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The book ‘Radio Engineering and Antennas’ is intended as a ready reference, study guide and a one-stop source for wireless communications professionals, practicing telecommunication engineers, technology professionals, engineering graduates and students. The guiding principle in writing this book is, to provide a simplified understanding of various concepts in the field of wireless communications, with a special emphasis on their practical application to the wireless communication standards that are practiced currently around the world, such as WiFi, WiMax, GSM, CDMA, and LTE. The general flow of various topics is to begin with a review of the basics, and then move on to current application of wireless technologies through practical examples and illustrations.

This book serves as an excellent companion to learning webinars offered on this web site. These webinars are conducted via live and interactive online sessions by experienced instructors and are based on the contents of this book. The book and the webinars can be used in conjunction to study for the ‘Radio Engineering and Antennas’ section of the IEEE WCET (Wireless Communication Engineering Technologies) certification exam, which is required to earn the IEEE WCP (Wireless Communications Professional) credential.

A list of acronyms, bibliography and web sites, is included at the end of the book as a quick reference for additional information.

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How many antennas are enough?

Massive MIMO promises to push the envelope of Wireless Broadband further….


Consider this…You have just received a link to the latest HD video on unusual sightings in the wild, and you race to stream the video to your smartphone. As luck would have it, your wireless signal fades and you are not the first one to watch the unusual video! You curse the phone and the service provider but to no avail. It is the last mile that is the problem here. Your wireless signal faded unexpectedly, right when you needed it the most.

What if, you had, not one antenna on your phone, but an array of antennas, that could mitigate the well known problem of multipath fading. Massive MIMO (Multiple Input Multiple Output) addresses exactly this challenge.

MIMO is a technology used in 4G Wireless Broadband. It provides multiple communication paths between the transmitters and the receivers. This is made possible by deploying multiple antennas on the transmitting as well as receiving side. Whereas it is comparatively easier to deploy multiple antennas on the cell towers, it is much more challenging to deploy multiple antennas on the smartphones.

This is because of the limits imposed by basic physics. The multiple antennas on a device must be separated by a distance of half wavelength to avoid mutual coupling between the antennas. For example, for a carrier frequency of 1800 MHz, half wavelength distance is about 8 cm. Antenna separation of this magnitude is difficult to achieve within the form factor of a smartphone.

Welcome Millimeter Wave carrier frequencies! If we push up the carrier frequency to, let us say, 60 GHz, the required minimum separation between multiple antennas is pushed down to only 2.5 mm. Well, that implies that we could easily fabricate 16 antenna elements within a square of 1 cm.

In other words, with Massive MIMO, we have the ability to squeeze an array of 16 antennas or more, within the form factor of a smartphone. Infact, the next generation Wireless Broadband standard called WiGig (IEEE 802.11ad) aims to achieve a robust data rate of upto 7 Gbps by using 60 GHz unlicensed carrier and Massive MIMO techniques.

Isn’t it great to dream of freedom from multipath fades?
What do you think?

Places to go from here:

Wireless Gigabit Alliance


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Democratized New Product Development

Democratization of new product development promises to fulfill the real needs of end users…
Democratic Product

When was the last time you found a product at a store that exactly fulfilled your needs? We always make compromises when selecting a product, because none of them was ever designed to fulfill the needs of a single individual. The conventional approach is to develop a new product with a set of functional requirements that are common to largest population of users. This is necessary to realize the economies of scale, so that the product can be sold to largest number of users at an affordable price and a hefty profit for the manufacturer, although the product will not fulfill the entire expected functional requirements for any single user.

Guess what, who is at the receiving end? Users like you and me! If you even make an attempt to ask the product manufacturer for a customized version of the product, you get pushed back by the hefty price to develop a version of the product, that is designed just for you.

There is light at the end of tunnel though, because of the way innovation and new product development is being transformed. This has been enabled largely by the way end users can communicate and share knowledge with each other over the web and through social media, and therefore drive the development of new products in the desired direction.

The trend towards democratized new product development is most noticeable in software and information products and is driven by open source communities of developers, innovators and end users, who are not satisfied with what commercial products have to offer for the masses. They contribute their skills, time and effort to create sensible products, and share their innovations for free.

Did you ever have a chance to build a personal computer from ground up, load it with an open source operating system, such as Fedora or Ubuntu, and install open source productivity applications, such as Open Office? Try it at least once and you will never forget the satisfaction derived from building a product that fulfills all your expectations and functional requirements.

Well, the point here is that the products, software as well as physical, can be made more useful by democratizing their development. The product manufacturers, who are aware of this trend, will ensure their own long term survival by exploiting the trend for the common good. A good example of how some manufacturers facilitate user innovation is by providing tool kits for the development of prototypes for new products.

As it seems, the domain of new and innovative product development, that was until now restricted to shining corporate R&D centers, is now expected to include the democratized user community, that will drive the functional requirements for any new product. The synergy between the corporations and the end users, by way of democratized new product development is sure to promote common good.

Places to go from here:

Democratizing Innovation



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Artificial Immune Systems

Artificial Immune Systems promise effective antidote for computer viruses, worms and malwares…


The havoc caused by viruses, worms and malwares on the modern interconnected world cannot be underestimated. As the complexity and scale of our interconnected networks of computers and other intelligent devices grows, they become increasingly vulnerable to these malicious forms of artificial life, that seem to spread and replicate just like natural viruses.

One of the challenges that anti-virus scanners face is the fast pace at which new viruses and malwares are created everyday. If you do not update the known virus signature database on your computer in a timely manner, your computer is sure to be hit by some new unknown deadly virus or malware, potentially infecting and damaging your files.

Polymorphic viruses, that modify themselves, and Metamorphic viruses, that rewrite themselves completely from one infection to another, pose another serious threat to the capabilities of contemporary anti virus scanners.

An alternative approach is to learn how biological systems, such as human bodies, protect themselves from natural viruses, and then apply those techniques to build Artificial Immune Systems that will protect our interconnected networks of intelligent devices.

The natural immune systems have a mechanism consisting of T-cells that can discriminate between self, that is, genuine cells in the body, and non-self, that is, viruses and harmful foreign cells. When a T-cell encounters a non-self, it attaches to the harmful cell, ultimately destroying it.

This mechanism works even for unknown non-selfs. The body rejects whatever its immune system does not like, with the objective of protecting its own genuine healthy cells.

When applied to Artificial Immune Systems (AIS), the self consists of authorized users, allowed IP addresses, port numbers, protocols, applications, utilities and legitimate files. The non-self in this context consists of computer viruses, worms, malwares and rogue programs.

The AIS essentially considers each self in the system to be represented by a string or a collection of sub-strings that uniquely identifies each data-set with certain attributes. For an incoming packet stream, the string may include source and destination addresses, checksum, port numbers, protocol, encoding etc. For a file in a storage system, it might additionally include authentication ids and file owners. In this way, AIS maintains knowledge of the known state of the system with these self-strings.

The task for AIS now is to continuously monitor these self-strings and detect any change, by comparison with detector strings, that are made up of all those strings which are not part of the set of self-strings. In other words, the set of detector strings includes all possible non-self-strings. For example if a self-string for incoming packet on a network interface includes only the source ip address, then the detector string will exclude source ip address In practical terms, the detector string will look for any incoming non-self packet that does not have the source ip address

The AIS algorithm generates the set of detector strings by using Negative Selection Algorithm (NSA). To begin with, detector strings are randomly generated and then matched with the known self-strings. If there is a match, the detector string is deleted. As a result, the remaining set of detector strings consists of only non-self-strings.

Once the detector strings are matured by this matching process, they are put into action for a match with any unknown or known non-self-string. If a match is detected, remedial action is taken to isolate and remove the detected non-self-string, which may be an unwanted computer virus, worm, malware or any unknown foreign agent potentially harmful to the self.

AIS can be implemented on each device that is connected to the network in a distributed manner. As a further extension, the AIS on each connected device can be made to collaborate with each other over the network. This collaborative AIS framework is expected to speed up the detection of known as well as unknown non-self entities, as the results of detection can be shared within the Distributed Artificial Immune System (DAIS).

Consider a future scenario with DAIS implemented everywhere. You will prefer to buy an immunized smartphone, or an immunized notebook computer. The Cloud Services Providers will ensure that all the servers on their network are immunized.

You as a consumer may demand to look at the immunization record of a service network, before you sign up for any of the cloud services!

What do you think?

Places to go from here:

Artificial Immune System for Intrusion Detection



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Automatic Accent Identification


Accent Identification in speech recognition has the potential to provide value added phone services…English is the de-facto international language of choice to conduct all official business. For most people around the world, it happens to be the second language and therefore, English is spoken in myriad varieties of accents. However, different accents in spoken language can prove to be problematic.

For a moment consider that you are calling the customer service of a global service provider. The customer service has an automated answering service with built-in speech recognition, that prompts you to say specific information in English.

If your accent does not have a match with the one that the automated speech recognition system is programmed with, guess what, you may end up cursing the customer service and asking why they cannot provide you service from a human operator.

Consider another scenario, when you are dealing with a multi-site global project team, and you are required to have a teleconference with team members at least once in a week. The team members have non-native English accents that come in different flavors, such as Chinese English, Indian English and East European, to name a few. These multiple accents could prove to be an obstacle to effective and efficient communication within the team.

What if, we had access to Accent Identification service from our phone services provider?

The automated customer service speech recognition system could identify accents, to have a perfect match with spoken English words, and ease the frustration that resulted due to the absence of human operator.

The teleconferencing service could normalize multiple accents of spoken English, and convert spoken English from multiple accents to intelligible text that is visible to all team members in real time.

However, accent identification is technically a difficult challenge. Essentially, contemporary speech recognition systems use Hidden Markov Models (HMM) to recognize the basic sound segments in speech, called phonemes. These are probabilistic models and work reasonably well with a fixed accent in speech, but fail when there is a dramatic change in accent.

Fortunately, the HMMs can be trained to recognize a specific accent. So, any Accent Identification system could use multiple of these HMMs in parallel, where each HMM is trained for a specific known accent of spoken English. When the incoming speech segments, or phonemes are processed in parallel by the HMMs, the one with the highest match will be recognized as the correct accent.

However, the challenge lies in having tens, if not hundreds of these HMMs trained for all the known accents of spoken English. Add to this the ability to identify accents and recognize speech in real time, to make it a really viable business case for the phone service providers.

The anticipated payoffs that could result from a successful Accent Identification service should be a strong reason to invest funds for advanced research in these technologies.

What do you think?

Places to go from here:

Accented Indian English Automatic Speech Recognition
An evolutionary approach for accent classification in IVR systems



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Self Organizing Agile Teams


Agile teams self organize to implement product backlogs…Imagine a small group of highly creative individuals, who are tasked to develop the complete design for a product, that typically includes the modules for web interface, database design, algorithmic design, and a FPGA interface that implements the data processor.

Now, consider for a moment that you are playing the role of a conventional project manager, responsible for getting work done from this team, and you are experienced in the Waterfall model of product development. What will you do?

You will exercise your authority to assign each module to the individual, whom you think is the most competent to work on that part of the product. You prepare a work assignment plan and publish it, so that you can hold each individual accountable for their assignments. In other words, you think that your job is to see that the plan is followed rigidly and all controls are in place, to ensure the completion of this project within budget.

Did you ever try to find out what happened with these creative individuals? They actually resented your authority and did not accept the rigid plan that was shoved down their throat. They seemed to feel that they had no role in the way modules were assigned to them, neither did they accept rigid controls in the project. In short, you killed their creativity, that might have been exploited for the benefit of everyone, had you followed a different approach.

So, what is the best way to get things done from a group of highly creative individuals, who are sovereign independent people in their own right?

First of all, we need to respect the creative spark in each individual. Nature has blessed each one of us with inherent capabilities and each individual knows what he can do best. Moreover, no creative individual likes to be told what to do and what not to do. We are born free and we do not want to be controlled. We always have a yearning to create something unique, and if we have the freedom to create, we contribute our best.

With this revelation in mind, what is a Waterfall project manager supposed to do?

He needs to let the group of creative individuals self organize and let each one of them own the modules. The project manager’s role is that of a facilitator. He lets the individuals freely interact and come up with innovative solutions. There is more informal communication within the team, to resolve problems on the fly. The project manager, also called the Scrum Master in this role, conducts daily stand up meetings that do not last longer than 15 minutes, where the team members share the progress on each of their modules. The Scrum Master helps to solve problems and remove obstacles that hinder progress on the project.

This is the Agile way of executing projects by self organized Agile teams. Isn’t it the natural way all projects should be done?

Places to go from here:

The Agile Manifesto
Self Organizing Teams in Agile Software Development



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Smart Materials of 21st Century

Smart Mat

Smart or intelligent materials are engineered to respond to their environment…These materials are embedded with their own sensors, actuators and control systems that are responsive to presence of light, temperature, pressure or other environmental conditions, and can cause the material surface to change its shape, texture, color or temperature. When engineered at atomic or molecular scale, these materials can be made sensitive enough to human touch, just like human skin. They can even be made to heal themselves if fine cracks begin to appear inside the structure.

Now, think of the products and applications that could be created out of these intelligent materials.

You could have a dream mattress made to order, that will assume the most comfortable shape and texture as per your personal requirements. It could heat or cool based on your profile or your body temperature. By applying the right amount of gentle pressure, it could help you overcome fatigue.

Another application could be the seat of a work chair, that auto adjusts its shape, texture, height, angle and temperature, and aligns itself according to your personal profile.

A little more far fetched application could be a personl robot clothed with an artificial skin made out of smart materials, that responds to human touch. This personal robot will have the ability to imitate human gestures by controlling the shape and texture of its facial skin.

Industrial structures could benefit by monitoring the cracks inside the material, that might appear due to continued stress or fatigue. At appropriate threshold, the self healing function of the material could be triggered to fix internal cracks, and avoid any damage to the structure.

Infact, future space missions are expected to require the extensive use of smart materials, that can bend on command, self heal the cracks and withstand the extreme conditions of outer space.

Future homes could have walls coated with smart materials that will change color and texture on command or in response to ambient light and heat conditions. The reflectivity of roof top could be changed based on how much heat needs to be absorbed.

Can you imagine any other everyday applications of smart materials?

Places to go from here:

NASA Smart Materials
Smart materials: an overview



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