. Based on collected information, it allows possibilities to exploit empty frequencies in the licensed band of spectrum and can be assigned to SU without causing any interference to other uses.

Results from [4] introduction derived relationship between the transmission and environmental parameters. Moreover, many other researches are based on GA adaptation to increase the performance and the quality of final solution. In [4] population adaptation, the adaptation was proposed. The paper [5] discussed the adaptation of mutation/crossover probabilities based on the evolution of generations.

In this paper, section 2 introduces Cognitive Radio concept, transmission/environmental parameters. Section 3 describes performance objective used in this paper and assumption considered in the model. Section 4 covers the background of GA and the presentation of the proposed crossover method. Section 5 presents simulation and results and Section 6 concludes the paper with introduction of possible future work.

2. Cognitive Radio

2.1. Introduction

Cognitive Radio is firstly used by Mitola and Magurire [6], It’s a smart radio that have ability to change and reconfigure internal parameters according to the radio environment. Secondary User in Cognitive Radio can borrow the unused spectrum for a time interval from the Primary User without causing interference to the Primary Users.

As described in [6], a Cognitive Radio device must have four qualities to achieve optimal spectrum utilization:

• Sensing spectrum.

• Understanding QoS requirements.

• Understanding regulatory policies enforced by the regulator.

• Understanding of radio cognitive internal capabilities.

Cognitive Radio essentially takes advantage of Software Defined Radio (SDR) with artificial intelligence, capable of sensing and reacting to the environment changes. A radio may be able to sense the current spectral environment, and have some memory of past transmitted and received data along with power, bandwidth, modulation, SINR, etc.

From all above, RC takes appropriate decisions about how to optimize the fixed objective.

Possible goals could include:

1) Primary and Secondary Users point of view:

• Maximize SINR, data rate, throughput.

• Mitigate interferences.

• Minimize power consumption.

• Minimize BER.

• Maximize Battery life.

• Maximize medium access.

2) Network point of view:

• Ensure efficient spectrum utilization.

• Maximize throughput aggregation.

• Ensure appropriate coverage.

• Optimal network capacity.

The main functionality of CR is its capability to perform dynamic optimization of communication parameters and enhance the capability to adapt with brisk environment changes. Many implementations of Genetic algorithms based cognitive radio are tested, but the performance and results of these algorithms depend on the fitness functions model, the number of performance objectives and it can also depend on GA operators. GA is used to optimize multi-objective problems and produce the Pareto Front (the non-dominated solutions in the solution space) which needs decision-making capability to decide accordingly optimal configuration that respect QoS requirements.

2.2. Transmission and Environmental Parameters

Transmission and environmental parameters represent inputs of CR system. Based on those parameters, the quality and accuracy of solution are evaluated. More parameters make the radio system more informed, thus increase the difficulty of the implementation but allowing generating optimal decision.

Two types of parameters are used:

• Environmental parameters include all information about wireless shared environment. Sensor module has to be implemented to collect environmental-sensed data.

• Transmission parameters include all important inputs that are controlled by the system and used for decision. The final decision is the optimal set of transmission parameters that achieves the set of performance objectives.

Table 1 illustrates the transmission and Environmental parameters used in this paper

Conflicts of Interest

The authors declare no conflicts of interest.


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