Massive MTC – Reducing the Physical Layer Overhead through Multi-Carrier Compressed Sensing based Multi-User Detection (MCSM)

Carsten Bockelmann (University of Bremen)

In the 5G book we focused on the overarching challenges to reduce the signaling overheads from protocol level to the physical layer design. Several ideas were discussed to resolve the problems of todays access reservation strategies to enable a truly massive access. However, todays systems mostly rely on coherent detection strategies that require knowledge about the channel state. Therefore, efficient channel estimation is a very important aspect to reduce the physical layer overhead in case of a massive number of users. In principle, the channel state information of every user communicating with the base station must be estimated which incurs a significant overhead in massive MTC with very small payloads (think temperature sensors, status messages, etc.). Furthermore, if large coverage areas are targeted high quality channel estimation requires significant resources for pilots to ensure good Signal-to-Noise ratios by noise averaging. Therefore, an alternative approach is called for.


Figure 1 – Multi-Carrier Compressed Sensing Multi-User Detection (MCSM) concept and components.

Taking the lessons learned and summarized in current research and the 5G book we proposed the so-called Multi-Carrier Compressed Sensing based Multi-User Detection (MCSM) as a physical layer concept for massive MTC [1, 2, 3]. MCSM is comprised of three main building blocks: (i) a multi-carrier waveform; (ii) Compressive Sensing Multi-User Detection (CS-MUD) and (iii) non-coherent communication.

The many advantages of multi-carrier waveforms are well discussed and need not to be repeated here. For MCSM two things are of importance: the realization of narrowband sub-channels in larger spectrum bands and flexible allocation of such narrow-band sub-channels. Specifically, narrowband sub-channels are required to enable easy differential modulation as explained below.

The second building block is Compressed Sensing based Multi-User Detection (CS-MUD) which serves as an activity detector in this concept and simultaneously separates the multi-user data streams that are superimposed through CDMA-like spreading [4]. Thus, CS-MUD reduces the protocol overhead as already discussed in the book, but in contrast to previous assumptions does not estimate the user data symbols. Instead it realizes the multi-user detection and provides estimates of the differentially encoded user symbols.

Finally, the third block “differential modulation” is introduced to solve the pilot overhead problem through non-coherent detection. Non-coherent communication is a very attractive solution for several reasons. A major advantage is the avoidance of channel estimation and the incurred pilot overhead. Instead of channel estimation and equalization the data is mapped onto the phase of transmit symbols which makes it robust against phase changes caused the by the transmit channel. If the channel is non-frequency selective and constant over the frame length only the starting phase of the data symbols must be known which reduces the overhead tremendously. As already indicated, the building block “multi-carrier waveforms” is required to implement this easily in a multi-service context. Massive MTC users are served by allocating sufficiently small sub-bands within the coherence bandwidth of the channel for a single MCSM system. Then, each MCSM system only experiences a non-frequency selective single-tap channel well suited for non-coherent modulation.

Of course, it is well known that non-coherent modulation suffers performance losses equivalent to a 3 dB SNR loss, but with advanced demodulation concepts this loss can be compensated in part [5]. So, fitting the theme of “simple transmitter” and “complex receiver” complexity is once again shifted to the base station for massive MTC uplink communication.


Figure 2 – Narrowband MCSM systems hopping in frequency.

A downside of narrowband MCSM channels is the dependence on channel quality as illustrated in Fig. 2. In the unlucky case that a user experiences a “bad” channel in the allocated frequency band decoding is nearly impossible. Therefore, the MCSM concept includes frequency hopping to allow for frequency diversity in one frame. Multiple MCSM systems can hop (pre-planned or randomly) in the allocated massive MTC resources as shown in Fig. 2 and thereby achieve a more stable performance. However, hopping incurs additional overhead. The starting phase of the differentially encoded user symbols must be known after each hop which is equivalent to another “pilot”. Hence, careful system design is required to balance overhead and diversity gains appropriately.

Finally, it is interesting to have a look at the performance of the MCSM concept depending on the allocated bandwidth. Fig. 3 shows the frame error rate after decoding of a half-rate convolutional code given different per user data rates [2]. Each rate corresponds to “narrowband” bandwidth that is allocated (fixed D-QPSK modulation and code rate). Thus, with increasing data rate the coherence bandwidth of the channel (approx. 300 kHz here) is increasingly violated leading to additional decoding errors. It is quite clear that such a system is highly dependent on the coherence bandwidth and chosen data rates (bandwidth) and must be carefully designed. Still, the general concept shows promising performance with low physical and medium access layer overheads. Also, we could show a first practical evaluation of MCSM in indoor contexts to demonstrate the practicality of the approach [6]. Surly, depending on the cell sizes, deployments, and so on the MCSM parametrization requires careful adaptation in a larger system context like 5G.


Figure 3 – Frame error rate over SNR for different data rates. Each data rate corresponds to an MCSM system bandwidth. Increasing data rates violate the coherence bandwidth (ca. 300 kHz) of the channel.


[1]       F. Monsees, M. Woltering, C. Bockelmann, and A. Dekorsy: „Compressive Sensing Multi-User Detection for Multi-Carrier Systems in Sporadic Machine Type Communication,” IEEE 81th Vehicular Technology Conference (VTC2015-Spring), Glasgow, GB, May 2015.

[2]       F. Monsees, M. Woltering, C. Bockelmann, and A. Dekorsy: „A Potential Solution for MTC: Multi-Carrier Compressive Sensing Multi-User Detection,” The Asilomar Conference on Signals, Systems, and Computers, Asilomar Hotel and Conference Grounds, USA, November 2015.

[3]       F. Monsees, M. Woltering, C. Bockelmann, and A. Dekorsy: “Multicarrier, Multi-User MTC System using Compressed Signal Sensing,” Paten application, PCT W02016177815 / DE102015208344A1.

[4]       C. Bockelmann, H. Schepker, and A. Dekorsy: „Compressive Sensing based Multi-User Detection for Machine-to-Machine Communication,” Transactions on Emerging Telecommunications Technologies: Special Issue on Machine-to-Machine: An emerging communication paradigm, Vol. 24, No. 4, pp. 389-400, June 2013.

[5]       L. Lampe, R. Schober, V. Pauli, and C. Windpassinger: “Multiple-Symbol Differential Sphere Decoding,” IEEE Transactions on Communications, Vol. 53, No. 12, December 2005.

[6]       M. Woltering, F. Monsees, C. Bockelmann, and A. Dekorsy: „Multi-Carrier Compressed Sensing Multi-User Detection System: A Practical Verification,” 19th International Conference on OFDM and Frequency Domain Techniques (ICOF 2016), Essen, Germany, August 2016.



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