LP Spread-Spectrum Sensors

For the IoT to truly fulfill its vision, sensors must be energy-efficient and not tethered by wires.


It is pretty much an accepted fact that the common denominator of the IoT will be intelligent sensors. Virtually everything and everyone will be “sensed.” These sensors will collect an immense amount of data, and that data will have to be funneled for analysis at some point. Much of this will occur in real time, but some of it can be stored and forwarded, or collected on demand.

Tethered sensors—defined in this case as fixed location, i.e. appliances, security cameras, entertainment centers, desktop computers, etc.) sensors do not face the same challenges as their wireless brethren. Tethered sensors generally have access to some sort of a sustaining power source; AC, solar, wind, rechargeable batteries, etc., whether they are wireless, or not. So low-power isn’t as big an issue, although low-power is still a primary criterion for green initiatives.

The most challenging scenarios for wireless intelligent sensors will be in mobile applications. Applications will vary widely as with the intelligence of the sensors. One example of where low-power wireless sensors are finding a home is in wearables. Another area is “smart dust.” A third is Ambient Intelligence, or AMI (see related article). For these, and many more IoT devices, such sensors are the ideal technology, and spread spectrum (SS) makes them secure. Finally, to make them small, lightweight, and autonomous will require a low-power platform. This article takes a look at low-power SS technology.

In many cases, the most power hungry element of a wireless sensor is the radio. So, to make the radio as low-power as possible, spread spectrum is the technology of choice for the communications link. A good overview of SS technology is presented here. In addition, there is a plethora of information available that dissects spread spectrum.

Spread spectrum, ultra wideband, and the IoT – a perfect marriage
Low-power wireless micro sensor radio design has a number of criteria that should be met. Among them are a low duty cycle, a low data rate, and short transmission range – all key elements for IoT wireless sensors. As one deviate from these basic precepts there will be a proportionally adversely effect on the sensor’s role and form factor, so it is important to use the best practices that relate to low-power spread spectrum radio design.

One of the technology platforms that show promise is the ultra-wideband (UWB) transceiver. Of late, it is finding a home in sensor data collection, so it would appear as a natural platform for the IoT. Today, UWB finds applicability in applications such as mobile LANs, wireless personal area networks (WPAN), wireless surveillance, RADAR, secure communications, and others – most of which will find homes in the IoT.

UWB’s most prominent characteristic, which is both an advantage and a disadvantage, is that it’s a short-range, low energy, high-bandwidth technology. For low-power IoT sensors, this is exactly what the doctor ordered. UWB is also an ideal platform for the implementation of spread spectrum modulation schemes because of its wide bandwidth, which is a requirement of spread spectrum technology.

UWB baseband radio architecture can be implanted in CMOS, making it cost effective. It features low power consumption because of the low duty cycles. It is multipath robust due to the frequency diversity of the spread signal, and it offers high capacity, for the same reason.

UWB Under the Covers
UWB signals are typically produced by a very fast rise time pulse. That pulse is integrated, and the impulse is derived from the first derivative of the step. That pulse is then sent to a resonant antenna. This creates an ultra-wideband spike in the frequency domain (see Figure 1).


There are a number of methodologies for implementing low-power spread spectrum designs, but the bottom line is the “energy per bit” rule.

One of the more interesting phenomena that makes UWB work well for sensors it its ability to operate as a power-limited scheme. There is an interesting anomaly here that relates to the Shannon Theorem:

Screen Shot 2014-11-07 at 12.11.57 PM

Where Screen Shot 2014-11-07 at 12.12.36 PM is receiver average signal power, Screen Shot 2014-11-07 at 12.12.50 PM is the average noise power at the receiver and, BW is the bandwidth. It can be seen from this relationship that, for low-data rate applications, the signal-to-noise ratio (SNR) can be very small for a relatively wide bandwidth. Thus a low SNR translates into low transmitted power, which is ideal for low-power applications such as wireless IoT sensors.

There are some challenges to this, however. The spread spectrum section is fairly easy to dial in so that the power consumption is optimized. However, the digital components are a bit trickier and can pose power challenges.

One component, the ADC, consumes power relative to the sampling rate. Obviously, the wider the bandwidth of the input signal, higher the sampling rate of the ADC must be (the maximum sampling rate of a system is defined by the Nyquist theorem as Screen Shot 2014-11-07 at 12.13.46 PM) and the more power will be consumed by it. As well, the ADC must have a sufficiently wide bandwidth to be able to pluck the signal from noise of the transmission. The wider the bandwidth, or the weaker or more complex the signal is, the more agile the ADC must be, and again, the more power it will consume.

One solution that helps here, is to use next-generation low-power MCUs, such as those from NXP. “There are a lot of sensors that need to be kept awake, that tie into radios,” says Ross Bannatyne, General Manager, Mass Market Product Line, Microcontrollers at NXP. “You don’t always know when the data is being sent, so lower power microcontroller with very low standby, or listen mode currents need to be part of the device.” This is especially critical for remote sensors that are difficult to get to.

System design considerations
Finally, overall design considerations for SS systems have to take into account acquisition and synchronization times (commonly lumped into what is called “wake up” time). This parameter has an effect on the power factor of the system. DSSS and FHSS systems have different parameters for wake up. DSSS is quicker to wake up than FHSS, therefore, uses less power, all else being equal. However, since each system has some advantages over the other, the choice of modulation schemes depends highly on the application.

Low-power sensors can use either coding scheme, but the choice depends largely upon the applications. For extremely low power sensors, FHSS is the better choice. Why? Because this scheme is simpler to implement in the critical subsystems of hardware, performance and power consumption.

FHSS modulation technology can use frequency-shift keying (FSK), which is a simplistic modulation scheme and allows the use of direct-conversion architectures. FHSS applies the spreading code to the modulated data. This allows for a higher level of integration, resulting in better power control. It is also easier to superimpose it on the hopping carrier via digital techniques.

DSSS, on the other hand, applies the spreading code on the carrier. This is a bit more of a complex design than FHSS because carrier modulation is a separate step that requires additional components. For example, a more complicated synchronization circuit is required to extricate the data from the code sequence. It is also more secure than FHSS, and a better choice for high-security applications because, DSSS signals can be hidden in the noise floor which makes them almost imperceptible.

There are, of course, many more subtle differences between the technologies. Even so, these subtle differences really only matter as with the more complex designs. For example, there are other forms of spread spectrum that exist, such as time-hopping spread spectrum (THSS) and a “chirp” technique that sweeps the carrier, linearly, in time. Finally, it is possible to mix and match the techniques as desired. So one can have a hybrid system that comprises elements of each technology.

Spread Spectrum and the IoT
Spread spectrum communications are very secure, right out of the box. The very nature of them makes it nearly impossible for all but the most sophisticated of methodologies to compromise them. The government has used them since WWII and they still are the most popular wireless security schemes.

For the IoT, SS radios seem to be the perfect solution to one very blatant security risk – wireless interconnect. It is already in place with a lot of current platforms – Bluetooth, Zigbee, SCADA, RFID, and others. It is mature, both in technology and economies of scale. It is well understood, easy to implement and has a wide compatibility base. And now it seems likely that SS will become the de facto wireless protocol for the emerging IoT, as well.

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