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January 30, This blog post examines the following factors that can impact Wi-Fi interference: The need to support multiple wireless standards Different types of interference Why band edges matter The importance of high-performance bandedge and coexistence BAW filters. Use filters with very steep band edges.
If the Wi-Fi unit doesn't have bandedge filters as in the block diagram on the left , your Wi-Fi strength and streaming degrade to the point where buffering occurs. Issue Purchase - Online Checkout. People also read Article. Xi Yang et al.
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My gratitute goes to Phil Meyler from Cambridge University Press, whose idea it was for me to write this book and who made it all happen with his very able team of technical and production editors, including Emily Yossarian, Anna Littlewood, Emma Pearce and Irene Pizzie. There are two specified unlicensed bands for the operation of wireless systems, namely: i the industrial scientific and medical ISM band that includes the MHz, 2.
This band was opened in in the United States in order to expand broadband access opportunities. Few rules apply in the unlicensed bands such as the ISM band.
For example, the rules defined in the Federal Communications Commission Title 47 of the Code for Federal Regulations Part 15  relate to the total radiated power and the use of the spread spectrum and frequency hopping modulations. It is commonly understood that all users of the unlicensed bands can equally affect the quality and the usefulness of this spectrum. Thus, the major downside of the unlicensed band is that frequencies must be shared and potential interference tolerated. We distinguish between several types of users in these unlicensed bands.
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Although the discussion and the examples provided in this book relate to wireless networks, the performance evaluation approach and the solutions may apply to other wireless systems. There are three types of wireless networks that we consider depending on the bandwidth and the coverage area supported. Wireless personal area networks WPANs are intended for cable replacement systems and short distance ad hoc connectivity.
Communications in WPAN are normally confined to a person or object and extend up 1 2 Introduction to 10 meters in all directions. This is in contrast to wireless local area networks WLANs that typically cover a moderately sized geographic area such as a single building or campus. They are often used to provide the final few feet of connectivity between the main network and the user. Finally, wireless metropolitan area networks WMANs  are mainly designed for broadband connections over long distances up to several tens of kilometers.
Although they can be used to provide last mile connectivity to mobile and vehicular users, they are mainly intended for interconnecting WLAN hotspots and cellular coverage areas. Thus, each wireless network type may be seen as filling a specific niche area and supporting a different application need, altough the co-location and simultaneous operation of all these networks in the same environment poses an unprecedented challenge since they are all competing for the same spectrum.
During the decade — we witnessed the emergence of a few dominant wireless technologies, such as IEEE The vision for interconnecting heterogeneous networks makes the coexistence problem extremely important and the solutions considered even more challenging. If the availability of the unlicensed bands makes the proliferation of wireless networks at all possible, coexistence is the only strategy to ensure their proper operation.
Published results can be generally classified into at least three categories depending on whether they rely on analysis, simulation, or experimental measurements in order to provide quantitative measurements. This error probability is a function of several parameters; for example, the number of transmitters, the distance between the transmitters and the receiver, the difference in power level between the transmitter and the interferers, and the receiver technology considered.
The results obtained are generally useful in order to gain a first order approximation on the impact of interference and the resulting performance degradation. However, these analytical models often make assumptions concerning the traffic distributions and the operation of the media access protocol which can make them less realistic.
More importantly, in order for the analysis to be tractable, mutual interference that can change the traffic distribution for each system is often ignored. Therefore, mathematical modeling is often used to complement measurements obtained from experimental and simulation data. Examples of experimental measurements can be found in refs[21,34,40,52,61]. In the case where the implementation details are completely known, including various optional add-ons and parameters, then the evaluation can be extremely informative.
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However, access to the complete implementation details is often restricted by the vendors or the implementors leading to the so-called testing of black box equipment. Thus, the results obtained are not applicable outside the experimental set-up of the equipment tested. Furthermore, since parameters cannot be modified, their effects on performance is not easily understood.
Therefore, experimental modeling is useful mostly in the context of specific product development and testing. It consists of using computer simulations to model the behavior of the protocols under consideration. This approach can provide a flexible framework where detailed parametrized models for the media access control and physical layer protocols are combined and the interactions between the various system parameters are identified and accurately quantified.
Simulations play a critical role in evaluating scalability issues and complex system behavior where parameters are modified and their effects on the overall performance quantified. Examples for simulation 4 Introduction models developed to evaluate wireless network interference can be found in refs [20,48,55,67]. The question we ask here is, how do these methods relate to the performance analysis techniques discussed in this book?
Basically, the evaluation approaches we consider are broken up into two categories. First, we consider an open-loop interference evaluation technique where the effects of mutual interference are ignored. Secondly, we describe a closed-loop evaluation approach where the interactions amongst interfering systems are considered. Results comparing both approaches are also discussed. Observe that all three modeling approaches presented above can be used in either open-loop or closed-loop evaluations, although closed-loop modeling is often associated with simulation modeling and open-loop evaluation is more related to mathematical modeling.
The focus is on adaptive and system-level solutions that can augment or enhance traditional filtering, anti-jamming, and physical layer techniques. Basically the emphasis is placed on solutions that do not require major changes to the hardware and the technical specifications of the technologies considered.
Interference mitigation has always been and remains a big part of any communication system design cycle. Since wireless system engineers have always had to contend with interference from both natural sources and other users of the medium, the classical communication design cycle has consisted of predicting channel impairments and choosing adequate modulation and error correction schemes.
Error correction can even be made to be adaptive to the error characteristics in the operation environment, as was shown by Eckhardt and Steenkiste , in which the effects of using an adaptive error correction scheme are investigated and adaptive schemes adjusted based on the environment. Power control is another adaptive technique generally used in spread spectrum systems such as carrier division multiple access CDMA. In addition to these design choices and adaptive techniques, there are several known physical layer interference suppression techniques such as notch filtering and adaptive equalization .
In contrast to these so-called classical approaches to interference mitigaton, our contribution becomes valuable when redesigning systems from scratch is not considered to be a viable option. Therefore, we favor in our discussion adaptive control strategies, system parameter adjustments over other signal processing, and physical layer strategies that are well documented and widely available in the literature. The techniques presented 5 1.
The IEEE This is a standing group that advises the executive committee on coexistence matters and assists various working groups to assess and develop coexistence strategies accurately. Prior to the formation of the IEEE This document considers solutions for mitigating the interference between these two technologies. Solutions range from collaborative schemes to be implemented in the same device to fully independent solutions that rely on interference detection and estimation. A priority of access is given to Bluetooth for transmitting voice packets, while WLAN is given priority for transmitting data.
They all use similar techniques for detecting the presence of other devices in the band, such as measuring the bit or frame error rate, the signal strength or the signal to interference ratio often implemented as the received signal indicator strength RSSI. Other MAC scheduling techniques known as packet encapsulation rules , or overlap avoidance OLA , use the variety of Bluetooth packet lengths to avoid the overlap in frequency between In ref.
Furthermore, a number of algorithms have been proposed on fair scheduling [57,58,62]. While there may be some differences in implementation and complexity, the basic idea in all these algorithms is for sources experiencing a bad wireless link to relinquish the unutilized bandwidth to other sources that can take advantage of it.
Compensation in bandwidth occurs when the channel conditions improve in order to achieve the so-called long term fairness objective. While the interference mitigation problem that we are trying to solve bears some resemblance to some of the problems addressed in refs [38,57,58,62], there are important differences to note. Regarding interference mitigation, it is important to consider an instantaneous measure of fairness rather than a long term fairness objective. The reason is as follows. All previous work uses a two state Markov channel model for each link.
The transition probabilities between the good and bad states are in the order of several seconds to account for periods of fading, multipath and various other wireless effects.