Blog Archives - Page 6 of 18

August 18, 2016

CTIA Takes Center Stage, One Last Time?

Over the past several years, wireless and telecom businesses have looked forward to CTIA as the sign that summer is officially over and business is back in full swing. This year, CTIA 2016 will in many ways give its last independent hurrah before moving its tent from Las Vegas to San Francisco in 2017, with the help of the GSMA.

Last Hurrah in Las Vegas

Announced several weeks ago, CTIA will be joining forces in 2017 and beyond with the GSMA to form a new event named “GSMA Mobile World Congress Americas, in partnership with CTIA”. Reportedly, both organizations will have an equal part to play.

It seems that the aim of this newly formed alliance will be to bring the type of scale and visibility to the wireless industry that events like Barcelona’s Mobile World Congress tend to do in early spring each year. The last Mobile World Congress event held in Barcelona is said to have attracted more than 100,000 guests.; whereas, CTIA 2016 is expected to see less than half of that total.

While this isn’t the first merger involving CTIA in the last few years, this one does seem to have a clearer path to an eventual eclipse, and ultimate dismissal, of the CTIA namesake.

August 16, 2016

Off-roading in a Formula One Race Car

Formula One: Built for Speed

Formula One (F1) race cars are insanely powerful and incredibly precise machines. They can accelerate from 0 to 60 mph in as low as 1.7 seconds. That’s absurdly fast. Cornering hard in one of these boxes of speed can generate a g-force of 4 gs. That’s a jet on wheels.

But what happens if you take one of these fantastically built, and genuinely jaw-dropping F1 race cars off-road? It starts to look a little ridiculous.

5G wave will only continue push for more data.

August 5, 2016

5G and the Future of the IoT

A Little More Light on 5G

5G cellular is touted to be a great many things and can be anything, but without actual specifications being written, it is nothing. However, some common themes have emerged from various participants. With the announcement of a US-focused 5G consortium, there is at least a bit more clarity. That announcement coupled with this one by the FCC where they released “nearly 11 gigahertz of high-frequency spectrum for mobile, flexible and fixed-use wireless broadband”, shows that 5G will continue the cellular world’s chase after greater amounts of throughput. This makes sense, as their primary customers today are high-throughput devices like smartphones. And, from the way things look, they intend to expand into replacing our cable box wires with wireless cable boxes. If you think that the importance of spectrum for high-throughput uses was intense today, then tomorrow’s devices will leave you floored.

IoT growth forecasted to be like a hockey stick

August 3, 2016

The Wrong Question: When will the boom of the IoT happen?

If you ever make it out to an IoT conference and look at the speaking agenda, you will invariably find at least a couple of panels or keynotes that will discuss the impending boom of the IoT. I’ve been in the industry for enough time that I no longer ask when that boom is coming, but when we’re actually going to stop talking about it. The fact is, the overused “hockey stick” term has become quite passé (and even I, being from Canada, love hockey). It’s time for the marketplace as a whole to start talking about the growth that is happening; no longer the growth that will happen.

So, let’s start fresh here and put the future in the past. We all know there are going to be a lot of devices that make their way online. Done.

The IoT does not need to be related to a hockey stick anymore

The topic that we should be discussing is how our infrastructure will need to be designed to support the long-term viability of those gazillions of devices. We’re so excited to flood the IoT with all kinds of things that we tend to overlook the ability of technologies to support those things.

When it comes down to it, we really should be talking about one critical question: how are our networks going to be able to handle all of these things?

Up to this point, all of the fanning of flames around the growth that’s quickly coming at us is just something that analysts use to draw in eyeballs. The fact is that our connected infrastructure is like a fire that starts to grow in size only to have a network sunset or new standard come along to throw a nice big bucket of water on those growing flames.

Times are changing and technologies have emerged that give priority to this blaze of IoT growth. This is critical to the boom that has been forecasted for years. Now, solution developers can put business plans in place that will have maturity dates of more than just a couple of years.

So, now that those technologies are budding, it’s time to ask the right questions. Those questions are centered on capacity, coverage, and longevity.

Many new technologies, while dedicated to machines, do not have the ability to support more than just a couple thousand devices per access point. This is a big problem when we’re talking about a flood of connected things. It’s critical that the industry start looking a little closer at the capacity component.

When it comes to coverage, the things that make up the IoT are not always where people are. That’s what makes the IoT so valuable: remote devices can be where people don’t have to be. Coverage, especially around cellular-based technologies, is often designed and built for people. The IoT needs affordable coverage in all kinds of places.

Lastly, as an industry we need to shift the conversation to network longevity. This is that bucket of water that keeps on dashing the flames of IoT growth. It should be part of the conversation every time a solution provider evaluates a network technology. How long is that technology going to be around? If history is any indicator, most cellular standards only last a handful of years. This isn’t going to cut it as the world becomes more and more connected.

So, let’s put away the hockey sticks for a season and just focus on the things that will get us there.

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

Cracking the Crowded 2.4GHz Conundrum

A common misconception among those investigating RPMA for the first time is that the 2.4GHz band is too crowded. The Big Bad Wolf, Wi-Fi rules this domain, along with his evil minions, Bluetooth baby monitors – pretty scary, right? The perception is that this space must be riddled with interference, however, this is false.

We have all been there. It’s a Saturday night at Wal-Mart. It’s the last minute grocery run for milk and a cartoon of eggs. You hustle through the store, and are ready to check out two minutes later. You get to the checkout lanes and realize the whole town has decided to check out at the same time. All twenty lanes are jam packed. You slump your shoulders and are resigned to wait a half hour to get through the line. Then, voila! Magically, 80 checkout lanes are suddenly fully staffed and open to receive you. You bypass big bad Wi-Fi with his two carts full of groceries and move to a lane near the edge of the store. There is no line and you are out of the store a couple minutes later.

So it is with the 2.4GHz spectrum. As it turns out, the 2.4GHz spectrum is composed of 80 distinct 1MHz channels. This provides an enormous amount of flexibility, especially given that an RPMA network can reside in a single 1MHz channel. It is well documented where the Wi-Fi bands lay, and Ingenu has wisely chosen to deploy RPMA outside of those channels. In fact, there are eleven 1MHz channels completely clear of Wi-Fi on which RPMA can operate.

While choosing a channel that is free and clear of Wi-Fi resolves the majority of inquiries regarding RPMA’s handling of interference, it is but one of the many methods RPMA employs to overcome interference. Physical layer mechanisms of the network like direct-sequence spread spectrum, and forward error correction have been essential in rolling out over 30 private RPMA networks across five continents over the past eight years. RPMA is a globally proven and available technology, and from the onset has been prepared to wisely navigate the interference concerns of the 2.4GHz spectrum.

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:

Transmit power control is essential. Signals must arrive at the access point at very similar power levels for this multiple access capability to exist.
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 (η_goodput)

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! η_goodput 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 _goodputxBW
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:

It’s the same math. The mathematical operations for demodulation are identical to other well-known NC-MM waveforms – particularly M-ary FSK.
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 2^SF.) If channel coding as added into the mix (like in the LoRa waveform), this expression becomes η = R(SF)2^-SFwhere R is the code rate (typically rate ½).

blog 3 table On 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 22, 2016

Idioms and the IoT

“You’re off your rocker,” they said. “That would cost an arm and a leg,” they said. Since the announcement that Ingenu was launching a public M2M/IoT network dedicated for machines that will cover 30 US markets by the end of 2016, I have heard comments like these. There is no shortage of idioms when it comes to discussing the rapid deployment of our M2M network. One of my favorites in response to explaining how our network covers such a large area is, “Are you pulling my leg?”

The fact of the matter is that we may be off our rockers, but we are definitely not pulling anyone’s leg. RPMA® (Random Phase Multiple Access) technology is so sophisticated and has extremely long-range capabilities. We can easily deploy our technology with minimal amounts of infrastructure, allowing us to cover many markets quickly and cost-effectively. A single tower deployed with an RPMA access point can cover up to 300 square miles. This piece of equipment is about the size of a shoebox, allowing us to quickly deploy more access points if capacity is an issue as well. A prime example of how we can cover so much area with so little infrastructure would be our Dallas/Ft. Worth deployment. We are covering a little over 2,100 sq. miles with less than 20 access points. This is roughly a tenth of what a cellular carrier would need to deploy for the same area. This allows us to cost-effectively cover all of these areas, making it truly possible to cover the US.

So we guess the proof is in the pudding! We are building the world’s first network dedicated exclusively to machines. With Dallas, Houston, Austin, San Antonio, Riverside and Phoenix all served, we are well on our way to covering all 30 markets in 2016. And, we are not stopping there. The year 2017 will bring us many more markets, covering close to 75% of the US population. If you are reading between the lines and realize that all of the cost savings Ingenu enjoys will be passed on to our customers, then you have just hit the nail on the head! It’s the icing on the cake!

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Idioms and the IoT

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