In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well known for their remarkable generality and versatility and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad-ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

Genetic algorithms in wireless networking: techniques, applications, and issues

Efficient allocation of channels for wireless communication in different network scenarios has become an extremely important topic of recent research. The main challenge lies in the fact that the channel allocation problem is NP-complete. Because of a maximum allowable time limit imposed in practical situations for allocation of channels, sometimes we may need to be satisfied with a near-optimal solution. In this correspondence, we present a discussion on the various challenges and approaches that have been used by different researchers to solve the problem of channel allocation taking into account different interference issues and efficient utilization of available communication channels for cellular mobile (including multimedia communication) environment and cognitive radio based networks. Copyright © 2010 John Wiley & Sons, Ltd.

A survey on the channel assignment problem in wireless networks

In order to use the frequencies more efficiently, a modified immune genetic algorithm to solve the problem of channel assignment in cellular radio networks is presented. Three constraints were considered for the channel assignment: the co-channel constraint, the adjacent constraint and the co-site constraint. Our objective is to obtain a conflict-free channel assignment scheme among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. The minimum-separation encoding scheme was introduced to meet the co-site constraint. The genetic operators(crossover and mutation) was proposed to ensure the traffic demand throughout the iterative process. In order to increase the efficiency and velocity of convergence, immune operators are given, such as immune clone, vaccination, immune selection etc. To void local optima, the elite strategy was used. Some well-known benchmark problems were simulated. The significant results indicate that the proposed algorithm is a better approach for solving the channel assignment problem.

A Modified Immune Genetic Algorithm for Channel Assignment Problems in Cellular Radio Networks

The wireless technology and its application growing faster and faster in last decades. Mobile network is one of the fastest growing technologies in wireless network. This headed to some challenges that face mobile network such as how to serve the big number of users, efficiently of frequencies is scarce and interferes with each other. One of the solutions to deal with such challenges is Cellular Networks which is used to divide a geographical area in to cells so that we can reuse the scarce frequencies in order to support more users and also to decrease interference. This paper introduces the importance of dynamic channel allocation in cellular networks and how much gain could be utilized by this technique. The Methodology depend on an intensive reading of what other research has been done in the field, then the model factors and the goal was built according to the main importance issues in this field. In order to realize the complications and limitations of the topic and to have comprehensive understanding many work in the literature have been revised. The mechanism was tested in two different scenarios, with uniform and non-uniform load distribution. For the findings: A new mechanism was introduced to overcome the previous limitations and to gain more efficient results. Also it utilizes artificial intelligence approach to make the allocation process optimal. Moreover, the new mechanism depends on four factors cell size, coordination, frequency reuse, and hand over to make the allocation process efficient and reliable.

Dynamic Channel Allocation in Cellular Networks

With limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel allocation becomes increasingly important. However, finding a conflict-free channel allocation with minimum interference and minimum channel span is NP hard. Genetic algorithm (GA) is one of the heuristic optimization tools that can be used to solve this problem efficiently. In this paper we investigate fixed and dynamic channel allocation problem for cellular networks and its optimum solution using GA.

Solving channel allocation problem in cellular radio networks using genetic algorithm

Systems and methods are disclosed that provide a closed loop power control system including adaptively adjusting the desired target SINR over time so as to ultimately achieve a feasible SINR. In one implementation, a method is provided of optimizing uplink closed loop power control in a RAN in which one or more base stations each service a plurality of mobile stations, including: determining a power level for each mobile station for its respective uplink transmissions, including measuring a current achieved SINR for each mobile station; and for each mobile station, adjusting the power level to be sufficiently high to meet desired transmission characteristics but not so high as to cause unnecessary interference with transmissions from other mobile stations, by adjusting a desired target SINR based on factors selected from the following: current and prior achieved SINRs, current and prior interference measurements, and current and prior transmission power control commands.

System and method for closed loop uplink power control

Devices and methods capable of addressing filter responses are disclosed. For example, a method for compensating a first low-pass filter and a second low-pass filter is disclosed. The method includes injecting a reference tone f R and a cutoff tone f C into the first low-pass filter, and measuring respective filter responses of the reference tone f R and the cutoff tone f C while changing capacitor codes that control a cutoff frequency of the first low-pass filter until a first capacitor code I CODE is determined that most accurately causes the first low-pass filter to utilize a desired cutoff frequency f 0 , performing a similar operation for the second low-pass filter until a second capacitor code Q CODE is determined, and calibrating for mismatch between the first low-pass filter and the second low-pass filter.

Srinivas Pinagapany

Papers

Solving channel allocation problem in cellular radio networks using genetic algorithm

System and method for closed loop uplink power control

Methods and systems for calibrating an analog filter