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Archive for February, 2011

Getting your web design company’s name out in the public, as well as networking with other businesses is a great way to draw in not only potential clients but also new ideas. Every web design company should take the time out to create online profiles on various networking web sites.

A company should not limit their online presence to advertising banners and blogging only isn’t guaranteed to get you too far. Powerful networking tools are available, giving the opportunity to connect with a large mass of people and reach specific target markets. It would be wise of a business to use these tools to build their internet existence.

Below is a compiled list of networking sites designed specifically with web design companies in mind. Review the list to see which ones will work best for you and your company.

Design Sites Up
9Rules
Ads of the World
Behance
Colour Lovers
Designer ID
Lo8os
Flickr
GFX Artist
Kuler
Open Web Design
Pixel Groovy
Typophile

Take advantage of networking because for the most part it is free. Branding can carry a company very far if done well. It is also important for companies to explore forums and write/post blogs. Here, you will be able to link up with other professionals, supporters, and consumers who are most likely to gain an interest in what your web design company has to offer.

About CODANK Charlotte Web Design

CODANK is a top Charlotte Web Design and Internet Marketing Company located in Charlotte, NC. The company is dedicated to providing a broad range of web design services. CODANK specializes in Search Engine Optimization (SEO), Graphic Design, Online Marketing, and Web Design and Development.

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A neural network also known as an artificial neural network provides a unique computing architecture whose potential has only begun to be tapped. They are used to address problems that are intractable or cumbersome with traditional methods. These new computing architectures are radically different from the computers that are widely used today. ANN’s are massively parallel systems that rely on dense arrangements of interconnections and surprisingly simple processors (Cr95, Ga93).

Artificial neural networks take their name from the networks of nerve cells in the brain. Although a great deal of biological detail is eliminated in these computing models, the ANN’s retain enough of the structure observed in the brain to provide insight into how biological neural processing may work (He90).

Neural networks provide an effective approach for a broad spectrum of applications. Neural networks excel at problems involving patterns, which include pattern mapping, pattern completion, and pattern classification (He95). Neural networks may be applied to translate images into keywords or even translate financial data into financial predictions (Wo96).

Neural networks utilize a parallel processing structure that has large numbers of processors and many interconnections between them. These processors are much simpler than typical central processing units (He90). In a neural network, each processor is linked to many of its neighbors so that there are many more interconnections than processors. The power of the neural network lies in the tremendous number of interconnections (Za93).

ANN’s are generating much interest among engineers and scientists. Artificial neural network models contribute to our understanding of biological models. They also provide a novel type of parallel processing that has powerful capabilities and potential for creative hardware implementations, meets the demand for fast computing hardware, and provides the potential for solving application problems (Wo96).

Neural networks excite our imagination and relentless desire to understand the self, and in addition, equip us with an assemblage of unique technological tools. But what has triggered the most interest in neural networks is that models similar to biological nervous systems can actually be made to do useful computations, and furthermore, the capabilities of the resulting systems provide an effective approach to previously unsolved problems (Da90).

Neural network architectures are strikingly different from traditional single-processor computers. Traditional Von Neumann machines have a single CPU that performs all of its computations in sequence (He90). A typical CPU is capable of a hundred or more basic commands, including additions, subtractions, loads, and shifts. The commands are executed one at a time, at successive steps of a time clock. In contrast, a neural network processing unit may do only one, or, at most, a few calculations. A summation function is performed on its inputs and incremental changes are made to parameters associated with interconnections. This simple structure nevertheless provides a neural network with the capabilities to classify and recognize patterns, to perform pattern mapping, and to be useful as a computing tool (Vo94).

The processing power of a neural network is measured mainly be the number of interconnection updates per second. In contrast, Von Neumann machines are benchmarked by the number of instructions that are performed per second, in sequence, by a single processor (He90). Neural networks, during their learning phase, adjust parameters associated with the interconnections between neurons. Thus, the rate of learning is dependent on the rate of interconnection updates (Kh90).

Neural network architectures depart from typical parallel processing architectures in some basic respects. First, the processors in a neural network are massively interconnected. As a result, there are more interconnections than there are processing units (Vo94). In fact, the number of interconnections usually far exceeds the number of processing units. State-of-the-art parallel processing architectures typically have a smaller ratio of interconnections to processing units (Za93). In addition, parallel processing architectures tend to incorporate processing units that are comparable in complexity to those of Von Neumann machines (He90). Neural network architectures depart from this organization scheme by containing simpler processing units, which are designed for summation of many inputs and adjustment of interconnection parameters.

The two primary attractions that come from the computational viewpoint of neural networks are learning and knowledge representation. A lot of researchers feel that machine learning techniques will give the best hope for eventually being able to perform difficult artificial intelligence tasks (Ga93).

Most neural networks learn from examples, just like children learn to recognize dogs from examples of dogs (Wo96). Typically, a neural network is presented with a training set consisting of a group of examples from which the network can learn. These examples, known as training patterns, are represented as vectors, and can be taken from such sources as images, speech signals, sensor data, and diagnosis information (Cr95, Ga93).

The most common training scenarios utilize supervised learning, during which the network is presented with an input pattern together with the target output for that pattern. The target output usually constitutes the correct answer, or correct classification for the input pattern. In response to these paired examples, the neural network adjusts the values of its internal weights (Cr95). If training is successful, the internal parameters are then adjusted to the point where the network can produce the correct answers in response to each input pattern (Za93).

Because they learn by example, neural networks have the potential for building computing systems that do not need to be programmed (Wo96). This reflects a radically different approach to computing compared to traditional methods, which involve the development of computer programs. In a computer program, every step that the computer executes is specified in advance by the network. In contrast, neural nets begin with sample inputs and outputs, and learns to provide the correct outputs for each input (Za93).

The neural network approach does not require human identification of features. It also doesn’t require human development of algorithms or programs that are specific to the classification problem at hand. All of this will suggest that time and human effort can be saved (Wo96). There are drawbacks to the neural network approach, however. The time to train the network may not be known, and the process of designing a network that successfully solves an applications problem may be involved. The potential of the approach, however, appears significantly better than past approaches (Ga93).

Neural network architectures encode information in a distributed fashion. Typically the information that is stored in a neural network is shared by many of its processing units. This type of coding is in stark contrast to traditional memory schemes, where particular pieces of information are stored in particular locations of memory. Traditional speech recognition systems, for example, contain a lookup table of template speech patterns that are compared one by one to spoken inputs. Such templates are stored in a specific location of the computer memory. Neural networks, in contrast, identify spoken syllables by using a number of processing units simultaneously. The internal representation is thus distributed across all or part of the network. Furthermore, more than one syllable or pattern may be stored at the same time by the same network (Ze93).

Neural networks have far-reaching potential as building blocks in tomorrow’s computational world. Already, useful applications have been designed, built, and commercialized, and much research continues in hopes of extending this success (He95).

Neural network applications emphasize areas where they appear to offer a more appropriate approach than traditional computing has. Neural networks offer possibilities for solving problems that require pattern recognition, pattern mapping, dealing with noisy data, pattern completion, associative lookups, and systems that learn or adapt during use (Fr93, Za93). Examples of specific areas where these types of problems appear include speech synthesis and recognition, image processing and analysis, sonar and seismic signal classification, and adaptive control. In addition, neural networks can perform some knowledge processing tasks and can be used to implement associative memory (Kh90). Some optimization tasks can be addressed with neural networks. The range of potential applications is impressive.

The first highly developed application was handwritten character identification. A neural network is trained on a set of handwritten characters, such as printed letters of the alphabet. The network training set then consists of the handwritten characters as inputs together with the correct identification for each character. At the completion of training, the network identifies handwritten characters in spite of the variations (Za93).

Another impressive application study involved NETtalk, a neural network that learns to produce phonetic strings, which in turn specify pronunciation for written text. The input to the network in this case was English text in the form of successive letters that appear in sentences. The output of the network was phonetic notation for the proper sound to produce given the text input. The output was linked to a speech generator so that an observer could hear the network learn to speak. This network, trained by Sejnowski and Rosenberg, learned to pronounce English text with a high level of accuracy (Za93).

Neural network studies have also been done for adaptive control applications. A classic implementation of a neural network control system was the broom-balancing experiment, originally done by Widrow and Smith in 1963. The network learned to move a cart back and forth in such a way that a broom balanced upside-down on its handle tip and the cart remained on end (Da90). More recently, application studies were done for teaching a robotic arm how to get to its target position, and for steadying a robotic arm. Research was also done on teaching a neural network to control an autonomous vehicle using simulated, simplified vehicle control situations (Wo96).

Neural networks are expected to complement rather than replace other technologies. Tasks that are done well by traditional computer methods need not be addressed with neural networks, but technologies that complement neural networks are far-reaching (He90). For example, expert systems and rule-based knowledge-processing techniques are adequate for some applications, although neural networks have the ability to learn rules more flexibly. More sophisticated systems may be built in some cases from a combination of expert systems and neural networks (Wo96). Sensors for visual or acoustic data may be combined in a system that includes a neural network for analysis and pattern recognition. Robotics and control systems may use neural network components in the future. Simulation techniques, such as simulation languages, may be extended to include structures that allow us to simulate neural networks. Neural networks may also play a new role in the optimization of engineering designs and industrial resources (Za93).

Many design choices are involved in developing a neural network application. The first option is in choosing the general area of application. Usually this is an existing problem that appears amenable to solutions with a neural network. Next the problem must be defined specifically so that a selection of inputs and outputs to the network may be made. Choices for inputs and outputs involve identifying the types of patterns to go into and out of the network. In addition, the researcher must design how those patterns are to represent the needed information. Next, internal design choices must be made. This would include the topology and size of the network (Kh90). The number of processing units are specified, along with the specific interconnections that the network is to have. Processing units are usually organized into distinct layers, which are either fully or partially interconnected (Vo95).

There are additional choices for the dynamic activity of the processing units. A variety of neural net paradigms are available. Each paradigm dictates how the readjustment of parameters takes place. This readjustment results in learning by the network. Next there are internal parameters that must be tuned to optimize the ANN design (Kh90). One such parameter is the learning rate from the back-error propagation paradigm. The value of this parameter influences the rate of learning by the network, and may possibly influence how successfully the network learns (Cr95). There are experiments that indicate that learning occurs more successfully if this parameter is decreased during a learning session. Some paradigms utilize more than one parameter that must be tuned. Typically, network parameters are tuned with the help of experimental results and experience on the specific applications problem under study (Kh90).

Finally, the selection of training data presented to the neural network influences whether or not the network learns a particular task. Like a child, how well a network will learn depends on the examples presented. A good set of examples, which illustrate the tasks to be learned well, is necessary for the desired learning to take place. The set of training examples must also reflect the variability in the patterns that the network will encounter after training (Wo96).

Although a variety of neural network paradigms have already been established, there are many variations currently being researched. Typically these variations add more complexity to gain more capabilities (Kh90). Examples of additional structures under investigation include the incorporation of delay components, the use of sparse interconnections, and the inclusion of interaction between different interconnections. More than one neural net may be combined, with outputs of some networks becoming the inputs of others. Such combined systems sometimes provide improved performance and faster training times (Da90).

Implementations of neural networks come in many forms. The most widely used implementations of neural networks today are software simulators. These are computer programs that simulate the operation of the neural network. The speed of the simulation depends on the speed of the hardware upon which the simulation is executed. A variety of accelerator boards are available for individual computers to speed the computations (Wo96).

Simulation is key to the development and deployment of neural network technology. With a simulator, one can establish most of the design choices in a neural network system. The choice of inputs and outputs can be tested as well as the capabilities of the particular paradigm used (Wo96).

Implementations of neural networks are not limited to computer simulation, however. An implementation could be an individual calculating the changing parameters of the network using pencil and paper. Another implementation would be a collection of people, each one acting as a processing unit, using a hand-held calculator (He90). Although these implementations are not fast enough to be effective for applications, they are nevertheless methods for emulating a parallel computing structure based on neural network architectures (Za93).

One challenge to neural network applications is that they require more computational power than readily available computers have, and the tradeoffs in sizing up such a network are sometimes not apparent from a small-scale simulation. The performance of a neural network must be tested using a network the same size as that to be used in the application (Za93).

The response of an ANN may be accelerated through the use of specialized hardware. Such hardware may be designed using analog computing technology or a combination of analog and digital. Development of such specialized hardware is underway, but there are many problems yet to be solved. Such technological advances as custom logic chips and logic-enhanced memory chips are being considered for neural network implementations (Wo96).

No discussion of implementation would be complete without mention of the original neural networks, which is the biological nervous systems. These systems provided the first implementation of neural network architectures. Both systems are based on parallel computing units that are heavily interconnected, and both systems include feature detectors, redundancy, massive parallelism, and modulation of connections (Vo94, Gr93).

However the differences between biological systems and artificial neural networks are substantial. Artificial neural networks usually have regular interconnection topologies, based on a fully connected, layered organization. While biological interconnections do not precisely fit the fully connected, layered organization model, they nevertheless have a defined structure at the systems level, including specific areas that aggregate synapses and fibers, and a variety of other interconnections (Lo94, Gr93). Although many connections in the brain may seem random or statistical, it is likely that considerable precision exists at the cellular and ensemble levels as well as the system level. Another difference between artificial and biological systems arises from the fact that the brain organizes itself dynamically during a developmental period, and can permanently fix its wiring based on experiences during certain critical periods of development. This influence on connection topology does not occur in current ANN’s (Lo94, Da90).

The future of neurocomputing can benefit greatly from biological studies. Structures found in biological systems can inspire new design architectures for ANN models (He90). Similarly, biology and cognitive science can benefit from the development of neurocomputing models. Artificial neural networks do, for example, illustrate ways of modeling characteristics that appear in the human brain (Le91). Conclusions, however, must be carefully drawn to avoid confusion between the two types of systems.

Nowadays,in fields of business and at home,computers are broadly used. However,people keep and share the great number of data if they don’t get enough security properly.A couple of steps must be taken ,which are generally called as computer security to defend the data.What’s computer security?It is a particular realm of computer science that concentrates on the progress and execution of security steps corresponding to the employ of a computer.A secure computing platform is evoluted that makes users be able to work without being anxious about the safty of their data.You are able to do anything you can without being anxious about other people obtaining the data as long as your computer is saftey.In order to achieve this objective,diverse technologys are employed.But machines, which have basic security flaws only can protected after they are altered in some aspects.This result in the consequence lots of machines can’t be able to be secured with usable security systems.

Your aime should be to keep it from unwelcomed programs because the computer is vulnerable to lots of attacks.Pay attention to any strange email in your mailbox,once opening these mails would give the attached virus a access to your system.The attached virus are a bostinate and as long as they are installed,a great many effort are needed to get rid of there threats.

Afterward,you install freash security steps in your computer,however,most of these viruses install freash programs and leave. Owning to their mix with your current programs,and hence it’s hard to remove with ease and it is rather hard to identify these annoy.Stop any of these viruses from getting is the best solution to defend your machine.

Click here to secure your computer effectively.

Today’s world evolves through the power of the Internet. The complexities of our daily living are turned into simpler and easy-to-access powerful source of information and data communication. Barriers are crossed and destinations are reached. Cyber communities are also built. Aside from communicating through simple electronic mails, social networking sites are also getting the pull of the crowd such as Facebook, MySpace, Twitter and the like. Interaction from different people around the globe, among different races, ages, religion and gender are coming into a single strand of way of interacting and mingling with each other. Virtual friendships are made possible today. So when this evolution of computer technology has emerged, it wasn’t made sure at first that everything will be kept in private with us. People are intelligent enough to create malicious software designs that can simply break and decrypt what was originally meant only for ourselves. This makes computer security and social network like Facebook security important to us.

Providing and working out for Computer Security, Security Articles, Facebook Security has also been the most focused practice and principles in any company today. Having a link to the most trusted internet security sites and creating the best software security tools not just give protection for all the information but more importantly it gives you the peace of mind that your access for your data and information will only be yours. Lacking of the best software security tools can really be detrimental to any organization. Below are some of the best internet security tool guidelines that everybody, be it a company or for personal purpose, that must be observed:

Keep it short and readable. Obviously, because humans are becoming more and more intelligent right through these days and apparently we can make things more complicated. We make policies and procedures as long as we can make it possible. This can pass the standard checking tool for any document but the question is, can these documents that are too long and complicated be read by others?

Make mandates appropriate for people. Sometimes in order to attain the best security possible, we codify mandates and make rules that are so strict making it so unrealistic for people to catch up. This is quite impractical and hard to come up with.

Computer Security, Security Articles, Facebook Security protection through information security gadgets. We love technology because we have a clear background of these. We have the technical and engineering background. We create new gadgets and devices and we get excited about hearing new gizmos that could certainly add to the increasing population of techs gadgets. However, we also tend to forget to have security tools that should be deployed with restraints. The limitations must also be accorded and contained in the guidelines and must never be forgotten.

Consider the best practices and their extents. Because we love to refer to “best practices”, we should also try to love to now up to what extents they would be applicable to us. This will show us at least a clue if they can render higher risks or will reduce it. Sometimes we also tend to go for high level frameworks because we think that it will better. Remember that we must also adapt the security tool that we acquire for our system. We should be cautious in choosing which security tools must be used and must be adapted.

Prevention of accidents through Computer Security, Security Articles, Facebook Security. To set up and brace ourselves against possible failures, we must already have a thought of terms for security accidents that could possibly ruin the system. Security incidents happen all the time and the most practical way to prevent this to happen is to make the security more costly to bypass your defences. Invest some effort into breach detection and incident response techniques. Evaluate the success possibilities of your security tool or program.

It might be very confusing for us if we will be the one who will experience breach in our accounts not just Facebook account but worse is when our emails we use for our professional works are the ones affected. That is why it is very essential to look for us to find the best internet security tools that and handily available for us. A little learning about protecting your personal sites from harmful infections scattered in the internet won’t really hurt especially Facebook where the security settings of this users were at risk just recently.

Introduction of 802.11:

“Wireless LANs opens new possibilities for LAN users, which are mainly terminal mobility and easy reconfiguration. In general, wireless LANs has the following advantages”: [J.H.Schiller,2000]

Flexibility: If WLAN nodes are within the network coverage area then they can communicate with each other without major limitations in terminal location. It is not important for the terminals to remain visible to each other. If the frequency of electro magnetic waves is not too high then the walls and other typical obstacles in an indoor environment are mostly penetrated.
Simplified planning: Configuring a ad hoc network is not necessary and the network deigning part is related to radio engineering.
Possibility of a temporary network configuration: Networks which are needed temporarily can be set up using wireless communication. (E.g. during large international exhibitions, sport contests, etc).

“WLANs also have some disadvantages. Most of them are the result of using a radio channel as a signal propagation medium. The main disadvantages are the following”:[Hazysztof Wesolowshi, 2002]

Lower transmission quality as compared with wire line LANs: 10^-3 10^-4 is the order of error in the radio channel or it can be worse than this. To achieve higher quality FEC or ARQ techniques are necessary. For comparison, the error rate for transmission over an optical fibre channel is at most 10^-10.
Lower safety and security: The information transmitted on radio channel can be easily intercepted when compare to wireline LANs. If WLAN is used inappropriately then it can become a source of interference for other sensitive devices such as medical equipment. Wireless LANs rarely work independently of other networks and, wireless transmission is used to access a wire line network.

Different generations in mobile phones:

1G:

1G is short form for first generation analog cellular technology (AMPS is one example of a 1G cellular system). Deployed during the 1980s, this technology has been designed to transmit voice phone calls from wireless handsets. Calls are transmitted in the air and are very easy to intercept. “1G (or 1-G) is short for first-generation wireless telephone technology, cellphones. These are the analog cellphone standards that were introduced in the 1980s and continued until being replaced by 2G digital cellphones. The main difference between two succeeding mobile telephone systems, 1G and 2G, is that the radio signals that 1G networks use are analog, while 2G networks are digital.”[http://www.nationmaster.com/encyclopedia/1G]

Circuit Switch:

Analogue circuit-switched technology system is used in 1G, with FDMA (Frequency Division Multiple Access), it works mainly in the 800-900 MHz frequency bands. The networks had a low traffic capacity, unreliable handover, poor voice quality, and poor security.

To send signal from cell base station to the handset, systems typically allocated one 25 MHz frequency band, and another 25 MHz band for signals being returned from the handset to the base station. Now these bands are split into a number of communication channels, each of which is used by particular caller.

2G

After 1G, 2G (second Generation) mobile telephone came into existence. For the first time 2G introduces a mobile phone which works purely on digital technology. The demands placed on the networks, particularly in the densely populated areas within cities, meant that increasingly sophisticated methods had to be employed to handle the large number of calls, and so avoid the risks of interference and dropped calls at handoffs. They are many common principles involved in both the generation phones. Such as, they both use the same cell structure. But the way the signals are handled are different. 1G network are not capable of providing the more advanced features of the 2G systems, such as caller identity and text messaging.

In GSM 900, for instance, two frequency bands of 25 MHz bandwidth are use. The band 890-915 MHz is committed to uplink communications as of the mobile station to the base station, and the band 935-960 MHz is used for the downlink communications as of the base station to the mobile station. Every band is separated into 124 carrier frequencies, spaced 200 kHz away from each other, in a similar manner to the FDMA method that was used in 1G system. Then, every carrier frequency is extra divided by TDMA into eight 577 usec long “time slots”, each one of which represent one communication channel – the entire number of probable channels obtainable is therefore 124 x 8, producing a speculative maximum of 992 simultaneous conversation.

GPRS and EDGE

2.5G (Second Generation Enhanced) is a general term used to pass on to a standard of wireless mobile telephone networks that lies in between 2G and 3G. The growth of 2.5G has been view as a stepping-stone towards 3G, which was encouraged by insist for improved data services and right to use the Internet. In the development of mobile communications, each generation provide a advanced data rate and additional capability, and 2.5G is no exemption as it is provide quicker services than 2G, but not as fast or as sophisticated as the newer 3G systems.

Some observer have seen 2.5G as an substitute route to 3G, but this appear to be short-sighted as 2.5G is quite a few times slower than the complete 3G service. In technical provisions 2.5G extends the capability of 2G systems by providing extra features, such as a packet-switched connection (GPRS) in the TDMA-based GSM system, and enhanced data rates (HSCSD and EDGE). These enhancement in 2.5G systems allow data speeds of 64-144 kbps, which enable these phones to characteristic web browsing, the use of routing and navigational maps, voice mail, fax, and the transfer and receive of large email messages.

3G

Third Generation mobile telephone networks are the most recent phase in the growth of wireless communications technology. Important features of 3G systems are that they carry much higher data transmission rates and present increased capacity, which makes them appropriate for high-speed data application as well as for the customary voice calls. In fact, 3G systems are planned to process data, and as voice signals are transformed to digital data, these outcomes in speech being deal with in a great deal the similar way as any supplementary form of data. Third Generation systems utilize packet-switching technology, which is more capable and faster than the customary circuit-switched systems, but they do need a somewhat special infrastructure to the 2G systems. “Japanese 3G services were launched in 2001, making Japan one of the first countries to offer 3G technologies commercially. Japan’s 3G technology is capable of matching the quality of fixed-line telecommunications devices whilst providing high-speed data transmission and global roaming to all areas where 3G networks exist.”[The Wireless Telecommunications Market in Japan, April 2006]

W-CDMA and CDMA2000

It is usually accepted that CDMA is a greater transmission technology, while it is compared to the previous techniques use in GSM/TDMA. W-CDMA systems make more capable use of the accessible spectrum, because the CDMA technique enables all base stations to employ the same frequency. In the W-CDMA system, the data is divided into separate packets, which are then transmit using packet switching technology, and the packets are reassembled in the accurate sequence at the receiver end by means of the code that is transmitted with each packet. W-CDMA has a probable problem, because by the fact that, as more user at the same time communicate with a base station, then a event known as “cell breathing” can take place. This effect funds that the user will compete for the limited power of the base station’s transmitter, which can decrease the cell’s range W-CDMA and cdma2000 have been planned to ease this problem.

UMTS

UMTS systems are planned to provide a range of data rates, depending on the user’s conditions, providing up to 144 kbps for moving vehicles (macrocellular environments), up to 384 kbps for pedestrians (microcellular environments) and up to 2 Mbps for interior or immobile users (picocellular environments). In disparity, the data rates support by the vital 2G networks were only 9.6 kbps, such as in GSM, which was insufficient to provide any complicated digital services.

Modulation Techniques:

Process of altering some characteristic of a periodic wave with an external signal is known as modulation. These high frequency carrier signals can be transmit over the air without difficulty and are able to travel long distances. The characteristics (amplitude, frequency, or phase) of the carrier signal are wide-ranging in agreement with the in turn bearing signal. In the field of communication engineering, the in turn bearing signal is also identified as the modulating signal.

FDMA:

Frequency Division Multiple Access or FDMA is a type of channelization protocol used in multiple-access process. In FDMA users are allocated one or more frequency bands, giving them the opportunity to utilize the allocated band without with each other. Coordination of access is done between multiple customers using Multiple Access techniques. Different methods like TDMA, CDMA are also used by users to share access.

TDMA:

Time division multiple access (TDMA) is a shared medium access method of channel (usually radio) networks. It divides the channel in to different time slots and allows different users to share the same frequency channel. The users transmit rapidly each using their own time slot on after the other. Only a part of the channel capacity is used to share the same transmission medium over multiple stations. GSM which is digital 2G technologies uses TDMA. It is also used in other systems like Personal Digital Cellular and iDEN, and in the system of Digital Enhanced Cordless Telecommunications which is a standard for portable phones

CDMA:

Many radio communication technologies use CDMA channel access method. Transmitting information at a time over single channel allowing many users is the main concept and idea in data communications. In this concept several transmitters share a bandwidth of frequency. This is called as multiplexing. Spread spectrum technology and coding scheme (where each user is assigned a code) which allows many users to get multiplexed on the same physical channel is employed in CDMA. In short FDMA divides access by frequency, TDMA divides it by time and CDMA which is a spread-spectrum and coding scheme divides access by assigning a code since a modulated coded signal has much higher data bandwidth than the data communicated itself. The codes assigned occupy the same channel, but the users associated with a code can understand each other.

3G and 802.11 Data Rates and Integration

Data rates of 3G compared to WLANs

The data rates of wireless LANs are anywhere from 1 Mbps to 54 Mbps. It is allowed by the 802.11 standards based on the distance to the access points. Access points can cover only a few thousand meters, making them suitable for small networks such as hotels and airports. When comparing wireless networks built using 3G standards require higher capital investment and support a data rate of around 64 Kbps to 2 Mbps as maximum, but covers a wide area range which enables ubiquitous connectivity. Architectures that allow users to normally shift between these two networks would be advantages and profitable to both service providers and users.

WLANs Integration with 3G networks.

The development of wireless communication has been rapid and it had been applied for many services. Wireless local area networks and cellular mobile networks have been the most useful technologies to use wireless communication. 3G networks cover wider areas with ubiquitous connectivity but with low-speed date rates. Wireless local area networks in turn cover small areas but with higher data rates and easy compatibility of wired internet. 3G and WLAN possess some complementary properties. Integrating these two networks will provide users high speed wireless data service and ubiquitous connectivity. Integration of thesetwo networks involves several issues to be considered which are authentication, billing, QoS, and roaming with out interruption in between the networks.

The degree of inter-dependence one is introducing between these two networks, there are two methods of integrating them. They are:

Tightly-coupled internetworking
Loosely-coupled internetworking

Tightly-coupled Interworking

The concept behind tightly-coupled approach is that the 802.11 network should appear like another 3G access network to the main 3G network. In this the 802.11 network will imitate functions which are present in 3G core network. In the figure we can see that the 802.11 gateway uses WISP No.1 to imitate itself as PCF for 3G core network and in case of CDMA2000 network it appears as SGSN to UMTS. The gateway of 802.11 hides all details of 802.11 networks from 3G core and it tries to implement all 3G protocols required for a 3G access network. “Mobile Nodes in this approach are required to implement the corresponding 3G protocol stack on top of their standard 802.11 network cards, and switch from one physical layer to the next as needed. All the traffic generated by clients in the 802.11 network is injected using 3G protocols in the 3G core”.[ M. Buddhikot, G. Chandranmenon, S. Han, Y. W. Lee, S. Miller, L. Salgarelli, 2003]

The unlike networks would divide the similar authentication, signalling, transport and billing infrastructures, separately as of the protocols utilized at the physical layer lying on the radio interface. However, this advance presents quite a few disadvantages. As the 3G core network straight away expose its interfaces to the 802.11 network, one operator must possess both the 802.11 and the 3G parts of the network. In fact, in this case, separately operated 802.11 island cannot be integrated with 3G networks. Today’s 3G networks are self deployed using cautiously engineered network-planning tools, and the ability and constitution of each network part is calculated utilizing mechanisms which are very much exact to the technology used over the air interface. By injecting the 802.11 traffic non-stops into the 3G core, the unit of the entire network, as fine as the constitution and the plan of network elements such as PDSNs and GGSNs has to be customized to maintain the increased load. The arrangement of the client parts also present many issues with this move.

“First, as described earlier, the 802.11 network cards would need to implement the 3G protocol stack. It would also mandate the use of 3G-specific authentication mechanisms based on Universal Subscriber Identity Module” or “Removable User Identity Module (R-UIM) cards for authentication on Wireless LANs, forcing 802.11 providers to interconnect to the 3G carriers’ SS7 network to perform authentication procedures”. [Removable User Identity Module Standard for CDMA 2000 SpreadSpectrum Systems, June 2000.]

This would also entail the use of 802.11 network interface cards with fixed USIM or R-UIM slots or external cards plug individually into the user devices. For the reason given above, the complication and the high price of the rearrangement of the 3G core networks and of the 802.11 gateways would push operators that choose the tightly-coupled approach to turn into uncompetitive to 802.11- only WISPs.

Loosely-coupled Interworking

Like the earlier architecture, the loosely-coupled draw near calls for the beginning of a new element in the 802.11 network, the 802.11 gateway. On the other hand, in this design, the gateway connect to the Internet and do not contain any straight link to 3G network essentials such as PDSNs, GGSNs or 3G core network switches. The users that access services of the 802.11 gateway may comprise users that have in the vicinity signed on, as well as mobile customers visiting from erstwhile networks. “We call this approach loosely-coupled interworking because it completely separates the data paths in 802.11 and 3G networks. The high speed 802.11 data traffic is never injected into the 3G core network but the end user still achieves seamless access. In this approach, different mechanisms and protocols can handle authentication, billing and mobility management in the 3G and 802.11 portions of the network. However, for seamless operation to be possible, they have to interoperate”. [Wireless IP Network Standard. P.S0001-A-1, 2000]

In the case of interoperation with CDMA2000, this require that the 802.11 gateway supports Mobile-IP functionalities to hold mobility across networks, as well as AAA services to interwork with the 3G’s home network AAA servers. This would allow the 3G provider to gather the 802.11 office records and make a unified billing statement representing usage and different cost schemes for both (3G and 802.11) networks. At the similar time, the utilization of well-matched AAA services on the two networks would permit the 802.11 gateway to dynamically get per-user service policies as of their Home AAA servers, and to implement and become accustomed to such policies for the 802.11 network. “Since the UMTS standard do not yet include support for IETF protocols such as AAA and Mobile-IP, more adaptation is required to integrate with UMTS networks. Mobile- IP services would need to be retrofitted to the GGSNs to enable seamless mobility between 802.11 and UMTS. Common subscriber databases would need to interface to Home Location Registers (HLR) for authentication and billing on the UMTS side of the network, and to AAA servers for the same operations to be performed while clients roam to 802.11 networks. There are several advantages to the loosely-coupled integration approach. First, it allows the independent deployment and traffic engineering of 802.11 and 3G networks”.[ IP Mobility Support for IPv4, January 2002]

Conclusion.

In this paper, I described the issues in the integration of third-generation wireless networks with local-area wireless technologies such as 802.11. In that introduction of two architectural choices for the integration, termed as tightly-coupled and loosely-coupled interworking, has been described. The WLANs when compared to 3G networks have high-speed data rates but small coverage area. The WLANs infrastructure is expensive compared to wireline LANs but cheaper when compared to 3G infrastructure.

References

C. Perkins (Editor). IP Mobility Support for IPv4. RFC 3220, IETF, January 2002.

http://www.nationmaster.com/encyclopedia/1G, Accessed 1st January, 2009.

Hazysztof Wesolowshi, Mobile Communication System, John Wiley & Sons, 2002

J.H.Schiller, Mobile Communications, Addison-Wesley, Reading Mass, 2000

M. Buddhikot, G. Chandranmenon, S. Han, Y. W. Lee, S. Miller, L. Salgarelli, Integration of 802.11 and Third-Generation Wireless Data Networks, IEEE INFOCOM 2003.

Removable User Identity Module Standard for CDMA 2000 Spread spectrum Systems. C.S0023-0, 3GPP2, June 2000.

The Wireless Telecommunications Market in Japan, April 2006, Market Research Centre, Canada

Wireless IP Network Standard. P.S0001-A-1, Third Generation Partnership Program 2 (3GPP2), 2000