The term Passband signal in the current context refers to the modulated signal that results from a baseband signal modulating a carrier wave. Passband signals have some interesting characteristics that we will cover by referring to the diagrams below. (Disclaimer: illustrative purposes only).
Properties of Passband Signals
Shifted Frequency Response
The complete frequency response (including both positive frequencies and negative frequencies) of the baseband signal is preserved in the passband signal, but is now centered around the positive and negative frequencies of the modulated carrier wave. We say that the baseband frequency response has been “moved up” from 0 Hz to the frequency of the carrier.
If we were to describe the bandwidth of the real baseband signal in the first image, we would simply describe it by its positive frequency components (in green). We don’t stop to include the negative frequency components. Think about the voice signal in a narrow-band digital phone. We measure the bandwidth from 0Hz to the maximum significant frequency component, its bandwidth is 3400 Hz. In comparison, the bandwidth of the passband signal is measured from its smallest significant frequency component to its largest significant frequency component. This value is double that of the old baseband signal bandwidth. This is a common phenomenon in allmost forms of wireless communications and modulation types involving real signals.
There is an analog amplitude modulation variant called single-sideband modulation that conditions the input and output signals of the modulator using mixers and filters respectively to eliminate the frequency doubling effect. I never encountered something like this in digital communications technologies.
As shown in the second image, complex baseband signals do not have a symmetrical frequency response around the 0 Hz mark. As a result, when they are used to modulate a complex carrier, you will simply see the complete frequency response shifted up to the carrier frequency. There is no guarantee we will see the same “bandwidth doubling” effect that we do with the symmetrical frequency response of a real valued signal. That said, the same mechanics are involved that shift the frequency response to the positive and negative carrier frequencies!
Symmetry around 0 Hz.
The real frequency components of a passband signal are symmetrical around the 0 Hz mark. That means that the real frequency components at negative frequencies are equal to the realfrequency components at positive frequencies for the passband signal. We will not concern ourselves with the imaginary frequency components that occur when dealing with complex signals as our focus is on communications signals which are predominantly real numbered.
If you conduct a Fourier transform on the modulated carrier signal, you will see the real, negative-frequency components of the modulated signal centered around -fc. You will notice that these are the perfect mirror image of the real, positive-frequency components centered around fc. If you look at real world version of the same signal through a spectrum analyzer, you will see only the positive-frequency components.
Signal filtering plays a fundamental role in electronics and communications. Filters modify specific frequency components of time-domain signals and are used as a tool for signal quality improvement, information recovery and frequency separation . Filters are a fundamental frequency domain tool and as a component in electronic circuits and digital signal processing allow us to:
Isolate circuits from DC (0Hz) currents.
Suppress high and low frequency noise in received signals.
Separate the frequency components of received signals for further processing and analysis.
There are many different analog and digital filter designs, with varying implementations and transfer functions. However, the general idea of a filter is that its transfer function should attenuate the magnitude of specific frequency components of a signal, or introduce a known phase-delay to specific frequency components whilst leaving other frequency components of the signal unchanged. Typically, in the communications industry, we are mostly interested in the amplitude-frequency effects of a filter.
The ideal filter
An ideal filter multiplies the passband frequency components by 1 (i.e does not change them in any way), and attenuates the noise (i.e signal we don’t care about) in the stopband frequencies by an infinite amount. The transition from passband to stopband for an ideal filter is instantaneous. The frequency response of such a filter is shown below, on the left. The corresponding time domain impulse response of the filter is shown on the right.Of course, this description is of an idealised sinc filter, shown below, and is not practically realizable.
There are also some things that even idealized filters cannot do. A filter cannot remove common mode or differential mode disturbances and interference in the passband. That means if someone else is using the same frequencies as you are, there isn’t much that can be done to remove their interference!
Filter Response Types:
A low-pass filter allows all of the frequencies below the cut-off frequency to pass through it and it attenuates the higher frequency components of a signal. As the frequency increases so the amount of attenuation increases. Low pass filters are useful in suppressing high frequency noise, and limiting the bandwidth of analog signals.
High Pass Filters
A high-pass filter allows all of the frequencies above a certain value to pass through it and it attenuates the lower frequency components of a signal. As the frequency decreases towards zero so the amount of attenuation increases. High pass filters are useful for isolating equipment from DC currents and also from low frequency noise sources like AC power signals at 50Hz, or in the case of old telephone systems, the 20Hz ringing signal.
Constructed from the combination of a high-pass filter with a low cutoff frequency, and low-pass filter with a higher cut-off frequency. Band-pass filters find widespread use in the RF front end of telecommunications equipment, predominantly in limiting power of transmissions to a specific frequency band and also in eliminating out-of-band noise from received signals. Another increasingly common use of analog band-pass filters in telecommunications is in co-location or co-existence filters inside radio equipment or handsets. Modern handsets have a slew of different radios simultaneously operating at different frequency bands and access technologies. Ensuring that the radios can peacefully co-exist in such close quarters without degrading each others’ performance is a major design challenge!
Band-Stop / Rejection Filter
Band Stop or Rejection Filters work in exactly the opposite way to band-pass filters. They are constructed by placing a high-pass filter with a high cut off, in series with a low pass filter with a low cut-off frequency. Band Rejection filters are useful for eliminating interference on specific frequencies.
Digital & Analog Filters
Filters can be implemented for analog or digital signals. Digital Filters by comparison are implemented by digital signal processing and operate on digital information.
Analog filters can be implemented in various forms depending on the application:
Passive electronic filters consisting of Resistors, Inductors and Capacitors.
Active electronic filters that use amplifiers which are very common.
Surface Acoustic Wave filters are often used for super heterodyne receivers at the intermediate frequency in digital receivers in radios and in television sets.
Cavity filters are mechanical boxes with a specific geometry that enables high-fidelity filtering of high power microwave signals.
Analog Filters are usually constructed out of a physical circuit and operate on analog, continuous time-domain signals. Analog Filters play an extremely important role in communications, especially as an important step in signal conditioning prior to entering an analog to digital converter. Analog filters also used to have a role to play in pulse shaping for older modulation types such as spread spectrum technologies (that require a high symbol rate).
Digital filters have sever key advantages over analog filters in that they are not affected by tolerances in component values, manufacturing processes, temperature differences and aging. The performance of digital filters is also vastly superior to that of analog filters achieving much higher stop band rejection, smaller transition bands, low passband distortion and linear or even zero phase delay!
Digital filters are useful for processing almost any form of digital information! In communications equipment, digital filtering is used for conditioning digital signals prior to modulation or after being converted from analog to digital values. Digital filters find applications in pulse shaping and can also be used to remove higher frequency noise components from over sampled digital signals. This whitepaper and tutorial details an example of using oversampling and a digital filter for pulse shaping of a transmitted digital signal to enhance spectral efficiency!
Properties of Filters
Pass Band & Stop Band
The pass band is a term that collectively refers to the range of frequencies that a filter allows through it. The stop band is the term used to collectively describe the range of frequencies that are sufficiently attenuated by the filter for us to ignore. The amount of attenuation required in the stop band is called the stop band attenuation. The frequencies at which the passband stops are called the cut-off or edge frequencies. The cut-off frequencies in analog filters are widely accepted to be the frequencies at which the amplitude of the frequency response is attenuated by -3dB. Digital filters are less standardized, the attenuation level that determines the cut-off frequency is usually specified. Common values are 99%, 90%, 70,7% and 50% [reference]
The transition band refers to the range of frequencies between the pass band and stop band that are not sufficiently attenuated by the filter for us to ignore. All practical filters have a finite rate at which they can transition from the passband to the stop band. Some filter implementations are capable of achieving very high frequency roll-off, minimizing the size of the transition band. Some digital filters are capable of roll-off rates as high as -36dB/Hz!
Passband & Stop Band Ripple
Some filter implementations like the Chebyshev and Elliptical filters can introduce a “ripple” in the passband and/or the stop band of the signal, causing the signal to be distorted. The maximum tolerable Passband ripple of a filter is generally specified in the design requirements.
Phase-Delay & Phase-Response
The phase delay measures the amount by which a single frequency component is delayed when traveling through the filter. This short time delay has the effect of delaying the phase of the sinusoidal wave relative to where we were expecting it.
Quick Note: It is important to realize that phase-delay is actually dependent on units of time and is converted to an angular measurement by multiplying by the frequency. Thus, for a constant time delay through a system, as the frequency increases, so the phase-delay angle will increase too! You can prove this in your head by thinking of a wave of 1 Hz going through a system with a time delay of 0.25 seconds. We know that the phase will be shifted back by -90 degrees or π/2 radians. Imagine we had a wave of 2Hz going through the same system. The same time delay results in a phase-delay of -180 degrees or π radians!
The phase-delay as a function of frequency is shown as the phase-response of a filter. Here is an image of a phase response:
Filters can be designed to have zero phase, linear phase or non-linear phase responses. A zero phase response system is one that does not change the phase of the signal at all, which implies that it introduces no delay to the time domain signal at any frequency. A linear phase response system is one that introduces a constant delay to all frequencies, like in the thought experiment above.
Obviously, if designing a control system or time-delay sensitive system, we would prefer zero phase response (zero delay) in our signal, which would imply instantaneous measurement or control, but often we have to settle for a linear phase-response if we are dealing with real-time systems.
Systems with a non-linear phase response have a phase-response that changes with frequency! A non-linear phase response can cause distortion of the time domain signal as different frequency components will now arrive at their peak amplitude at different times relative to each other. This kind of distortion either speeds up or slows down the rate of change of a time domain signal and is referred to as ringing. Non-linear phase response is a concern in systems design where accurate replication of the time domain signal is a key requirement such as digital receivers (another reference here).
Typically, digital filters can be designed with a zero or perfectly linearphase-response and this is not an issue, but unfortunately, physically realizable filters have a much poorer performance in this regard!
Group delay is defined as the derivative of the phase-response with respect to frequency and has units of time. Group delay is also a measure of the non-linearity of the phase-response of a system. A linear phase response system will have a constant group-delay. A highly non-linear phase response will have a rapidly changing group-delay!
To think of group delay, remember the following:
Phase delays are caused by time delays in the system.
Phase-delay is calculated from time-delay by multiplying by the frequency of interest. You could say that phase-delay is measured in frequency seconds (Hz.s) or (rad.s.s-1 = rad)
If everything has the same time delay, then the phase-delay will be linear with respect to frequency, and the gradient of the line will be equal to the time-delay of the system. Negative gradient will indicate a time-delay).
If we take the derivative of phase-delay with respect to frequency, we will simply get the time delay of the system!
So, group-delay is actually just a measure of the time delay of the entire system with respect to frequency. Imagine a square pulse arrives at the input of the system. Group delay describes how each frequency component of that square pulse will be delayed through the system!
Group delay is a useful way to evaluate the normalized response of a filter design. That is the logical way to think about it. Here is a picture of a Chebyshev filter showing frequency-response and group-delay.
Quality-factor is actually not a term used specifically for filter design but for many applications in engineering and physics, including antennas and other forms of resonant systems. The Q factor of a resonator or oscillator is the ratio of its central frequency to the bandwidth over which it works. Example, we build a filter with a central frequency of 1850 Hz and a total bandwidth of 3100Hz. The Q factor of such a filter would be approximately 0.6.
All filters regardless of their type, digital or analog, will introduce some form of loss to the passband signal. Insertion loss in telecommunications refers to the loss incurred by inserting a device into the path of the signal. A good reference discussing the sources of insertion loss can be found here.
You should also be aware that the transducer you are using to create the analog signal itself also has a frequency range of operation and will also filter out frequencies outside of its own range. The picture below is the frequency response chart of a Shure SM57 microphone. As you can see from the chart it is much less sensitive to lower frequencies and much more sensitive to higher frequencies in the audible range. This means that the microphone will distort the original signal by attenuating lower frequencies below 200 Hz and amplifying frequencies above 2kHz. You can read more on microphones and their response charts here.
Loss of Information
Whenever we use a transducer to create an analog signal, or a filter to limite the bandwidth of a signal, we must always accept that we are distorting the original input and losing information. The question however is how much information is an acceptable amount to lose and do we care about the information we are losing? For instance, when capturing the sound of the human voice most of the energy is concentrated within the bands of 200 to 2000 Hz. Band limiting the signal to 3400 Hz will result in a band limited baseband signal that still allows people to communicate clearly over a digital phone. Similarly, audio destined for high quality musical playback can be band limited to 20Hz – 20kHz because we cannot hear any of the higher or lower frequencies and it makes no discernible difference to us! The same can be said of images and the color gamut that can be captured by a camera, supported by a video codec or displayed by a monitor!
Here are some great resources I found on the topic of both Analog and Digital Filters.
The difference between the baseband signal and the passband signal in communications is really quite a simple one. The baseband signal refers to any signal that has not modulated a carrier waveform.
NOTE: The use of the verb “modulated” there may made you think twice. If so, you are not alone. I always used to think that the carrier waveform was the object that acted on our baseband signal. This is the wrong way to think of it. The process of modulation is actually the process of how our baseband signal modifies the carrier waveform to create a modulated, passband signal! Thus we actually say that the baseband signal modulates the carrier waveform.
The important thing to realize is that a baseband signal can be an analog signal, a pulse code modulated signal (also actually analog really), or it can be digital information. Provided that the signal has not been used to modulate a high frequency carrier waveform, it is still considered to be baseband. Let’s go back to our model of a wireless communications system to understand where we may find baseband signals:
Properties of Baseband Signals
The term Baseband is used due to the fact the signal has a frequency component that starts close to 0 Hz relative to the carrier wave’s frequency. Baseband signals have a defined bandwidth starting at a frequency greater than or equal to 0 Hz and ending at the highest non-negligible frequency component of the signal. And now that I have said that, and it made some sense, let me immediately seem to contradict myself.
It is important to note that there is no such thing as a practical time domain signal with a finite bandwidth as I just described in the previous sentence. If you were to see a practical time-domain signal with a finite bandwidth, it would have to continue on and on and on forever! These kind of signals exist in theory only.
The truth is that every practical time-domain signal we deal with has an infinite number of frequency components because it has to be limited in time for us to capture it! You will see time-domain signals with most of their power concentrated in a certain set of frequencies and negligible power in higher or lower bands. But they will always have some power in higher frequenciy component. You can attenuate the contribution of these frequency components with minimal error, but the total bandwidth of the signal will never be truly finite.
The picture below showing the benefits of a wide-band voice codec, actually illustrates the point very nicely (even though they stop counting at only 7kHz or so which makes it less than awe-inspiring). You can see how the energy in the voice signalabove 4kHz is much less than that below 4 kHz. We can also apply the narrow-band filter of the digital telephone (shown in blue) to attenuate frequencies higher than 3400Hz and lower than 300Hz so that their contribution becomes negligible and we can minimize any errors that could be caused by sampling at 8 kHz. However, even the filtered signal (shown in blue) still does not have a truly finite bandwidth, its higher frequency components are just small enough for us to ignore.
The fact that all practical time-domain signals have infinite bandwidth has some critical implications for sampling time domain signals (read here).
Baseband signals have a specific frequency response that describes the magnitude of each frequency component. Generally the value of each frequency component can be positive (add a sinusoidal wave of a given amplitude at some frequency) or negative (subtract a sinusoidal wave of given amplitude at some frequency).
Baseband signals don’t only have components with positive-frequencies. They also have components that operate at negative-frequencies. When I said that a baseband signal “starts close to 0 Hz relative to the carrier wave’s frequency” I omitted to mention that the baseband signal’s frequency-domain representation is in fact centered around the 0 frequency mark.
If you want to understand where these negative frequencies come from, I would suggest reading more about the Fourier Transform, Euler’s identity, and the complex sinusoid. Effectively what we need to understand is that when we look at the frequency domain representation of any real numbered, time-domain signal, we will end up with negative frequency components that have the exact same values as their corresponding positive frequency components. i.e the frequency response of a real, time-domain signal is symmetrical around the 0 Hz mark.
Here are some real, time-domain, baseband signals and their frequency-domain representations.
Real vs Complex time-domain signals
Most engineering problems deal with real time-domain signals only. Real time-domain signals have real frequency components that are symmetrical around 0Hz mark and the imaginary frequency contributions on the positive and negative frequency bands cancel each other out! The above pictures show only the real frequency components.
Complex signals by comparison, (i.e time-domain signals that have both real and imaginary number components) have real and imaginary frequency components that are not symmetrical around the 0Hz mark and do not neatly cancel each other out. If you really want to read about it, go here.
Many physical objects have a frequency range over which they perform most of their work. Your ears for instance, can generally only hear frequencies between 20Hz and 20 kHz. Your own voice when speaking normally, concentrates most power in the range of 500 to 2000Hz. You can think of these as the bandwidth of their operation. Other real world sources have their own bandwidths of operation. A tuning fork has a very narrow bandwidth tuned to a single note. An organ pipe, or harp string is tuned to only release a specific frequency and its higher order harmonics. A Hi-Fi amplifier has to have a very wide operating bandwidth to allow it to amplify your music uniformly across all frequencies in the audible range. The loud-speakers in your car that turn the analog signal into sound waves are usually designed to work only on low, medium or higher frequencies in the audible range.
Your Wi-Fi modem has to operate equally well across a wide range of electromagnetic frequencies in the 2.4GHz and the 5GHz bands of the electromagnetic spectrum. The sun generates electromagnetic radiation across an enormous bandwidth that includes infra-red radiation we feel as heat, visible light, ultra-violet rays that damage our skin as well as X-Rays and Gamma rays. Thankfully, most of the energy that the sun releases is green visible light instead of the destructive X-rays and Gamma rays that fly out of other stars!
All of the objects and systems we have been talking about above, have a bandwidth of operation and a specific behavior in the frequency domain.
Analyzing objects in the frequency domain is a fundamental tool in mathematics that simplifies and provides insight into the analysis and design of electronics, control systems, communications systems, structural engineering, mechanical engineering, statistics and many other disciplines!
The Fourier Transform
Any signal that varies with time can be referred to as a time-domain signal. All time-domain signals have a corresponding frequency-domain representation. The frequency-domain description tells us about the frequency-components that make up the total energy of the signal. You can describe any continuous time-domain signal as a sum of sinusoidal waves of increasing frequency and varying amplitude. The mathematical method to do this is called the Fouriertransform. Here is a great picture that explains the Fourier transform really well in a visual way!
Forms of the Fourier Transform
I am not going to dig into the details of how the Fourier Transform works in a blog. However, it does seem sensible to understand the various forms of the Fourier Transform and the contexts in which we make use of them!
Fourier Transform – Integral
The Fourier transform as I was taught it with respect to time-domain signal f(t), has the following form:
This form of the Fourier transform is great for continuous time-domain signals that we want to analyze from a mathematical perspective, but this form does not easily lend itself to solution by digital computers.
Discrete-Time Fourier Transform
The Discrete Time Fourier Transform is applied to signals that are not continuous in time and is useful for analyzing a sampled signal. The signal x(nT) is an infinitely long series of very short pulses of continuous, varying amplitude. It should be noted that the Discrete-Time Fourier Transform has a practical weakness in that the input is an infinitely long pulse train, and the output X(Ω) is a continuous function of the frequency variable and cannot be represented exactly by digital computers.
Discrete Fourier Transform
This is the form of the Fourier transform used by digital computers. The output is a discrete function of the frequency variable and this can easily be represented by a computer!
To calculate the DFT and generate the discrete frequencies of the output samples, we have to start with a finite number of discrete-time samples of the input signal, we denote the number of samples as N.
In the Signals & Systems textbook I have, they write: “Let us choose the value of N sufficiently large, so that our set of samples adequately represents all of x[n]”. In the digital communications world, this would translate to all the sample values of the last received symbol.
We select our discrete angular frequencies as Ω = 2πk/N where k is some integer value between 0 and N-1.
In this case, where N is the number of time domain samples we have collected, and k is the current frequency sample we are calculating for, the discrete Fourier Transform is:
To figure out the frequency each discrete frequency k represents, recall:
Fast Fourier Transform (FFT)
This is the method used by modern computers and radio receivers to calculate the Discrete Fourier Transform of a received time-domain signal and retrieve the symbol information encoded inside it. The Fast Fourier transform implements an algorithm to reduce the number of computational steps in calculating the Discrete Fourier Transform.
The complexity of calculating the DFT is easily seen to rise in proportion with the square of the total number of time-domain samples, N. That means that for a 1024 sample signal, a 1024 sample DFT will result, requiring in excess of 1 million complex multiplications! The Fast Fourier Transform enables the same calculation to be carried out using many fewer complex multiplications as shown in the table below:
If you are looking for a single, comprehensive source that will take you from basic mathematics all the way to the Fourier transform, I found this to be a fantastic, free resource (click on the picture!)
What does a digital communications link actually look like?
This is a useful question to answer as it gives us a model we can continuously refer back to as we learn more about communications. Having a model means that when things get confusing later on, we can go back and see which technique, technology or innovation fits where. A simplistic model of a communications link is shown below, consisting of a source, transmitter, channel, receiver and destination.
The source is basically the signal we want to send. It could be your voice or a TV image or some music, or it could even be some digital data in the form of a frame. It could be a great many things, but for now let’s just accept that the source is some kind of generic information. We will get into the details later! Next is the transmitter responsible for (you guessed it) transmitting the signal to the other side.
The “channel” comes next. In this context we are using the word “channel” to collectively refers to the time and space that the information we are sending must travel through. The channel could be the glass tube of a piece of fiber, the twisted pairs of copper wires of an Ethernet cable or even the room you are standing in that separates you from the Wi-Fi router your phone is communicating with. It is important to realize, right now, that the channel in this context does not refer to the choice of radio frequency like when you switch the channel on the radio or TV, which is also a valid use of the word, but with a different meaning. To re-iterate, in the current context, the channel means the entire physical medium through which the transmitted signal must travel. A channel is characterized by the effects it has on the transmitted signal, which become apparent at the receiver. The receiver is what hears the transmitted signal as it has been affected by the channel and converts that signal back into an approximation of the original message to be processed by the destination. If the approximation is good enough, the destination will be able to recover the original message. If the approximation is not good enough, then the message is lost and the transmitter will have to try again.
Why Digital Communications?
I was asked this question in a job interview many years ago. My future boss stared at me as I fumbled with the answer. I blanked. I had never actually thought of it. Why the hell do we communicate using digital communications, instead of analogue? I could think of a hundred reasons, but couldn’t put my finger on one that summed the answer up in a sentence. But thankfully i have learned the answer: Noise immunity.
Digital signals lend themselves easily to being stored, and the information can be easily copied, true. But most significantly they are also significantly more immune to noise in a channel, making them easier to replicate at the receiver. Here is a picture (acquired from two separate introductory courses to digital communications, here and here) that illustrates the point extraordinarily well,
The Digital, Wireless Communications Link
Let’s go a little deeper and get to the key parts of this, the digital bit and the wireless bit! A more detailed block diagram of a digital, wireless communications link is shown below.
As we progress through this series of blog posts we will look at each of these blocks in more detail, but for now here is a brief summary of what each block does.
The Input Signal is very simply the information we want to transmit across the wireless link. This information is typically already in a digital format, although it could also be an analogue, continuous time domain signal (your voice entering the telephone perhaps?).
Source Coding is the process through which the original input signal is stored in some digital format and is compressed to reduce the storage and transmission requirements of the original information. You can think of source coding as removing redundant bits to lower (improve) the required storage space or data rate of some information. The simplest form of source coding could be analogue to digital conversion. An example of source coding of a digital signal would be the audio codecs used for a digital voice calls such as G.711 or G.729. For data, source coding would be built into the file or data you were sending, e.g. an MP3 music file that compresses digital information from raw, pulse code modulated audio of a .wav file.
Encryption exists to secure the message against interception (confidentiality), spoofing (authenticity) or from being tampered with (integrity).
Channel coding is the process of adding redundant information to the message to allow limited forward error correction and to minimize the need to resend messages that have been affected by channel induced errors. Effectively, channel coding adds a dimension of reliability to communications even in the presence of interference and noise. It would be accurate to state here that modern digital wireless communications technologies rely very heavily on channel coding techniques.
Modulation is the process of mapping the information in the coded information stream onto a carrier signal to create a digitally modulated waveform. This can be done in many ways, but typically involves manipulating the amplitude, frequency or phase of the carrier wave in a predetermined, finite number of ways. Each possible manipulation of the carrier wave is referred to as a symbol and carries a specific sequence of binary information.
The transmitter’s role is to further process and amplify the digitally modulated signal before it is fed into the antenna. The antenna then radiates the signal into the wireless channel which as we have mentioned is actually the physical space and time through which the signal must travel.
The antenna on the receiver is responsible for “hearing” and passing the electromagnetic signal into the receiver. The receiver amplifies the (typically very small) received signal and passes it to the demodulator so that it can be converted from the detected complex waveform back into a series of 1’s and 0’s.
The channel decoder then takes chunks of the received information and uses the redundant information (from the channel coder) to perform forward error correction on the received digital information, recovering the original encrypted message. The encrypted message is de-crypted before being passed to the source decoder which recovers the original information!
One of the things that I have not shown in the diagram above is the synchronization necessary between the transmitter and receiver. It is imperative that the receiver is synchronized to the same frequency and phase as the transmitter. There must also be synchronization of the symbols and of the frames sent so that data can be reliably reproduced on the other side!
In the beginning of 2017, I made the decision to attempt the IEEE Wireless Communication Engineering Technologies (WCET) Certification. If you have ever read this blog, followed me on twitter or met me in the last 7 years you will know that I have had my head buried pretty deeply in Wi-Fi as I worked towards my CWNE certification. But, it also makes sense to keep aware of the other technologies available.
Today we are seeing the emergence of many technologies like LTE-U, LAA that implement LTE on the 5GHz unlicensed bands, LTE-A that will bring the precursors of 5G New Radio to mobile networks, and CBRS & Open G that will allow deployment of LTE on coordinated spectrum in the 3.5GHz band. In the IoT space we are seeing Sigfox and LoRa cement their positions as preferred IoT protocols in the unlicensed 900MHz band with incredible range and battery life but we are also beginning to see projects using NB-IoT from some mobile operators to enable the internet of things.
Regardless of your opinion on the various emerging protocols above (I have many), it is certainly an exciting time to be alive in the wireless communications space!
Back to the point.
The WCET certification is aimed at the wireless professional with a bachelors degree and several years of work experience in the wireless communications industry. It covers a huge swathe of knowledge including Fundamental Mathematical Knowledge, RF Engineering, Propagation, Antennas, Signal Processing, Wireless Access Technologies, Network & Service Architectures, Network Management & Security, Facilities & Infrastructure, Industry Agreements, and finally Standards, Policies & Regulations.
This information can quite easily be covered in a series of heavy textbooks. This will be my attempt to share some of my study notes as I move through the material!
So I will admit I am writing this in a fit of pique. Today Bloomberg published this preposterous piece of marketing flimflam claiming that LTE-U has the potential to replace or drown out Wi-Fi. I am not sure if the consultants quoted in that article are drinking kool-aid together, but I certainly feel like they have missed some key points.
So I am going to ask one very simple question:
What does LTE-U enable that Wi-Fi doesn’t?
Answer: The ability to seamlessly charge a customer for its use, without any knowledge, or intervention required by the customer.
OK, so that’s a pretty big carrot for the mobile operators! I mean imagine. They could give a customer an unlimited data plan, the subscriber can move anywhere around the mobile network, indoors and outdoors. Mobile operators finally get to remove the need to deploy Wi-Fi and the motivation for subscribers to use it in the first place. LTE-U keeps them on the mobile network and they can do it cheaply with the unlicensed 5GHz spectrum!
Holy crap! Wi-Fi is dead yo! We comin for you Wi-Fi! Cry ‘havoc!’ and let slip the dogs of war!
Ahhh. Indeed. This is exactly the kind of blinkered thinking demonstrated by mobile operators, the 3GPP and anyone involved in mobile telecommunications that causes me to sneer.
Let me ask another question:
What does Wi-Fi provide that LTE-U doesn’t?
I hope you’re ready. School is commencing.
Local Area Networking
Contrary to the opinion of those who move exclusively in mobile operator circles, Wi-Fi networks are actually not only built to handle Internet bound traffic from hotspot users and subscribers. In fact that is likely an incidental service that resulted from what they were actually developed to provide. The primary purpose of a Wireless LAN is to allow mobility over a venue’s own local area network. Services enabled by Wi-Fi or WLANs include:
Corporate / Operational Communications
Security Systems / Services
Internet of Things Applications
Voice over IP
Touch to Talk services
Real Time Location Services (Security Personnel, Doctors, Nurses, Asset Tracking…)
Digital Advertising Boards
Point of Sale Terminals
Back Office Connectivity for Stores / Shop Fronts
Location Based Services / Location Tracking (location analytics, more asset tracking etc)
Public Internet Access (The ONE thing LTE-U actually currently enables)
Targeted Digital Advertising
… and any other service you recently used on or integrated with a Wireless LAN.
LAN connectivity is a fundamental and critical function that LTE-U simply cannot provide in its current form. Wi-Fi allows you to setup a radio and plug it directly into your Enterprise LAN. LTE-U can’t do that. LTE-U traffic must go via the mobile operators’ Packet Gateways which means all traffic gets hoovered up, sent into some operator’s core network and is then popped out on a public IP in some APN based on who your sim card says you are. Good luck getting back to the LAN with a reasonable latency. Also, please explain the complicated architecture, SLAs, agreements, firewall rules / VPN tunnels and identity management that a mobile operator would have to implement to get a heterogeneous group of SIM authenticated users back into a venue’s LAN from multiple APNs.
LTE-U is coming and it is indifferent to your Enterprise LAN, distinctly unfriendly to your Wireless LAN and it could arguably interfere with the very wireless networks that most venues depend upon to operate on a day to day basis. Which brings me to my next point.
Value to the Venue/Business Owner
Time for another question…
If you were a venue owner, with a WLAN that you used for a mixture of corporate, operational and public access use cases, would you be happy about LTE-U being installed in your building?
What value does LTE-U actually add to a venue? Sure people will be walking around with smiles on their faces as they stare obliviously at their phones. But what do I get out of allowing LTE-U in my Office, School, University, Warehouse, Logistics Center, Hospital, Shipping Port, Airport, Care Facility, Residence, Stadium, Convention Center, Mall, Coffee Shop or Train Station? I’ll probably get a ticked off IT engineer and a slew of complaints from all my tenants who are currently using Wi-Fi for business related functions. I have no doubt, LTE-U will find some use in public venues. But in my office? Where it effectively DOS attacks my WLAN with radio interference on a duty cycle determined by the mobile operator?
When LTE-U is allowed into a venue, the venue owner will ultimately have to accept some form of performance degradation on the Local Area Network, for which they could charge a sizeable rental fee.
LTE-U deployments are likely to be hobbled by high rental costs and restrictions on the density of their deployments, in an effort to mitigate interference with existing WLANs in the building. It also means that operators will likely have to share LTE-U installations using Neutral Host architectures. Limitations on deployment density and spectrum usage enforced by the venue owner and tenants will cause LTE-U deployments to suffer congestion just like the outdoor macro network does. Don’t like it? The venue owner has every right to show you the door. You’re not hobnobbing it at MWC anymore Dorothy. Site acquisition is hard when you’re pissing people off.
But these are the latest, greatest phones. And it’s just the phones. There is no major existing use case outside of it. If you want your new technology to wipe out Wi-Fi, you need to be in every phone, every tablet, every laptop, every mini PC, every gosh darned thermostat, camera, doorbell, pet cam, smart plug, tv and a bazillion other things that didn’t come up on Google’s suggested search items.
Connect Devices without Sim Cards
At this point, if you don’t have a sim card, you can’t connect to LTE-U. Market researchers IDC expect cellular connected tablet devices in 2019 will still account for less than half of all tablets. Granted many consumers will simply tether their devices, but that would ultimately load the LTE-U cell to the point where consumers will want to cut back over to a faster Wi-Fi network on a different channel. Multefire is one technology which could remove the need for a sim card, but nobody seems to be rolling that out just yet and device support is still a problem.
Low Cost Wireless Access
In the article above there is a claim about the cost of LTE-U small cells. The estimate is that deploying approximately 24 LTE-U radios is comparable to the cost of deploying 80 Wi-Fi access points. Which Access Points? High End Enterprise APs that are worth ±$1500 each? Or low end SMB entry level APs that sell for around $150 each? There is a big range in price points for Wi-Fi Access Points and that is a great thing! It means that just about anyone can find something that will work and fit their budget. There is no such range of pricing and features on LTE-U today. I am also certain nobody would be investing in this technology if the starting price of the first LTE-U AP to market was only $450 (wink).
Cheap International Roaming
This is hardly a technical constraint. but still a valid one when considering the use cases of LTE-U. If you want to hop onto an operator’s LTE-U network overseas, sure go right ahead, so long as your home operator has a roaming agreement that doesn’t utterly annihilate your bank balance with fees as high as $10.00 per Megabyte. Quite seriously, where I come from, if you don’t activate a special “travel saver” option for about $3 per day, they hit your bank account with the Hammer of Thor. Who on earth connects to a mobile operator overseas with data roaming enabled when you know there is free Wi-Fi somewhere?
Obviously, the solution to this particular problem is a simple business decision (har har), just make cheaper roaming agreements. Some of you reading this may not have this problem. But really if the operators wanted to do this internationally, they would have done it already.
One of the most overlooked advantages of using a Wi-Fi network anywhere is that Venue / Business Owners are free to build an almost infinitely customizable network for all of their internal IT needs and public access services. Business owners can choose from a plethora of architectures, vendors and solutions providers to build something that meets their exact requirements.
The only initiative that could enable this is the Multefire Alliance who have only just recently released their 1.0 specification. They have some a reasonably impressive member list, but I’ve seen groups with impressive member lists before. Importantly there are other technologies out there like Ruckus Wireless’ OpenG that uses the CBRS band for Neutral Host Small Cells and opens up new spectrum! Either way, LTE-U initiatives have a lot of ground to make up and a big ecosystem to develop within two years before 802.11ax comes wandering round the corner.
Thus far with only Ericsson and Nokia having approved equipment in this space I cannot see how LTE-U will deliver a remotely attractive enterprise use case to snuff out the venerable Fi.
To think that LTE-U could somehow match Wi-Fi’s depth and breadth of applications for the enterprise in only a few short years is a pipe dream. Mobile Operators generally have no business interests in common with business / venue owners and typically want as little to do with their enterprise business needs as possible. You’re never going to be happy with the one size fits all approach that a mobile operator will take to solving what they see as their biggest problem.
The most interesting technology in the LTE-U space right now is actually MulteFire which really could enable something like LTE-U or LAA (which doesn’t affect Wi-Fi as badly) for enterprise use cases. But there is little evidence right now to demonstrate that this technology will truly get off the ground before the marginal performance gains it delivers over Wi-Fi are matched by newer generations of Wi-Fi equipment. Until that point, LTE-U and LAA are going to be relegated to the Service Provider segment which by all accounts is only a fraction of the overall WLAN landscape and operators trying to install it will have an uphill battle with venues who already have a WLAN that delivers business value.
That’s it, Rant Over.
It is also worth mentioning that Dean Bubley did a great job of breaking the same topic down here.
Sometimes, I write blog posts that are more for my own benefit than for the reader. This post is supposed to benefit both parties. I am planning on ramping up the amount of content I write and organizing my posts and related articles will become a critical task. This post documents how I should categorize and tag posts on this site and will give you an idea of how I organize my content. It will also serve as a reference for me, so I can apply a standard system that keeps everything tidy.
Today, this is a blog about Wireless Technologies and my experiences with them. I have decided to categorize articles based on radio technology type.
Top Level Categories: (Example)
Informational – posts that supply information about the site itself (like this one)
Personal Area Networks
Low Power WAN
Mobile / Cellular
Each post must be assigned at least one category based on the radio (or other) technology being discussed. The number of categories should be kept to a minimum, only covering the specific technology in use. I am not going to be able to generate enough content at first, and so I will not use sub-categories (yet). I consider that to be a slippery slope to just having too many categories which is as bad as having none.
I am going to implement some other categories that cut horizontally across the Radio technology types. For instance in some posts i have been talking about how to configure an ODROID as a WLAN Tool, a useful category here is Linux as that will gather all posts that talk about Linux knowledge too. I will use these categories sparingly to prevent category bloat.
Tags will work well to further define the posts’ content. Often times tags may be re-used between categories keeping the structure more flat.
Tags will be used to flesh out the description of the post from the top level Category. The following tag structure should be used:
Industry Trends: Big Data, IoT, Cloud, Wireless, Mobility, Security, Software Defined Networking, Network Function Virtualization… This is really so non-engineers and people who read NetworkWorld can find my content more easily…)
Technology: Wireless LAN, PAN, WAN, Low Power WAN…
OK, so the idea here is that you get a quick and good idea of what the discussion is about from just the tags (thats the point of tags).
So lets look at “Using the ODROID C2 as a WLAN Testing Tool” Series of posts and apply our logic as a test. Tags could include: Mobility, Security, Wireless LAN, 802.11, Linux, DietPi, ODROID, Tutorial, WLAN Tools
That’s a pretty awesome set of tags. At least i think it is.
I may optimize this in the future, but hopefully this structure is capable of lasting some time as editing all your posts and re-tagging them is a pain!
In my time as a Wi-Fi professional I have come across several customers who have attempted to formulate what can only be described as “Promethean” IoT (Internet of Things) solutions . These customers typically have a requirement to measure some kind of data and transmit it back to a server somewhere where it can be processed. The data gets measured infrequently, and it is small. Perhaps a heartbeat, a status message, or a simple numeric value sent several times an hour. The measurement locations are typically quite far apart or dispersed over a large area.
The customers had often looked at using GSM/UMTS/CDMA networks with sim-cards in the hardware, but getting a contract with a mobile operator ain’t cheap, the hardware gets expensive and it’s a recurring monthly cost! Plus people break into your gear and steal your sim cards… or whatever other useful hardware might be in there!
After this experience, the customers would often come to me to discuss the possibility of using Wi-Fi as a potential solution to their problems. To them it looked like the ideal solution! This was a license free radio technology that got them away from the exorbitant monthly prices of mobile contracts, and besides, Wi-Fi radios are cheap, right?! Looks like a possibility to me!
One such customer had gone out to tender for monitoring of an electrical grid. Some spoke to me about smart electricity and water metering. Others spoke about automation for agricultural applications. Others wanted smart parking meters, smart street lights and air quality measurements. Another even spoke to me about monitoring the location of livestock.
It was fun to engage with these customers to learn about what they wanted to do and to come up with creative solutions to their problems. Sadly, there are often just too many problems associated with using Wi-Fi for these kinds of use cases. The biggest issue (as with many things of a technical nature) ultimately boiled down to cost. It simply isn’t cost effective to deploy a Wi-Fi network over an enormous area to gather small amounts of data. The other issues include logistics, infrastructure, power, maintenance, security and internet or network connectivity. All of these things would drive up the total cost and complexity of maintaining the solution.
Wi-Fi connected sensors themselves are usually power hungry, requiring a mains power or a solar panel, inverter, regulator, charging circuits and deep cycle batteries. A decent regulator circuit alone can cost in excess of US $30 if you buy it in volume. The devices also require a technical resource to configure and install them and make sure they associate to the network. The total cost of deploying your “smart sensors” can quickly dwarf the cost of the access network which itself already costs into the hundreds of thousands of dollars for an area as small as 10 square kilometres with contiguous coverage.
You want to track your livestock? Yes! Could that make your operation more effective and help prevent livestock theft? Yes! Is it worth building a network across several thousand square kilometres, whose scale would rival that of a small ISP and cost you a small fortune to run? No, it would bankrupt you almost instantly.
We always got to this point. Don’t get me wrong, Wi-Fi is a fantastic technology, I have built my career on its myriad applications and it is a very useful ally when building an IoT solution. But even applications of the almighty Wi-Fi have their (technical and financial) limits!
What the world needed was a set of protocols and radio technologies that enabled the use cases described above, in a simple, secure, effective way. We needed something that could cover large rural areas (± 7500 km2) using a single tower, that could gather data from thousands or even millions of sensors in a dense urban environment, both indoors and outdoors. And it needed to do this without making much of a dent in the bank balance.
The protocols and technologies that address this growing need today are collectively referred to as LPWAN (Low Power WAN) and its range of applications are growing extraordinarily fast!
In the previous posts in this series I took a look at how I installed and configured DietPi on my ODROID C2. I also went through the settings and some software packages that I wanted to install on the first boot. I should re-iterate here that one of the goals of this series is not to blandly show the reader how to do things, but also to try and learn more about how a machine like this fits together. So as I go along you may see me point out some things that have more to do with Linux or DietPi or other topics. They may also seem obvious to you or not worth explicit mention. I am doing this is in the spirit of sharing the totality what I learn along the way, so that you the reader may benefit. I am also doing it so I can come back and read it later when I forget… (it happens more often than not!)
Right so, at this stage, you have booted your ODROID or other SBC (Single Board Computer) for the first time, you have logged in and you are now at the command prompt. I am assuming you weren’t adventurous enough to add a desktop and you are simply booting into the standard command line. You may still have the ODROID connected to your screen and keyboard, and that’s fine too. Go ahead and login (if you haven’t already) and let’s take a look around.
You should be at the User@HostName~:# prompt. Let’s have a look at our present working directory and a few other things…
Ok, so our home directory is /root. Let’s go up to the top of the directory structure…
root@Droid-01:~# cd /
root@Droid-01:/# ls -a
. .. bin boot dev DietPi etc lib lost+found mnt opt proc root run sbin srv sys tmp usr var
Let’s go back to the home folder and have a look inside there…
root@Droid-01:/# root@Droid-01:~# ls -al
drwxr-xr-x 4 root root 1024 Feb 25 17:29 .
drwxr-xr-x 20 root root 1024 Feb 25 16:58 ..
-rw------- 1 root root 212 Mar 3 23:24 .bash_history
-rw-r--r-- 1 root root 3526 Feb 25 17:29 .bashrc
drwxr-xr-x 3 root root 1024 Feb 25 17:29 .config
drwxr-xr-x 2 root root 1024 Feb 25 17:29 .local
-rw-r--r-- 1 root root 140 Feb 25 17:29 .profile
Neat OK, so we have a 1000 mile view of where we are and what we are dealing with (actually, at this point we really have no idea!)
One of the cool things that DietPi OS comes with is a set of menu based tools for configuring your SBC and for installing optimized versions of software. Let’s go and find out where those are…
root@Droid-01:~# cd /
bin boot dev DietPi etc lib lost+found mnt opt proc root run sbin srv sys tmp usr var
root@Droid-01:/# cd DietPi
boot.ini config.txt dietpi dietpi.txt
root@Droid-01:/DietPi# cd dietpi
boot dietpi-backup dietpi-cleaner dietpi-cpuinfo dietpi-drive_manager dietpi-letsencrypt dietpi-obtain_hw_model dietpi-ramlog dietpi-survey finalise misc
conf dietpi-banner dietpi-cloudshell dietpi-cpu_set dietpi-funtime dietpi-logclear dietpi-process_tool dietpi-services dietpi-sync func
dietpi-autostart dietpi-bugreport dietpi-config dietpi-cron dietpi-launcher dietpi-morsecode dietpi-ramdisk dietpi-software dietpi-update login
The three main applications you will use are:
dietpi-launcher: A full menu for optimized software selection, HW config, autostart settings, cron jobs, management of external drives and updating dietpi
dietpi-software: Allows you to run configuration and select software for dietpi to install. Also available in the dietpi-launcher menu.
dietpi-config: This allows hardware configuration changes and optimizations. Also available in dietpi-launcher and dietpi-software menus.
Go ahead and try each of them, you will realize you’ve already used them to install other software during the first boot!
After the first boot and configuration, you should already have some network tools installed. You should be able to use iftop, iptraf, iperf, mtr, nload and tcpdump.
You should also have access to some useful text editors, I only have Vim and Vim-Tiny installed (I don’t need both, I was just being greedy!)
If you want to check out what other executable programs are included in your DietPi system, use cd /bin to open the /bin directory and use the ls command to have a look what’s there.
root@Droid-01:~# cd /bin
root@Droid-01:/bin# ls -a
. bzip2recover dash fbset ip login mount ntfscat pidof setfacl systemd-ask-password udevadm zdiff
.. bzless date fgconsole journalctl loginctl mountpoint ntfscluster ping setfont systemd-escape ulockmgr_server zegrep
bash bzmore dd fgrep kbd_mode lowntfs-3g mt ntfscmp ping6 setupcon systemd-inhibit umount zfgrep
bunzip2 cat df findmnt kill ls mt-gnu ntfsfallocate ps sh systemd-machine-id-setup uname zforce
bzcat chacl dir fuser kmod lsblk mv ntfsfix pwd sh.distrib systemd-notify uncompress zgrep
bzcmp chgrp dmesg fusermount less lsmod nano ntfsinfo rbash sleep systemd-tmpfiles unicode_start zless
bzdiff chmod dnsdomainname getfacl lessecho machinectl netstat ntfsls readlink ss systemd-tty-ask-password-agent vdir zmore
bzegrep chown domainname grep lessfile mkdir nisdomainname ntfsmove rm stty tailf wdctl znew
bzexe chvt dumpkeys gunzip lesskey mknod ntfs-3g ntfstruncate rmdir su tar which
bzfgrep con2fbmap echo gzexe lesspipe mktemp ntfs-3g.probe ntfswipe rnano sync tempfile ypdomainname
bzgrep cp egrep gzip ln modeline2fb ntfs-3g.secaudit open run-parts systemctl touch zcat
bzip2 cpio false hostname loadkeys more ntfs-3g.usermap openvt sed systemd true zcmp
Of course, you can learn about these commands all by simply typing their name and –help at the end!
Installing New Software
The DietPi OS we are using is a stripped down variant of Debian OS and so it uses the apt-get command line interface for installing and managing software. If you want to learn more about apt-get, simply type apt-get –help into your command line on your SBC. We are going to be using apt-get to install some useful software packages on the ODROID
At this point in my installation, I want to start being able to connect to other types of networks and I want an easy way of configuring them. Linux typically uses the wpa_supplicant program to act as a network connection controller / manager and it is a very powerful tool. But there is a catch. The wpa_supplicant software comes with two front end programs to allow you to manage your network connections. The first, wpa_gui offers a graphical user interface that I assume should be eas(ier) to use, but I cannot test as it is not included in DietPi and besides, I am using the command line user interface exclusively at this point anyway. The second front end program wpa_cli offers a command line user interface. Don’t get me wrong, wpa_cli does have a help file, but learning all those commands right now seems a little ambitious. If you want to see what I mean try:
root@Droid-01:~# wpa_cli --help | less
The “less” command is a great tool for showing terminal output only one page at time!
Back to the point: easily changing my network settings with a wide array of choices and settings. ENTER wicd and wicd-curses! The key part about wicd is that it supports both a fully featured console interface as well as a graphical user interface and it should work across almost all Linux distributions! So let’s get this installed, the commands you will want to run are below!
DING! All done, so let’s go and have a look shall we? Let’s open the console interface:
You will see something like this.
Notes: When you enter the prefs menu, you will need to use something for page up / page down to tab between high level menus, best to Google that for your keyboard layout! I have also found that if you are accessing your SBC remotely via SSH, and you open up wicd-curses and start playing with the network connections you are quite likely to interrupt the ssh session. This is seems like a good tool to use with a display, keyboard and mouse… (cue my disappointed face!)
With that limitation in mind, feel free to wander around and use the tool to scan for networks (use the Refresh function), you can also set various preferences and configurations for different connections. Enjoy exploring! Interestingly enough in this setup, ODROID-1 is set to use WPA-Personal / AES and ROBROBSTATION is set to use WPA2-Personal / AES, but wicd reports both as WPA2 because they both use AES. You will also notice that wicd also gives you the ability to select the bssid that you want to connect to! That is VERY useful indeed.