e-book Wireless Multimedia: A Guide to the IEEE 802.15.3™ Standard

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In the process of developing the standard, a call for applications CFA is sent out to solicit responses from manufacturers and users to indicate the types of applications for which they need a solution. Some of the applications that would use telephone quality audio are: cordless telephones and intercoms. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account.

If the address matches an existing account you will receive an email with instructions to retrieve your username. Chapter 2. James P. Gilb Dr. Search for more papers by this author. Book Author s : James P. For equipment designers and network operators, the need to support multiple operating modes is a growing problem. Even operation in a single country and on a single carrier requires that multiple frequency bands and operating modes be supported.

Further Reading

WiFi has its own challenges with a growing number of bands and modes in use and more being planned. While operation in the 2. The newer versions of IEEE SDM transmits multiple data streams concurrently within the same channel. The data rate is then multiplied by roughly the number of data streams being used.

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A further refinement is the use of a variety of modulation and coding schemes MCS. Different MCS levels can be used, based on the channel characteristics, capabilities of the transmitter and receiver, and signal quality. Then there is the ability to vary the guard interval. A guard interval is a period of time between symbol transmissions that allows reflections from multipath to settle before the next symbol is sent. A mobile device must support over 40 RF bands to be fully international. IEEE Standards 95 and the guard interval. In IEEE However, IEEE That makes for possible combinations and a very wide range of potential data rates.

This increases the number of possible combinations from to , and an even greater spread in the achievable data rates, as shown in Figure C RF interference can have any of several different impacts on a WiFi network. In most cases, RF noise will degrade the signal quality, lowering the achievable data rate. RF noise is not the only fac- tor that impacts the data rate. The amount of signal reflections, multipath, in the environment will also have a very significant impact on the data rate.

RF interference can also cause packet errors, decreasing the data rate because more packets must be retransmitted. In these cases, the impact of the interference is not a loss of connection but a slower data rate. However, if a high data rate is needed because a lot of data is being sent or if the wireless local area network is already congested with a lot of concurrent users, then the impact can be very significant.

Current Issue

In extreme cases, RF interference can totally block a WiFi signal, at least on some channels or even in an entire band. Here again, if the interference only blocks some channels, the WiFi clear channel assessment CCA may simply identify that and move to a different channel, perhaps in a different band. The user may not even notice.

However, under crowded conditions the loss of a channel or band could be catastrophic. In addition, IEEE Depending on the operating mode, it may be difficult or even impossible for a user to know that they are expe- riencing interference. This also creates the very real possibility that network managers will not get the information they need or misunderstand the data they are getting. They may think that they are being given an accurate picture of the activity on their network, but the sources of their information may be screening off critical information and only giving them part of the picture.

It is vital that network managers understand how their network data is coming to them and what information their tools may be filtering out. Filtering of data works both ways. When data is filtered in the right way for the problem being worked on, it helps the network manager to quickly spot where the problem is. But when the filter is incorrectly matched to the current need, it can blind the network manager to the true problem. Network fragmentation creates a vast array of possible situations.

When the fragmentation of the WiFi and cellular networks is brought together, an incredible number of possible combi- nations are created. The potential is that in any specific situation a number of combinations of. Modulation and coding schemes used by IEEE However, other combinations may be very problematic. If CCA and other interference manage- ment methods work well, then the conflicting combinations may be automatically identified and avoided, without the user ever being aware of it or needing to take any action. However, in other situations these mechanisms either may not be capable of dealing with the situation they face or not have options available to avoid the interference.

IEEE Standards 97 A potential for interference that appears particularly concerning is created by the approaching use by LTE of the bands adjacent to the 2. LTE Band 40 operates below the 2. Sprint used LTE band 41 in the U. Aeronet is planning on using the band in Puerto Rico. Component manufacturers work closely with their leading customers to support their needs and have the components available for the next generation of technology.

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Freescale is a leading provider of RF components, particularly power amplifiers, and a good example of how new components show the next steps their customers will be taking with their products. These transistors span a broad range of power points, from 50 W to W. In an asymmetrical Doherty configuration, it delivers The issue of band fragmentation is complex enough if equipment can be relied on to correctly comply with local regulatory requirements.

A wide array of wireless devices can be bought in one country but used in another, where they may be operating in violation of local regulations.

IEEE - Wikipedia

Interference is addressed in the context of the business and regulatory structure within which airports operate. The guidebook is designed to provide practical assistance for improving WiFi performance by enhancing the ability of airport authorities to identify when radio frequency interference is occurring and then how to eliminate, reduce, or at least minimize its impact. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

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Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available. Thus, we illustrate some of the trade-offs that must be considered in using the Standard method and highlight the usefulness of the metrics we have proposed in conducting these evaluations. The complexity of the standard and the consequent heavy requirements, which have not encouraged software engineers to adopt the standard.

The developing complexity evaluation drives us to propose two possible implementation strategies that cover almost all possible use cases and eases handling the standard by non-expert users. The first one is focused on medical devices MD and proposes a low-memory and low-processor usage technique. In this way a framework for MDs can be obtained.

Second one is focused on more powerful machines such as data loggers or gateways aka. It consists on a general platform that can access to any device supported by the standard.

Control physical layer (PHY) data unit

Ensuring self-coexistence among IEEE In this study, we propose a fully-distributed non-cooperative approach to ensure self-coexistence in downlink channels of IEEE We formulate the self-coexistence problem as a mixed-integer non-linear optimization problem for maximizing the network data rate, which is an NP-hard one. This work explores a sub-optimal solution by dividing the optimization problem into downlink channel allocation and power assignment sub-problems. Considering fairness, quality of service and minimum interference for customer-premises-equipment, we also develop a greedy algorithm for channel allocation and a non-cooperative game-theoretic framework for near-optimal power allocation.

The base stations of networks are treated as players in a game, where they try to increase spectrum utilization by controlling power and reaching a Nash equilibrium point. We further develop a utility function for the game to increase the data rate by minimizing the transmission power and, subsequently, the interference from neighboring networks. A theoretical proof of the uniqueness and existence of the Nash equilibrium has been presented.