August 1, 2016

The “WA” in LPWA is Wide Area… and Pizza

I would like to address an aspect of Low-Power, Wide-Area (LPWA) networking that is extremely important yet receives little attention:  capacity.   Capacity is a measure of how many devices a piece of networking infrastructure may support and/or how rich of a data model a network can provide to those devices.

Below are LPWA technology comparisons that either ignore capacity entirely, or use unjustified vendor claims that are often simply based on the amount of addressing space supported.

http://frankrayal.com/2016/06/11/comparative-look-at-lpwa-performance/

http://lora.multitech.com/white-papers/lpwa_white_paper_86003000_2016-06-13.pdf

http://www.eejournal.com/archives/articles/20150907-lpwa/

http://www.eetimes.com/document.asp?doc_id=1328365&page_number=3

These comparisons spend much of their attention on supported link data rates, thus missing the point fundamentally on what a wide area network (WAN) is supposed to do.   A WAN in general is not about delivering data rates to a single endpoint, rather, it is fundamentally about providing high-capacity connectivity to a large number of endpoints.

Here’s an analogy. When you order a pizza, would you be comfortable understanding a range of size of individual pizza slices you might receive (analogous to range of data rates), but giving the pizza parlor full control of the number of slices that they will deliver as well as the sizes of those slices (as long as they are within the stated pizza slice range)?   If this pizza slice scenario makes perfect sense, then these “data rate centric” frameworks are for you.  But, if you’re like me, and prefer ordering a pizza based on the size of the pizza, then you would likely be more comfortable with a different measure – one that examines the “capacity” of the system.  Just as the size of a pizza is indicative of the number of hungry family members that can be satiated (or often over-satiated), capacity is a measure of how many IoT devices may be served by a network based on their data needs.  We should probably have colored the circles more pizza-colored for clarity.  As you can imagine, insufficient capacity means not being able to build a profitable network…or analogously, whether that pizza parlor can stay in business.

If you would like to understand the details behind these calculations,  please check out Appendix A through C of How RPMA Works:  The Making of RPMA.

On a side-note, I was surprised to see RPMA represented as inferior coverage in this framework. We work through the link budget calculations in Section 2.3 of the same document (How RPMA Works:  The Making of RPMA) and make the point that RPMA has a massive coverage advantage.

Capacity Comparison

July 22, 2016

The Economics of Receiver Sensitivity and Spectral Efficiency

We have asserted textbook style categories for Low-Power, Wide-Area (LPWA) connectivity Blog 1: Categories of LPWA Modulation Schemes.  Then we went back to basics to get some framework and vocabulary in place to discuss these various schemes in Blog 2: Back to Basics – The Shannon Hartley Theorem, where we introduced the critical concepts of spectral efficiency (η), Eb/No, and how a point of diminishing returns can be reached in the quest to reach the Shannon Limit.  In Blog 3:  Chirp Spread Spectrum (CSS): The Jell-O of Non-Coherent M-ary Modulation, I confirmed that CSS can be treated more generally as Non-Coherent M-ary Modulation (NC-MM).

And then in Blog 4: “Spreading” – A Shannon-Hartley Loophole?, we discussed in more detail technologies that use links with very low spectral efficiency (η << 0.1) and the capacity implications with three concrete examples:  the Sigfox® technology, LoRa™, and Ingenu’s RPMA®.

In this blog, we will set the stage with a description of a public network business model including how the fundamentals of coverage and capacity fit into building that business.  Given that framework, I will then go onto discuss how the three technologies highlighted below (the Sigfox technology, LoRa, and RPMA) compare on the economics of coverage (building the network), capacity (monetizing the network), and capacity scalability or cell splitting (growing a successful network.)

Category Local Area Network Wide Area Network
Ultra Narrow Band

(UNB)

 Sigfox

Telensa

N-Wave

WaveIOT
Non-Coherent M-ary Modulation (NC-MM) Bluetooth

802.11b

LoRa (CSS)

Sensus (7-FSK)

 GSM/GPRS

EC-GSM

 
Direct Sequence Spread Spectrum (DSSS) 802.11

Zigbee

 

 W-CDMA

RPMA
Orthogonal Frequency Division Multiplexing

(OFDM)

 802.11a/g LTE

WiMax

NB-IOT

 

Setting the Stage: The Public Network Business

The public network business requires two general components: a carrier, and applications that may benefit from that network.  Let’s define these roles and look at a public LPWA network from these points of view:

  • The carrier owns and operates the LPWA network. The carrier invests in building this network, and charges applications for the use of this network.  The successful carrier business profits by the revenue (connectivity fees from the applications) exceeding the expenses of running the LPWA network (tower rental, backhaul expenses, construction costs, human resources, etc.)
  • Applications are benefitted by the connectivity that the LPWA network provides. For an application to participate in an LPWA network, there must be a positive return-on-investment (ROI) of this connectivity.  In other words, the value provided by the LPWA connectivity must exceed the connectivity fees paid to the carrier.    Moreover, if given a choice, the application will want to maximize their ROI by selecting the LPWA network that minimizes their connectivity fees.

The LPWA business begins with the carrier.   Typically, the carrier must invest in building the network prior to revenue being realized.  Let’s introduce a few key terms to bridge the LPWA economics to the LPWA technology:

  • Coverage – a metric of how much network infrastructure is required to reliably cover a region. Most of the expenses associated with running a carrier are proportional to the amount of network infrastructure required (e.g. tower rental, backhaul expenses, construction costs) particularly as the geographic extent of the network becomes large.  The number of square miles (or square kilometers) covered, on average, by a piece of network infrastructure (e.g. tower) represents the initial investment a carrier must make to build this network.
  • Capacity – a metric of how many devices, on average, may be supported by a piece of network infrastructure. The capacity metric is relevant to the revenue side of a successful carrier business.  The amount of revenue per tower to the carrier will typically be directly proportional to the number of endpoints served by that tower.

From a carrier’s point of view, investment in coverage is a slight “dip in the road” in terms of outward cash-flow; capacity represents that “road up the mountain” in turns of how profitable that network may be based on number of devices supported.

Coverage

To build a network economically means that each piece of infrastructure must cover large amounts of area with high probability.  This means that the range of the link must be as high as possible, which, in turn, means the receiver sensitivity must be a good as possible.

In the figure below, we show the amount of typical reliable coverage that can be expected with the three approaches based on the analysis in Section 2.3 of How RPMA Works:  The Making of RPMA.  Naturally, the cost of a carrier covering a large region is far lower if each tower is capable of covering the large area shown for RPMA.

coverageimageblog5

Capacity

It is important for a technology to allow for connection of a sufficient number of endpoints per piece of networking equipment to make long-term economic sense. There are costs to delivering LPWA connectivity including the infrastructure cost, the deployment cost, and any maintenance costs.  These costs need to be shared among a tremendous number of devices such that each device’s share of the burden is very low. Keep in mind, these are typically low-value devices that can only bear minimal networking expense. If you take a look at the figure below, you will see that RPMA supports a factor of 60x to 1300x more data per piece of networking infrastructure (which equate to more 60x to 1300x more devices) relative to LoRa (exactly where in that range depends whether we are talking uplink/downlink and the particular regulatory domain). The Sigfox technology numbers are similar. Not surprisingly, the 60x to 1300x reduction in the per device connectivity cost is the difference between making the economics of the network work at all, and being able to make the economics of the network work easily.

The Sigfox technology is argued to serve only very low-bandwidth devices and thus, capacity is not an important attribute for the low-end devices they serve.  There are two main problems with this argument:

  • Low-usage devices tend to justify only low connectivity costs. If the Sigfox technology is constrained only to low-usage devices, the carrier requires more of these devices to build a business.  Whether the endpoint distribution is fewer high-usage devices, or more numerous low-usage devices, capacity is being consumed, and as such, it is an important figure of merit to understand if there is any economic value to be split between carrier and application.
  • Even if there is some minuscule amount of economic value to be split between application and a carrier employing Sigfox technology, an RPMA carrier will always be able to undercut the connectivity costs due to the tremendously unfair capacity advantage. An RPMA carrier will be in a position to offer more link capability at a small fraction of the cost, and that RPMA carrier will remain massively profitable.

Note the number of devices supported on average per piece of network infrastructure as shown below and imagine the amount of revenue per endpoint for the Sigfox technology and LoRa.  In our opinion, these numbers will not support a profitable carrier business model if you assume reasonable connectivity cost per endpoint as revenue.

An additional headwind LoRa in particular faces is the lack of selectivity (discussed in more depth in Section 2.10 of How RPMA Works:  The Making of RPMA.) where endpoints that are deployed on private LoRa networks actually consume capacity on overlapping public networks – far more capacity, as a matter of fact, than had they actually been connected to the public LoRa network.

RPMA's capacity is 10s to 100s of times more than other LPWA technologies.

Capacity Scaling (Cell Splitting)

 What happens when a tower is at capacity based on the number of devices shown above?  As a carrier, you would probably like to add more tower locations to continue offering robust connectivity.  The cellular industry has a term for this called “cell splitting” and it can be done effectively.  However, not all technologies allow for offloading capacity by adding towers.  All the technologies in the LAN category share this aspect including the Sigfox technology and LoRa.  Due to lack of support for transmit power control (among other things), once a critical density of endpoints is reached, the system simply ceases to work robustly.

By contrast, RPMA has been built from the ground up as an LPWAN solution. It was not easy or fast to do this. It took considerable investment because a new technology had to be developed from scratch, along with a whole new type of chip to inexpensively implement the new technology with minimal cost and power.  The most important thing now is that RPMA is done, commercialized, and networks and endpoints are rapidly being deployed! We believed (and we still strongly believe) that with LPWA device counts in the 10s of billions or more, an investment in a purpose-built technology was well worth it.

In future blogs, we will explore additional topics such as sunsetting, NB-IOT looming technical issues, standardization, RPMA commercial progress, and development of RPMA technology.  If you would like an advanced peek into many of these topics please take a look at the document How RPMA Works:  The Making of RPMA.

July 22, 2016

“Spreading” – A Shannon-Hartley Loophole?

We have asserted textbook style categories for Low-Power, Wide-Area (LPWA) connectivity Blog 1: Categories of LPWA Modulation Schemes.  Then we went back to basics to get some framework and vocabulary in place to discuss these various schemes in Blog 2: Back to Basics – The Shannon-Hartley Theorem, where we introduced the critical concepts of spectral efficiency (η), Eb/No, and how a point of diminishing returns can be reached in the quest to reach the Shannon Limit.  In Blog 3: Chirp Spread Spectrum (CSS): The Jell-O of Non-Coherent M-ary Modulation, I confirmed that CSS can be treated more generally as Non Coherent M-ary Modulation (NC-MM).

So, what is “spreading”?  From the most generic perspective, it is increasing receive sensitivity (thus, a longer range and more robust link) by reducing the data rate and keeping the same channel bandwidth.  That underlined part is very important and represents a potential looming problem:  the link data rate is becoming a trickle, and yet that link takes the same amount of valuable spectrum.  The result is that the capacity could become vanishingly small.  But the capacity does not have to – and therein lays the loophole.

Let’s use what we’ve learned to determine the capacity of a few well-known Low-Power, Wide-Area (LPWA) “spreading-based” connectivity technologies [LoRa™ (CSS) and RPMA®] highlighted in the table below and additionally, a well-marketed Ultra-Narrow Band (UNB) approach used as the Sigfox™ technology for comparison purposes.

Category Local Area Network Wide Area Network
Ultra Narrow Band

(UNB)

 Sigfox

Telensa

N-Wave

WaveIOT
Non-Coherent M-ary Modulation (NC-MM) Bluetooth

802.11b

LoRa (CSS)

Sensus (7-FSK)

 GSM/GPRS

EC-GSM

 
Direct Sequence Spread Spectrum (DSSS) 802.11

Zigbee

 

 W-CDMA

RPMA
Orthogonal Frequency Division Multiplexing

(OFDM)

 802.11a/g LTE

WiMax

NB-IOT

Recall from Blog 3: Chirp Spread Spectrum (CSS): The Jell-O of Non-Coherent M-ary Modulation, we introduced the calculation of the individual link spectral efficiency (η) for LoRa.  Let’s take that information and insert the Sigfox technology and RPMA for comparison.  The Sigfox technology looks great and RPMA looks worse than even the meager spectral efficiency of LoRa.  Uh oh…Looks like RPMA is off to a rough start.

But wait, this analysis is far from complete in terms of a capacity calculation.  RPMA is a WAN that allows for simultaneous transmission of overlapping waveforms.  That is the loophole.  Recall that the Shannon-Hartley Theorem is applicable only to a single link.

The result is that for RPMA, there is no capacity benefit for an individual link being extremely spectrally inefficient.   RPMA can use extreme variable spreading (or processing gain), result in an extremely sensitive modem (-145 dBm is best in the world), and not be hit with any capacity penalty.

RPMA can demodulate 1,000 signals simultaneously for a couple of fundamental reasons:

  1. Transmit power control is essential. Signals must arrive at the access point at very similar power levels for this multiple access capability to exist.
  2. There must be a mechanism such that the received waveforms can be individually resolved. Any individual waveform may not be correlated with any other waveform, or neither waveform will be resolvable.  That is exactly what the RPMA access scheme was designed to do.

RPMA satisfies both of these requirements; LoRa only partially satisfies the second criteria.  LoRa signals at different data rates have a chance of both being resolvable which results in incremental capacity benefit (<30%), not the game-changing 1000x advantage that RPMA enables. Note that RPMA satisfies these requirements without the need for protocol overhead.  This is a big differentiator from cellular technologies such as LTE (and the LPWA offshoots like NB-IOT) and I will discuss this in more depth in a future blog.

As detailed in Appendix A-C of How RPMA Works:  The Making of RPMA, there are other significant factors that influence the total amount of data that can be pushed through the spectrum (ηgoodput )

  • Amount of Media Access Control (MAC) protocol overhead such as retransmissions and headers.
  • The effect of transmissions that are heard by multiple access points/base stations (known as other cell interference) and the corresponding reduction in capacity.
Technology Spectral Efficiency Single Link

η link

 Spectral Efficiency

Multiple Access

η MA

 Spectral Efficiency

Goodput

η goodput
RPMA 0.00006 to 0.008 0.03 0.0026
Sigfox 1 0.013 0.00019
LoRa (US) 0.0049 to 0.047 0.00057 0.000013
LoRa (Europe) 0.0049 to 0.0015 0.00017 0.000019

And that’s not all!  ηgoodput is a per Hertz of bandwidth number, it still needs to be multiplied by the amount of spectrum available!  Since RPMA is at 2.4 GHz where 80 MHz is available, we can use significantly more spectrum that the fractured, regional subGHz bands that the Sigfox technology and LoRa use.

Technology Spectral Efficiency Goodput

η goodput

 Available Spectrum (MHz)

BW

 Total Goodput

(bps)

η goodput xBW
RPMA 0.0026 16 41,000
Sigfox 0.00019 0.2 37
Lora (US) 0.000013 12.8 170
Lora (Europe) 0.000019 1.6 30

As you can see, RPMA has a massive advantage in the resultant goodput.   That means a piece of RPMA network infrastructure (typically on a tower or rooftop) will be capable of servicing orders of magnitude more devices and devices with far greater data needs.  We’ll crunch some numbers on this in Blog 5 – The Economics of Receiver Sensitivity and Spectral Efficiency.

At any point, if you’re interested in a more in-depth treatment of these topics, please download, How RPMA Works:  The Making of RPMA.

July 22, 2016

Chirp Spread Spectrum: The Jell-O of Non-Coherent M-ary Modulation

We have asserted textbook style categories for Low-Power, Wide-Area (LPWA) connectivity Blog 1: Categories of LPWA Modulation Schemes.  Then we went back to basics to get some framework and vocabulary in place to discuss these various schemes in Blog 2: Back to Basics – The Shannon-Hartley Theorem, where we introduced the critical concepts of spectral efficiency (η), Eb/No, and how a point of diminishing returns can be reached in the quest to reach the Shannon Limit.

Based on this framework, what I would like to discuss here is why Chirp Spread Spectrum (CSS) can be treated more generically as Non-Coherent M-ary Modulation (NC-MM). The title of this blog likely makes you ask what could CSS possibly have to do with Jell-O?  With brilliant marketing, Jell-O has eclipsed the more generic terminology of “gelatin” defined as “protein obtained by boiling skin, tendons, ligaments, and/or bones with water.”  Non-Coherent M-ary Modulation (NC-MM) is analogous to “gelatin”; LoRa™(CSS) is analogous to “Jell-O”.  It is actually a compliment to the marketing around LoRa (CSS). As you can imagine, if you want to study the science behind Jell-O, you would have better luck starting with “gelatin”.  The same is true with understanding LoRa(CSS).

Category Local Area Network Wide Area Network
Ultra Narrow Band

(UNB)

 Sigfox

Telensa

N-Wave

WaveIOT
Non-Coherent M-ary Modulation (NC-MM) Bluetooth

802.11b

LoRa (CSS)

Sensus (7-FSK)

 GSM/GPRS

EC-GSM

 
Direct Sequence Spread Spectrum (DSSS) 802.11

Zigbee

 

 W-CDMA

RPMA
Orthogonal Frequency Division Multiplexing

(OFDM)

 802.11a/g LTE

WiMax

NB-IOT

But first, we must justify why we’re lumping CSS into the NC-MM category.  There are two parts to the argument:

  1. It’s the same math. The mathematical operations for demodulation are identical to other well-known NC-MM waveforms – particularly M-ary FSK.
  2. If it looks like a duck, swims like a duck… CSS has the same performance as other NC-MM waveforms (again particularly M-ary FSK), and that is further proof that it’s best to treat CSS more generically as NC-MM.

I will back up the two arguments below.

It’s the Same Math

Please take a look at the figure below – it is the demodulation engine of 2-ary FSK.  If you added M-2 more elements, this could be turned into a demodulation engine for M-ary FSK.  And, most critically, if the set of waveforms in the figure Φ are sinusoids, it is an FSK demodulator, and if Φ is an orthogonal set of chirp waveforms, it’s CSS.  As long as the set of waveforms Φ are an orthogonal set, the performance of this demodulator will be 100% identical regardless of the particular orthogonal set Φ.

blog3 math

If it Looks Like a Duck, Swims Like a Duck…

Beware the straw man argument! Taking a look at the document entitled “LoRa Modulation Basics”, they do a comparison of the LoRa modulation (also known as CSS) against FSK and make the claim of an impressive sensitivity improvement of 15 dB for the SF = 12 LoRa mode over FSK (There is an obvious typo in the equivalent bit rate column in SFs 6 through 9 where the data rate is expressed in b/s as opposed to the stated kb/s.)

First, we need to talk about the relationship between SF and spectral efficiency.  For un-coded Non-Coherent M-ary Modulation (NC-MM) the expression for η = (SF)2-SF (in other words a non-coherent symbol yields SF bits of information and that non-coherent symbol is of duration 2SF.)    If channel coding as added into the mix (like in the LoRa waveform), this expression becomes η = R(SF)2-SF where R is the code rate (typically rate ½).

blog 3 tableOn face value, that makes the claim that the math is the same rather dubious.   However, as discussed in Blog 2: Back to Basics – The Shannon-Hartley Theorem, there are several reasons why similar modulations may achieve different sensitivity.

  • Increased data rate means reduced receive sensitivity even with the same spectral efficiency (η).
  • Increased spectral efficiency means reduced Eb/No which reduces sensitivity.
  • Almost all modern communication theory approaches include channel coding. An apples-to-apples comparison would be consistent relative to whether channel coding was implemented.  A very popular and effective channel coding is convolutional encoding coupled with a Viterbi decoder (invented and named after the same individual that is on our advisory board).

As it turns out, relatively higher data rate, spectrally efficient, un-coded 2-ary FSK is fairly easy to beat up on in terms of sensitivity.  A more accurate comparison point would be the coded 7-ary FSK that a company called Sensus uses.  The Sensus flavor of FSK is more of an apples-to-apples comparison because 7-ary FSK has a lower spectrally efficiency (η) which allows for better Eb/No.

Mode Coding Equivalent bit rate (kb/s) Eb/No η Δ (dB)
7-ary FSK Rate ½ (likely) 8 9 0.2 0.0
2-ary FSK No 1.2 17.2 1 -8.2
LoRa SF = 12 Rate ½ 0.293 8.3 0.0015 0.6
LoRa SF = 11 Rate ½ 0.537 8.2 0.0027 0.8
LoRa SF = 10 Rate ½ 0.975 8.1 0.0049 0.9
LoRa SF= 9 Rate ½ 1.757 8.6 0.0088 0.4
LoRa SF = 8 Rate ½ 3.125 9.1 0.016 -0.1
LoRa SF = 7 Rate ½ 5.468 9.6 0.027 -0.7
LoRa SF = 6 Rate ½ 9.375 12.3 0.047 -3.3

Thus, from a fundamental technology viewpoint, the well-marketed LoRa (CSS) is really no different than the Sensus 7-FSK approach that has been around in the utility space for years. The Sensus technology is known to be very low capacity, typically appropriate for meter billing reads only, and requiring licensed spectrum to operate robustly. The Sensus technology is certainly not a WAN as we define it.       

In the table above, notice many of the LoRa modes have η << 0.01.  Is that bad in terms of capacity?  Not necessarily, is the general answer.  What’s not explored in the Shannon-Hartley theorem is the ability to send simultaneous links over the same bandwidth.  That is potentially a bit of a “loophole” in the theory. Does LoRa leverage this “loophole”?  What about RPMA?  I’ll explore that in more detail in Blog 4: “Spreading” – A Shannon-Hartley Loophole?.

And finally, I’ll get back into the real world and test whether this discussion is even useful in Blog 5: The Economics of Receiver Sensitivity and Spectral Efficiency to attempt to quantify the commercial value of that which is discussed on these blogs.

At any point, if you’re interested in a more in-depth treatment of these topics, please download, How RPMA Works:  The Making of RPMA.

July 20, 2016

Setting the Stage: Categories of Low-Power, Wide-Area Modulation Schemes

At first glance, the number of communication technologies being discussed for Low- Power, Wide-Area (LPWA) networks may be a bit overwhelming.  What may be helpful is to look at the underlying technology from a fundamental perspective and tune out the marketing component.  Note that marketing is an extremely important aspect of LPWA success, but since I am a CTO, I would like to push it to the side for this discussion.

Many of you know that Communication Theory is a very mature field going back many decades with a vast wealth of generated knowledge.  Tens of thousands of books, articles, and papers have been published over this time.  There are giants in the field – Claude Shannon, Harry Nyquist, Ralph Hartley, Alan Turing, and Andrew Viterbi (who has been an Ingenu strategic advisor from the beginning) whose work we can turn to for clarity.    This great body of work gives us frameworks and vocabularies for comparison.  It’s often a drier and less interesting world once the marketing innovation is subtracted out – but please, bear with me.

The table below shows four categorizations of the various categories of modulation schemes that are being discussed for LPWA.  Bold denotes those technologies being branded as applicable to Low-Power, Wide-Area networking.  Since this is a technology treatment, I am defining Local Area Network (LAN) and Wide-Network (WAN) by the underlying technology as opposed to how these approaches are being marketed. Note that the most well-known approaches in each category are the Sigfox™ technology, LoRa®, also known as Chirp Spread Spectrum (CSS), Narrow-Band IOT (NB-IOT), and Random Phase Multiple Access (RPMA®).  These technologies tend to be those with the best marketing (yes, marketing is very important).

Category Local Area Network Wide Area Network
Ultra-Narrow Band

(UNB)

 Sigfox

Telensa

N-Wave

WaveIOT
Non-Coherent M-ary Modulation (NC-MM) Bluetooth

802.11b

LoRa (CSS)

Sensus (7-FSK)

 GSM/GPRS

EC-GSM

 
Direct Sequence Spread Spectrum (DSSS) 802.11

Zigbee

 

 W-CDMA

RPMA
Orthogonal Frequency Division Multiplexing

(OFDM)

 802.11a/g LTE

WiMax

NB-IOT

From a technology perspective, the definition of being appropriate as a WAN is whether the multiple-access considerations of coverage and capacity are taken into account:

  • Coverage. If you want to build a WAN, you would like a single piece of network infrastructure (often on a tower or rooftop) to cover as much area as possible.
  • Capacity. There’s not a lot of good in covering a massive area if you cannot support the data needs of all the devices in that footprint.

Giving a bit more color on the categories:

  • Ultra-Narrow Band (UNB). The reason many companies have elected this approach is the advantage of low barrier to entry.  Companies in this category can leverage commodity radios and skip any technology development.  These companies tend to argue that no new technology is required.  We disagree for many reasons including the inability to make the economics of LPWA work as discussed in Blog 5: The Economics of Receiver Sensitivity and Spectral Efficiency.
  • Non-Coherent M-ary Modulation (NC-MM). This is a commonly used modulation in both LAN and WAN applications. Cellular 2G technology was based on GSM/GPRS which uses a modulation approach called Minimum Shift Keying (MSK) and is also being repurposed to Extended Coverage GSM (EC-GSM) which is cellular LPWA in 2G spectrum.  The LoRa modulation (CSS) is a member of this category (as is justified in Blog 3: Chirp Spread Spectrum: The Jell-O of Non-Coherent M-ary Modulation.  The “spreading” of CSS has no discernible advantage and indeed, as discussed in Blog 4: “Spreading” – A Shannon-Hartley Loophole?, has some significant drawbacks in terms of spectral efficiency.
  • Direct Sequence Spread Spectrum (DSSS). We described LoRa as “spreading” for no discernible reason.  Well it turns out NC-MM does not have the monopoly on this. DSSS has a couple of technologies that also spread for no discernible reason – IEEE 802.11 (the original 1 and 2 Mbps data rates) and Zigbee (based on IEEE 802.15.4).  This is just one example to show you that standards bodies are less about technology and more about politics.  I will discuss this in more depth in a future blog.
  • Orthogonal Division Multiple Access (OFDM). This is the way you get extreme spectral efficiency.  It’s great for voice high-speed data and has enabled LTE (4G) to become the dominant cellular standard. When you try to point this approach at LPWA (such as NB-IOT), significant problems emerge.  I will discuss this in more depth in a future blog.

In the next blog, we visit the building blocks of communication and translate the perfectly good and intuitively understandable terms of coverage and capacity to nerd in Blog 2: Back to Basics – The Shannon-Hartley Theorem.

  • Coverage translates to receive sensitivity, which is a function of something called Eb/No (energy per bit relative to thermal noise spectral density).
  • Capacity translates into something called spectral efficiency and we need to go even one level deeper into nerd and assign it a Greek letter (of course) and that Greek letter is…. η.

Using this fundamental framework, in Blog 3: Chirp Spread Spectrum: The Jell-o of Non-Coherent M-ary Modulation, I’ll talk about a category of approaches that is very similar from a technology point of view and one very successfully marketed approach in this category called Chirp Spread Spectrum (also known as LoRa).

And while we’re on the subject of spreading, I’ll discuss how there a potential capacity loophole to take advantage of in Blog 4: “Spreading” – A Shannon-Hartley Loophole?.

And finally, let’s get back into the real world and test whether this discussion is even useful in Blog 5: The Economics of Receiver Sensitivity and Spectral Efficiency to attempt to quantify the commercial value of that which is discussed on these blogs.

At any point, if you’re interested in a more in-depth treatment of these topics, please download How RPMA Works: The Making of RPMA.

July 18, 2016

Reduce, Rethink, Recycle

When deploying an Internet of Things (IoT) strategy, it is important to consider the longevity of the network as well as the longevity of the devices that will be attached to said network.  The benefit of the IoT lies in its relatively low device costs, but this benefit can be all but negated when the device needs regular servicing or replacement.

Avoiding an expensive service truck roll is paramount, especially when it is just to change out a device battery. Low-power networks typically serve devices which are battery powered, and ideally, these devices should remain operational for years, even decades without the need for attention.

Changing IoT device batteries can harm profitability, but it can be even more harmful to the environment.

According to the U.S. Environmental Protection Agency (EPA), each year Americans throw away more than three billion batteries, which equates to about 180,000 tons of waste.  While modern batteries do not contain the same harmful properties of their predecessors, choosing to recycle these batteries is still a better choice as they can take decades to break down in a landfill.

Although the U.S. is a leader in IoT device adoption, it lags far behind other countries in battery recycling initiatives.  It is estimated that European countries such as Switzerland, Belgium and Sweden recycle more than 50% of all batteries, while the United States is in the low double digits for battery recycling, despite the prevalence of clean initiatives throughout the nation.

Aside from changing habits, we should look at changing consumption.  As advances in battery life technology continue to progress, devices can utilize fewer and fewer batteries throughout their lifetimes.  Additionally, designing an IoT network to effectively transmit data at very low power can also help extend battery life.   So, when implementing an Internet of Things strategy, remember that the battery is one of the most important “things” that should be considered.

For more discussion on this topic, download our white paper, The Six Secrets to Extremely Long Battery Life.

July 13, 2016

My Fascination with RPMA

“How can a 2.4 GHz radio signal from one tower cover 300 square miles when my 2.4 GHz Wi-Fi access point can hardly cover my house?”

This is a question I often get asked when I first tell people about the capabilities of the Ingenu Machine Network.

I wondered the same thing when I first started following all the low-power wide area (LPWA) network industry.  Sigfox, LoRa and Weightless-N were all using 868/915 MHz frequencies to cover long distances at low power.  I thought Ingenu was exaggerating their claims.

That coupled with the fact that the industry analysts I’d been following for the previous three years had said that the company was a niche player.  They didn’t appear to be interested in expanding beyond the utility and oil & gas industries.

Boy, were they wrong.  Things changed dramatically when John Horn came to Ingenu.  He is a 14-year veteran of the machine-to-machine (M2M) industry and was appointed CEO in mid-2015.  With the blessing of the board, he immediately changed the company name from On-Ramp Wireless to Ingenu and committed Ingenu to deploying its Random Phase Multiple Access (RPMA®) technology on the company’s public Machine Network™ in 30 metropolitan areas in the US by the end of 2016.

When I read that, while preparing to do a presentation to update the Dallas IEEE chapter, I did a lot more digging to understand why Ingenu was so confident and willing to commit to such an aggressive goal.

As I researched the technology behind Ingenu’s RPMA protocol, I began to understand the power of the technology and why the company would ultimately be successful.

The first thing I learned was that two engineers from Qualcomm had started On-Ramp Wireless back in 2008.  I knew the technology that Qualcomm had created was very powerful.  Their radio protocols were used for clandestine communications during the Cold War.  Those techniques evolved and became the basis for the cellular industry’s CDMA (Code Division Multiple Access) standard.  Powerful stuff.

Then I also discovered:

  • Ingenu had installed 38 private RPMA networks worldwide since 2008
  • Over the eight years of its existence, the company had invested over 300 person-years in development, enhancements and real-world field testing to create an extremely sophisticated radio protocol dedicated for machine-only communications
  • The radio signal is a highly processed signal that uses such techniques as:
    • Spread spectrum
    • Extreme “processing gain”
    • Selectivity
    • Tight control of remote node transmit power
    • Antenna diversity
    • Custom ASIC to control the node’s complex calculations and functions
    • The list goes on…

The more I read about Ingenu and their intensely scientific and highly engineered approach the more impressed I was.

As a final step in my research, I decided to reach out to the two engineers that left Qualcomm to start On-Ramp/Ingenu.  Dr. Ted Myers, the CTO of Ingenu connected with me and agreed to talk briefly on the phone.  We expected the call would last maybe 10 or 15 minutes but after an hour we were still talking “geek” about the protocol and how Ingenu planned to build out the RPMA nationwide… then worldwide.

After that call, I was convinced Ingenu’s RPMA protocol was heads and shoulders above the rest of the LPWA players.  I then set my sights on working for Ingenu, and invited their business development team to come to Dallas-Ft. Worth to present their story and spread the word.  I arranged a 90-person meet-up at UT Dallas and took them around to several prospects in the area to explore ideas of how to build an eco-system around the Ingenu Machine Network.

The rest is history.  I’m now working with Ingenu to promote the launch of the Dallas-Ft. Worth network then Austin, Houston and San Antonio as they go live.  What a ride.

JOIN THE INGENU NEWSLETTER

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google
Save