Reliable Reporting for Massive M2M Communications

In many scenarios, M2M communications involve a massive number of low-rate connections. A showcase application is smart metering, consisting of a massive number of devices, up to 30000 [1], where meters periodically report energy consumption to a remote server for control and billing purposes. Massive communications present a new operating mode, not originally considered in the cellular radio access and that us why M2M-reengineering of the cellular systems has been so much into the research focus. In such a setting, conventional assessment of the system based on the average throughput is not sufficient.

We have instead asked a different question. Let us assume that each message that comes to the sensor needs to be reported within the deadline TRI with guaranteed reliability of, say 99.99%. There is a large number N of potentially reporting devices. How many cellular resources need to be used for such reporting and how to arrange those resources? There are two things that prevent us from assigning deterministic resources for reporting. One is that the arrival of a message at the device is a random process. Another is that the transmission from the device has a random outcome – the packet may end up in error and it will need to be retransmitted. In summary, the amount of resources required by each device is random.

We present a solution to this problem in our paper recently accepted at IEEE Wireless Communications Letters:

[1] Corrales Madueno, G.; Stefanovic, C.; Popovski, P., “Reliable Reporting for Massive M2M Communications With Periodic Resource Pooling,” Wireless Communications Letters, IEEE , vol.3, no.4, pp.429,432, Aug. 2014
doi: 10.1109/LWC.2014.2326674

The key idea to combat the individual randomness associated with each device is to take advantage of the statistical regularity that arises due to the fact that the number of devices N is massive. We show how to allocate resources using this statistical regularity and still guarantee 99.99% to each individual sensor.

A condensed version of the letter is given below.

How to achieve  reliability without sacrificing system efficiency?

Corrales_WCL2014-0193_Fig1

Figure 1: Representation of the LTE uplink resource structure, where a set of RBs has been reserved for M2M purposes.

In [1], we consider a system with a periodically occurring pool of resources that are reserved M2M communications and shared for uplink transmission by all M2M devices (see previous figure). The re-occurring period is selected such that if a report is transmitted successfully within the upcoming resource pool, then the reporting deadline is met.

Corrales_WCL2014-0193_Fig2

Figure 2: a) Periodically occurring M2M resource pool. b) Division of M2M resource pool in the pre-allocated and common pool.

The M2M resource pool is divided into two parts, denoted as the preallocated and  common pool, which reoccur with period TRI, as depicted in previous figure. We assume that there are N reporting devices, and each device is preallocated an amount of RBs from the preallocated pool dimensioned to accommodate a single report and an indication if there are more reports, termed excess reports, from the same device to be transmittedwithin the same interval. The common pool is used to allocate resources for the excess reports, as well as all the retransmissions of the reports/packets that were erroneously received.

The question that arises is:  How many periodically reporting devices can be supported with a desired reliability of report delivery (i.e., 99.99%), for a given number of resources reserved for M2M communications?

We note that, if each device has a deterministic number of packets to transmit in each resource pool and if there are no packet errors, then the problem is trivial, because a fixed number of resources can be preallocated periodically to each device. However, if the number of packets, accumulated between two reporting instances, is random and the probability of packet error is not zero, then the number of transmission resources required per device in each transmission period is random.

The derivation of the individual reporting reliability can be found in [1]. Promising results have been shown in the context of LTE, where even with the lowest-order modulation only 9% of the system resources are required to serve 30K M2M devices with a reliability of 99.99% for a report size of 100 bytes. The proposed method can be applied to other systems, such as 802.11ah.

Corrales_WCL2014-0193_Fig4

Figure 3: Fraction of system capacity used for M2M services, when P[Φ] ≤ 10−3, RI of 1 minute, RS of 100 bytes, bandwidth of 5 MHz and pe = 10−1.

 

[1] Corrales Madueno, G.; Stefanovic, C.; Popovski, P., “Reliable Reporting for Massive M2M Communications With Periodic Resource Pooling,” Wireless Communications Letters, IEEE , vol.3, no.4, pp.429,432, Aug. 2014
doi: 10.1109/LWC.2014.2326674

(Also available in http://massm2m.lab.es.aau.dk/publications.php)