Keynote on Massive and Ultra-Reliable Access at IEEE ICC Workshops

I have given a keynote speech at the IEEE MASSAP Workshop at ICC 2015 in London. The talk is on wireless massive and ultra-reliable communications, which are seen as two new modes that will be featured in 5G. There is, of course, a technical part in the talk, but there is also a part which argues why the research on wireless and communication theory is still vital. The slides can be found here:

Wireless Lowband Communications: Massive and Ultra-Reliable Access

First impressions on the IEEE 802.11ah standard amendment

As highlighted in the previous blog post, there is a new emerging standard in the M2M arena based on the IEEE 802.11 standards family. This standard is being developed under the IEEE 802.11ah group, and aims to define the physical (PHY) and medium access control (MAC) layers that operate at radio frequencies below 1 GHz. One of the goals of this standard is to ensure that the transmission ranges up to 1 km and that the data rates per user are above 100 kbit/s.

The standard is currently being drafted, but some essential details about this new standard are already available, which we will highlight in this blog post. It is important to emphasize that although the IEEE 802.11ah standard will define operations below 1 GHz, it will not use the TV white space bands (54-698 MHz in the US), which are targeted instead by IEEE 802.11af.

The PHY transmission in IEEE 802.11ah is an OFDM based waveform consisting of a total of 64 tones/sub-carriers (including tones allocated as pilot, guard and DC), which are spaced by 31.25 kHz. The modulations supported include BPSK, QPSK and 16 to 256 QAM. It will support multi user MIMO and single user beam forming.

In [1] is stated that stations will support the reception of 1 MHz and 2 MHz PHY transmissions. The channelization (i.e. operating frequency) depends on the region. In Europe it will be within 863-868 MHz, allowing either five 1 MHz channels or two 2 MHz channels. While in the US the available band will be within 902-928 MHz, allowing either twenty-six 1MHz channels or thirteen 2MHz channels. In Japan, the available band is within 916.5-927.5 MHz, with eleven 1MHz channels. In China the available band will be within 755-787 MHz, with thirty-two 1 MHz channels. South Korea and Singapore also have specific channelizations that can be found in [1].

The MAC layer will include a power saving mechanism and an alternative approach to perform channel access, which will allow an access point to support thousands of stations, as required for M2M applications. The channel access also supports a mode of operation where only a restricted number of stations can transmit.

There are several use cases for this standard [2], which include:

  • Sensor Networks – where the IEEE 802.11ah is used as the communication medium for the transmission of short-burst data messages from sensors, which include smart metering;
  • Backhaul networks for sensors – where the IEEE 802.11ah can be used to create the backhaul of mesh networks created by IEEE 802.15.4 networks;
  • Extended Wi-Fi range for cellular traffic off-loading – where the IEEE 802.11ah can be used to off-load traffic from a cellular network. The caveat is that the performance should be at least comparable with the one from the cellular network;
  • M2M communications – Whereas current systems are optimized more for human-to-human (H2H) communications, IEEE 802.11ah standard will mainly consider sensing applications.
  • Rural communication – Wireless communication in rural areas has led to some effort that is also titled as bridging the digital divide. Large potential is given by sub 1 GHz due to the wider supported range.

In future blog posts, we will follow up with the standardization activities in IEEE 802.11ah.

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M2M @ IEEE Globecom 2012

M2M has been a notable topic in IEEE Globecom 2012, ranging from tutorials, industry fora and a dedicated workshop, featuring technical papers and a panel. Perhaps the most interesting discussions were circling around the question which of the wide area wireless technologies will prevail and transform itself into a dominant M2M solution.

2G (GSM/GPRS) is a very mature technology, long abandoned by the cutting-edge research in wireless communications, but the one that has won the trust of the end users due to its ubiquity, reliability, energy efficiency and low cost. Our groups will soon publish a paper where we show that GSM/GPRS, using only rather minor software updates of the protocol stack, can be converted into M2M-dedicated system, in which a single cell can support 10000 simultaneous low-data rate connections (e. g. to smart meters). But, although technologically possible, 2Gmay not survive as a dedicated M2M solution on a long run due to other factors. First, in many countries 2G is scheduled to be closed down and release the frequency. the plan is to refarm the spectrum for LTE. Second, even if 2G can keep the operating frequency, the operators will not be willing to maintain multiple technologies in their network, and in that case LTE is a clear winner.

But, if 2G can technologically support various M2M applications, perhaps it can re-emerge in another form. For example, operate on a single frequency, keeping a narrow band and being owned by a M2M service provider i. e. not necessarily by a company that is also a mobile LTE-based provider. A more radical thought could be to put 2G in a certain license-exempt spectrum and large M2M users (e. g. utilities) may have their own 2G cells; the license-exempt operation could be created in a way to facilitate interference management among 2G cells that have different owners and are in proximity where they can cause interference. How about porting 2G into a “cognitive M2M radio”, by re-engineering it through protocol (software) updates?

Another thing is the role of 3G. The M2M discussion is very often between 2G and 4G; however, Qualcomm presented their solution in which CDMA-based protocols are re-engineered to support different M2M requirements (latency, reduced access overhead, etc.). Considering the impact of Qualcomm, it is clear that 3G should also be considered in the M2M discussion.

Finally, an emerging technology in the M2M arena is the sub-GHz WiFi specified as IEEE 802.11ah, dedicated to sensor networks and smart metering, and scheduled for finalization in May 2015.

WiFi Wake-up Receiver

The best way to reduce energy consumption of wireless device is to turn it on only when necessary. This is easy to realize if the transmitter and receiver know the exact timing of their communications, that is, if a complete rendez-vous can be accomplished between them. But of course, this is not the case most of the time since the traffic pattern over communications network is bursty and unpredictable: the receiver does not know when the transmitter wants to send packets to it.

Then, the transmitter can somehow poke a sleeping receiver when it needs to communicate. This is called wake-up signaling, and a lot of studies have been (and are being) done for sensor networks where the energy-efficiency of devices is one of the most important requirements. In general, the wake-up signaling is done through secondary channel. Here, the primary channel is the channel for data transmission/reception which consumes relatively high amount of energy. On the other hand, the secondary channel is only for sending wake-up message, which is realized by very simple and low-power radio. When there is no communications demand, only secondary channel is active, and radio interface for primary channel is completely turned off. Since the secondary radio consumes little amount of energy, we can significantly reduce the energy consumed in an idle state. That is, the gap of energy consumption between primary and secondary channels is exploited for energy saving.

We have been applying the concept of wake-up signaling to reduce the energy wastefully consumed by WiFi routers in a research project funded by Japanese government. The active duration of WiFi routers is much shorter than the idle duration. For example, WiFi router at your home is automatically powered-on/off according to your communications demands. A very simple wake-up receiver, which operates with non-coherent on-off-keying (OOK) detection, is installed into WiFi router. The energy gap between WiFi and such a simple receiver is so large that we can have a huge gain in terms of energy saving. But, one problem was the need for WiFi station (e.g. your laptop or smatphone) to have an additional device to transmit a wake-up signal.

Our solution to this problem was to reuse WiFi transmitter already installed into WiFi station. The simple, OOK wake-up receiver at WiFi router is designed to be able to detect the length of WiFi frames observed over 2.4 GHz ISM band. The WiFi station embeds information (e.g. wake-up ID) into the length of transmitted WiFi frames (you can imagine Morse code where the length of WiFi frame corresponds to dot and dash). The wake-up receiver turns on WiFi router if the detected length matches with its registered ID. The detailed information on wake-up mechanisms and receiver can be found in [1] and [2].

Basically, we have realized information exchange between WiFi transmitter and a very simple, low-cost, and low-power receiver which has completely different physical layer from WiFi. The layering concept has been developed to offer communications capabilities between devices having a common communications protocols. We have shown that, in a particular setting and scenario, communications between devices implementing different protocols are possible and useful. We are now seeking for scenarios in M2M where this type of communications and device can be exploited.

[1] Y. Kondo, H. Yomo, S. Tang, M. Iwai, T. Tanaka, H. Tsusui, and S. Obana,” Energy-efficient WLAN with on-demand AP wake-up using IEEE 802.11 frame length modulation,” Elsevier Computer Communications, Vol. 35, Issue 14, pp. 1725–1735, August 2012.  http://www.sciencedirect.com/science/article/pii/S0140366412001478

[2] H. Yomo, Y. Kondo, N. Miyamoto, S. Tang, M. Iwai, and T. Ito, “Receiver Design for Realizing On-Demand WiFi Wake-up using WLAN Signals,” in Proc. of IEEE Globecom 2012, Dec. 2012. http://arxiv.org/abs/1209.6186

MASS M2M at 3rd ETSI Workshop

Two of our researchers (German and Nuno) are at the 3rd ETSI Workshop presenting a poster about our ongoing work in enhancing the capacity of GPRS and LTE in the Radio Access Network.

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There are more than 220 participants attending the workshop.

German and Nuno will post a summary of the events in the workshop in the end of the day.

If you are around come and visit them in the poster section during the coffee breaks.

Distributed control of DGs within microgrid

In this blog we continue our overview on the use of M2M communications in microgrids (see https://massm2m.wordpress.com/2012/09/11/m2m-communications-in-microgrids/).

As outlined in the previous blog, the future smartgrid is envisioned as a network of microgrids. Microgrid is a small-scale electrical network consisting of localized distributed generators, storage capacities and loads, interconnected by low-voltage (LV) cables.

Distributed generators (DGs) within microgrid typically exploit renewable energy sources (such are photo-voltaic and wind generators) whose behavior cannot be controlled, posing new challenges with respect to balancing the generated power and user loads. Basically, when controlling the DGs within microgrid, the imperative is that their output voltages and frequencies should match.

In traditional grids, the voltage and frequency outputs of generators are regulated using the so-called droop control. Droop-control is essentially a proportional control algorithm that is executed locally at each generator, driving output voltage and frequency based on the locally measured active and reactive powers. Loads and generators in traditional grids are interconnected using high-voltage cables, which have much greater reactance then resistance. Taking into account this fact, it can be shown that, when the active and reactive power that are flowing into the cable from the generator are expressed through the generator’s voltage (and assuming that the power angle is small), the active power depends on the generator’s frequency and the reactive power depends on the generator’s voltage. The overall result is that, in traditional droop control, the frequency is regulated by the local measurements of the active power, and voltage is regulated by the measurements of the reactive power.

Traditional droop control cannot be used directly in AC microgrids[*], as resistance of LV cables dominates over reactance. One way to address this issue is to modify the droop control such that control algorithm includes the information exchanged among DGs[**] using communication network.

In a recent paper [1], the authors apply the paradigm of distributed consensus algorithms, exploited in their earlier works (see previous blog, https://massm2m.wordpress.com/2012/09/11/m2m-communications-in-microgrids/), to conduct series of local exchanges among DGs, in order for all DGs to learn what is the global state of active and reactive power. Traditional droop control is then augmented to include this information, such that frequency controller includes a term proportional to the differences between the desired and the actual active and reactive power, and the voltage controller uses a corresponding integral term. Using small signal analysis, the authors show that improved stability of the microgrid is achieved with respect to the traditional drop control.

The authors of [2] consider a similar setting, but the focus of their work is the impact of communication impairments, such are packet loss and delay, on the (modified) droop control, rather than on the control algorithm itself. The key observation is that outdated information about remote measurements, when combined with locally obtained measurements, can adversely affect the performance of the droop control. In order to neutralize this effect, the authors suggest a scheme in which locally obtained signal is delayed using the same delay distribution of the remote signal. The local delay is realized using Smith predictor and it is statistically the same as the delay of the remote signal. Finally, the authors present the simulation results obtained in an example microgrid with two DGs, showing that in the proposed scheme a more stable active power output is achieved, compared to the case when there is no local delay.

Recently, our group started collaboration on the same topic with a group at Department of Energy Technology at our university, with an aim of designing of robust and simple communication algorithms in the framework of DG control, both in AC and DC microgrids. The initial results are encouraging, however, we leave their presentation for our future blogs.

[1] H. Liang et al, “Decentralized Inverter Control in Microgrids Based on Power Sharing Information through Wireless Communications”, to be presented at IEEE GLOBECOM 2012

[2] S. Ci et al, “Impact of Wireless Communication Delay on Load Sharing Among Distributed Generation Systems Through Smart Microgrids”, IEEE Wireless Communications, June 2012

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[*] Regulation of DGs in DC microgrids will be addressed in our future blogs.

[**] Droop control is actually applied at inverters that connect DGs to the interconnecting AC bus.

M2M traffic in cellular networks

Current cellular mobile networks are designed for human communication, and therefore are optimized for the traffic characteristics of human-based communication applications, i.e. communication with a certain session length, data volume, interaction frequency and patterns.

As the number of Machine-to-Machine (M2M) type devices increases so does the motivation to optimize existing cellular networks. While the features of the traffic generated by M2M devices are varied and application specific, the connected devices will be a mix of sensors and actuators. It is expected that a large majority will be sensors; therefore a bulk of the generated traffic will be in the uplink direction, i.e. from the M2M devices towards the network.

According to a recent market research study, in the next 10 years it is expected that the amount of M2M devices connections will reach 2.1 billion and that from these 61% will be due to the utilities market [1].

There are two interesting points about the characteristics of the traffic generated by this kind of application: The first is that the amount of data reported is generated sporadically and in small amounts, i.e. only a few bytes at a time; The second is that due to the expected density of the devices and the observed correlation between the reporting times [2], then the network will often observe a traffic pattern similar to a botnet, i.e. the traffic will arrive in batches. These two points will have an adverse effect on current cellular networks.

Currently, if one of these M2M devices wants to send a report to the network it will most likely use as carrier a SMS message. For this to happen, first the device needs to be connected to the network, which entails significant signaling overhead [3], then there is also the overhead associated to the transmission of a SMS message. Therefore, in the case of batch arrivals this will easily lead to the network to become overloaded due to signaling, while only transmitting a limited amount of data.

The network overload can occur at the radio access network, i.e. from a multitude of devices towards a base station, where in this case the base station gets overloaded, or globally where several base stations convey the traffic to a core network node. The ultimate consequence is the induction of signaling congestion and high computational load in the network.

In future blog posts we will describe what are the ongoing efforts to optimize the cellular networks to handle the characteristics of this particular type of M2M traffic.

[1] 2.1 billion M2M devices in 2021, http://www.analysysmason.com/About-Us/News/Press-releases1/M2M-forecast-2012-PR-Jun2012/

[2] M. Zubair Shafiq et al, “A First Look at Cellular Machine-to-Machine Traffic – Large Scale Measurement and Characterization” in Proceedings of the International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), London, United Kingdom, June 2012.

[3] Annex B.4 3GPP TR 37.868 V11.0.0

The Rise and the Research of Machine-to-Machine (M2M) Communication

This is a research blog dedicated the communication technologies that are related to Machine-to-Machine (M2M) communication. We will primarily provide information related to our research project MassM2M, but also analysis of the relevant literature, technology trends, and research ideas.

What is M2M?

Probably today you have used M2M communication if you paid by a credit card. The terminal (a machine) in the shop connects to a server (another machine) in order to approve the transaction. M2M is about communication between devices, objects, things, which is different from Human-to-Machine (H2M) (e. g. “Googling”) or Human-to-Human (H2H) (e. g. “Skyping”).

Techno-economical forecasts indicate that in the coming years M2M communication will become massive, connecting tens of billions devices. Wireless chips have grown in capability and power efficiency, while shrinking in size and cost. It becomes affordable to embed wireless chips in many diverse objects and make them “digitally visible”, similar to the way a person is digitally visible through Facebook.

M2M becomes massive also in terms of diversity across applications. Wireless M2M networks are instrumental to manage the complexity of tracking, fleet, and asset management. The industrial sector can widely apply M2M in monitoring and control of processes and equipment. Radio Frequency Identification (RFID) in the retail industry is an example where M2M enables real-time visibility of the individual items. The M2M showcase is the smart grid: the evolved power grid where a rich information flow is used to balance the electricity production (e. g. windmills), distribution, storage, and consumption (e. g. large industrial capacities).

M2M has a large transformational power to make the processes more efficient by saving time, costs, and energy. But the present optimism about M2M is also fueled by its promise for new business models and the large innovation potential. The companies that use M2M, such as the industrial sector, can introduce novel features in their products and deploy new, information-intensive services. The companies that provide M2M services, such as the telecom operators, see them as important alternatives to the flat-data-rate-like services.

Skeptics may question the upcoming M2M revolution, pointing, for example, that RFID has been around for years, but failing to become massive. This is true; but it was also true that the phrase “smart phone” had been around from 1992, while it lifted off with the iPhone in 2007. Recall that the mobile phone started with voice as a single application at its focus, only to become our assistant and chief entertainer. M2M communication has started slowly with a wide range of applications, so our expectations should be very high for the upcoming billion connected devices.

Research on M2M

M2M services are already up and running in the networks of many mobile operators. Furthermore, there is are very active standardization processes related to M2M in different bodies, such as ETSI or 3GPP. So, is there a need/space for carrying out fundamental research on M2M communication technologies?

Our answer is (clearly) yes. Specifically, there are two (expected or predicted?) features of M2M communication that allow one to pose new and interesting research questions:

  • The “Massive” feature: Technology predictions say that by 2020 there will be 50 billion connected wireless devices, spanning a wide application range: smart grid, metering, control/monitoring of homes and industry, e-health, etc. Wireless networks need a revolutionary reengineering to be able to embrace the massive number of devices. In many cases M2M traffic will feature short data packets, where the useful data is comparable in size to the signaling overhead used to send that packet. To- day the networks can efficiently carry large data from few devices; the problem is how to carry few bytes from a large set of devices (machines). In a nutshell, sending 100000 bytes from one device is very different form sending 1 byte from 100000 different devices; the latter will clearly consume much more resources for signalling/coordination.
    •    The requirement for dependability. M2M communication becomes vital for various control, monitoring, and industrial processes, where it is critical to keep the wireless link alive during 99.99+% of the time. This is in a stark contrast to many existing systems, such as WiFi, which works fine around 95% of the time, but offers zero data rate under harsh receiving conditions. Increased dependability means that the wireless link is available almost all the time and, under harsh conditions, it can scale down the data rate in order to maintain reliable connection.

Other M2M issues that pose interesting research questions are security and device management. They are only peripherally related to our research, but it has to be noted that they also essential for wide adoption of wireless M2M deployments.

Regarding our research approach, we have two different tracks. In one track we investigate protocols and algorithms for rather generic communication systems. An example is Frameless ALOHA, where we are exploring a new concept for massive random access. In the second track we adapt our research context to a particular system, such as LTE, see the article on Code-Expanded Access in LTE. Although it may seem far from the cutting-edge research, we are very interested in the GSM system. We believe that the GSM networks should not be put out of use, but rather to be re-engineered and dedicated to M2M traffic. Even the research on GSM can become fundamental if we understand the GSM protocols and structure as design constraints for new protocols and signalling schemes that should be built on top of it. Our initial activities related to dependable wireless communication have been chiefly related to the university spinoff Wisecan.