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Institut Mines-Télécom

Continuous Phase Modulation: a “New " Waveform for 5G Massive Machine -Type Communications

Malek MESSAI, Karine AMIS, Frédéric GUILLOUD

In collaboration with Giulio COLAVOLPE (Univ. Parma)

ACOSIS 2016,17-19 Oct., Marrakesh


Contents

�Context and Motivation

�CPM basics

�CPM Issues

• Complexity

• Robustness

• Multiple Access

�Conclusions

02/11/20162ACOSIS 2016, 17-19 oct., Marrakesh

CPM for 5G MTC Communications


Context and Motivation




Machine-Type Communications: The Smart Metering Example (Automatic Meter Reading)



� Meter for (Electricity), Water, Gas, …

Credits : Ondeo Systems


Requirements for Deployment

� Transmissions:

• Short Frames

• Low Throughput

• Rare (idled nearly all the time)

• Mostly Asymmetric Transmissions

� Transceivers:

• Massive deployment

• Low cost devices

• Battery-limited device




Battery Powered:How to Increase the Lifetime?

� Re-Charge Battery

• But human technical support is not acceptable

• Energy harvesting may be unavailable (e.g. gas meter

in the cellar, …)

� Reduce the Consumption

• Processing: reduced to minimum

• Transmission

─ Transmitted power

─ Power Amplifier Consumption




Power Amplification Basics



APoutPin

Pout

Pin

Variable

envelope

Constant

envelope

Lost energy(heat)

Amplifier


Linear Modulation Envelope



Pm

oy=

0.1

25 W

QPSK

Pmax = 0.25 ∆= 3 dB





Pm

oy=

0.1

25 W

QPSK 256-QAM

Pmax = 0.5

Pmax = 0.25 ∆= 3 dB∆= 6 dB





Pm

oy=

0.1

25 W

QPSK 256-QAM

QPSK – OFDM(256) 256-QAM – OFDM(256)Pmax = 0.86

Pmax = 0.77

Pmax = 0.5

Pmax = 0.25 ∆= 3 dB∆= 6 dB

∆= 8.4 dB∆= 7.9 dB


Modulations with Constant Envelope



sin � � /�

cos � � /��

Constant envelope modulations

(FSK, FM, GMSK, …):

s�� e��

Constant envelope modulations with

higher spectral efficiency:

CPM (Continuous Phase Modulation):

� � 2��

��

http://www.ece.umd.edu/~tretter/commlab/c6713slides/FSKSlides.pdf

Pmoy = Pmax∆= 0 dB

DSP of FSK (switched or filtered)

Low cost modulator:Voltage Control Oscillator (VCO)

Modulation index(VCO gain)


Continuous Phase Modulation(CPM)




CPM is not New!A little bit of history …

� First Waveforms

• 1961: MSK [DH61]

• 1972: Fast FSK [DB72]

� Hide a trellis structure: increase the minimal distance

• 1971: CPFSK [PLD71]

� Generalization: CPM

• 1979 [ARS81]



[DB72] Rudi De Buda. Coherent demodulation of frequency-shift keying with low deviation ratio. Communications, IEEE Transactions on, 20(3):429 435, 1972.

[DH61] M. L. Doelz and E. H. Heald, "Minimum-Shift Data Communications Systems," U. S. Patent No. 2,977,417, (March 28. 1961).

[PDL71] MG Pelchat, RC Davis, and MB Luntz. Coherent demodulation of continuous phase binary FSK signals (coherent demodulation of continuous phase binary fsk signals in additive white noise, determining error probability). In International Telemetering Conference, Washington, D. C, pages 181 190, 1971.

[ARS81] Tor Aulin, Nils Rydbeck, and Carl-Erik W Sundberg. Continuous phase modulation Part i and ii: Full / Partial response signaling. IEEE Transactions on Communications, 29(3):196 225, 1981.


But there’s a re-growth interest for CPM:



DECT

IRIG 106(Airborne telemetry)

Telecom Industry

Industry where measurements matter


CPM Signal Model

� Complex baseband CPM signal: � �, !, � "# e�$ �,!,%

� The information-carrying phase:

& �, !, � 2��

��



GMSK (h=0.5, L=3) MSK (h=0.5, L=1)


CPM Signal Model

� During � �th time interval t ∈ [nT, n + 1�T]:

& �, �, � �� ./

��+ 2��

�

��./01

&%,� + &%,��, and �� ∈ ±1, ±3, … , ± 25 � 1

� The CPM signal is completely defined by symbol �� and

state

6%,� &%,�, ��./01, … , ��.1�




CPM Signal Model: Trellis ( � 7/8)



6%,�9:�

&%,�9:�, ��./01

9:� , ��./0;9� , … , ��.1

9�

6%,�019�

&%,�019� , ��./0;

9� , … , ��.19 , ��96%,�

9<�

&%,�9<�, ��./01

9<� , ��./0;9� , … , ��.1

9�

��9

��9

6%,�1� 6%,�01

1�

6%,�=>?@:� 6%,�01

=>?@:�


But CPM offers also some interesting issues …

Issue #1: DECODING COMPLEXITY



[MAG16] M. Messai, K. Amis and F. Guilloud, "On the Optimization of a PSP-Based CPM Detection," in IEEE Transactions on Wireless Communications, vol. 15, no. 3, pp. 2144-2154, March 2016.


Non-Coherent v.s. Coherent Receivers

� Non-coherent

• Bluetooth, AIS

• Low complexity but poor

performance

� Coherent

• GSM …

• Better performance but

more complex (in general)



Non-coherent

Coherent

≈ 9 dB

GFSK, M=2, h=0.35, L=3 (Bluetooth)


Coherent Decoding is Based on the Trellis

�� is to be rational

�5 branches per states

• OK if 5 2�But 85/.1 states …

�Ex. GFSK

� 0.35 7/20, 5 2,

E 3• 7 F 2; 28 states!



6%,�9:�

6%,�019�

6%,�9<�

��9

��9

6%,�1� 6%,�01

1�

6%,�=>?@:� 6%,�01

=>?@:�


Reduced State Trellis

� Based on the decomposition of � according to [MAG16]:

� �H + I%

& �, J, � & �, J, �H + I$K + 2�I% � ��

��./01



Tracked by the Viterbi algorithm

Calculated using the PSP technique

Calculated at the output of the matched filter

[MAG16] M. Messai, K. Amis and F. Guilloud, "On the Optimization of a PSP-Based CPM Detection," in IEEE Transactions on Wireless Communications, vol. 15, no. 3, pp. 2144-2154, March 2016.


Trellis Reduction Example� 5/8, 5 2, E 2



The PSP update of the phase difference

for each state

Δ$KM:9 Δ$K

N∗ + ��./01N∗ � Δ%

Reduced State-trellis with �H 2 3⁄


Simulations results: uncoded case



Bluetooth standard (Binary 3GFSK modulation) for � 0,28 Q;R

Full-state receiver Reduced-State receiver

� 7 25⁄ hH 2 3⁄ �H 1 2⁄ �H 2 5⁄200 12 16 20


Simulations results: uncoded case



CPFSK modulation with � 5 8⁄Full-state receiver Reduced-State receiver

� 5/8 hH 2 3⁄ �H 1 2⁄ �H 2 5⁄16 3 4 5


How to avoid bad �Tvalues?

� Use a bound on the minimal distance

• (for uncoded cases)

� Use EXIT charts analysis

• (for coded cases)

� Choose among the remaining �T according to

the lowest complexity




Minimum Distance Analysis(Uncoded CPM)

� The normalized squared Euclidean distance:

U; J � V log; 5

� Y 1 � cos & �, J � V, ∆% � & �, J � V, �H�#

�dt

� An upper bound denoted U\ on the minimum distance U]^_can be

found:



(T1): PSP process (T2): reduced trellis

{ } { })( min )( min 2

b)(a,

2

ba f00

badbad −≤−Γ∈≠

2mind 2

Bd

Γa: difference sequence J � V of short duration on the reduced state based upon �H .


Upper Bound on U]^_ Example :Quaternary 2RC




Simulations results: Coded caseCC(7,5), 5 4, 2RC CPM � 1 4⁄



Iterative decoding


Coded CPM: EXIT Charts Analysis(same parameters)





Issue #2: ROBUSTNESS



[MCAG15a] M.Messai, G.Colavolpe, K. Amis, and Frederic Guilloud, "Robust Detection of Binary CPMs with unknown modulation index" IEEE Commun. Letters, vol. 19, no.3, pp. 3339-342, March 2015.

[MCAG15b] M.Messai, G.Colavolpe, K. Amis, and F. Guilloud, "Binary Continuous Phase Robust to a Modulation Index Mismatch" IEEE Trans. Commun, vol. PP, September 2015.


MLSE (Trellis) Detector is very sensitive to the Modulation Index



Modulation Index = 0.5(MSK)

Modulation Index = 0.52


Robustness to Modulation Index Mismatch and Phase Noise for a binary CPM

� Problem: � → � + �d� Solution from State of Art: Non Coherent receiver

� Proposed solutions:

Modulation index error �d induce erroneous Cumulative phase state



∑−

=

=∆Ln

iien ah

0

πφ

Unknown To be cancelled

� Cancelling the phase errorfrom the transmitter side

Adequate precoder

� Tracking the cumulated phase error and phase noise at the receiver side

Factor Graph and SPA


Robustness to Mismatched Modulation Index: Receiver Perspective (Binary CPM)

� We consider a transmission over a channel affected by phase noise [BC07]

� In addition to the phase noise, a modulation index error may occur.

� The cumulated phase error and the phase noise are tracked from the

receiver side by

A Factor Graph and the Sum Product Algorithm

using an a-priori on the modulation index probability density function

� Based on the Laurent decomposition.

• We describe the joint a-posteriori probability of the transmitted symbols,

the channel phase, and the modulation index through a factor graph.

• We apply the sum-product algorithm on the resulting graph to compute-

either exactly or approximately-this a-posteriori probability.



[BC07] A. Barbieri, G. Colavolpe, "Simplified soft-output detection of CPM signals over coherentand phase noise channels," IEEE Trans. Wireless Commun., vol. 6, pp. 2486-2496, July 2007.


Laurent Decomposition (= CPM Linearization)

� Based on Laurent representation, the complex envelope of a CPM signal may be exactly expressed:

� In the binary case, most of the signal power is concentrated in the first component, i.e., that associated with pulse 8_0 (�), which is called principal component and the principal pseudo-symbol satisfies

� A very good trade-off between approximation quality and number of signal components is reached.



)(),,(1

0, nTtphats

Q

m nmnm −=∑∑

−

=

α )12(2 )(log1 2 −= − MLQ

nhajnn e παα 1,0,0 −=


Simulation Results(Bluetooth -BR - GFSK)



+ reduced Trellis



Issue #3: MULTIPLE ACCESS




Multiple Accessbased on Frequency Division (FDM -CPM)

� Spectral Efficiency can be increased by reducing the spacing

between two adjacent channels

� But increasing the Interferences

� Factor-Graph based multi-user CPM detector proposed in

[PC10] and extended to the robust case in [MFA15]



[PC10] A. Piemontese and G. Colavolpe, A Novel Graph-Based Suboptimal Multiuser Detector for FDM-CPM Transmissions, IEEE Trans. Wireless Commun., vol. 9, pp. 2812-2819, September 2010.

[MFA15] M. Messai, F. Guilloud, K. Amis, and G.Colavolpe, "Robust Multiuser Binary CPM Detection with Unknown modulation index," Proc. EUSIPCO 2015, Nice.


Simulation Results (MSK, ∆e f/gh,3 or 5 users, CC(7,5))




Increase in number of users:Machine-Type Assumptions



� Short frames (isymbols)

� Sporadic

transmission:

• the probability

that a node is

active is very low

8j≪ 1�� Question: estimate

• Active users?

• Data symbols?


Compressed Sensing Based Multi-User Detection



� SoA: Linear modulations

� Matched filter + Nyquist rate

sample at � �� Solve lm argmin q� � rl ;But Unknown l has length s, and

only one equation!

Using "l is sparse” as an a-priori, can be solved with

CS if iu ≳ sj log wwx

� Introduce diversity iu (antennas or spreading factor for example)

� Then iu equations, but iu y s …

(Joint work also with Prof. Abdeldjalil Aissa El Bey)


Issues for CS -MUD in CPM

� Symbols cannot be sampled at the Nyquist Rate

� Non-linear modulation not fitted to CS

� Laurent Decomposition: Linearization

• But then the waveform does not fulfill the

Nyquist criteria

• Symbols are dependent

• Use Block-wise sparsity




Take-Away Messages

� Many multicarrier waveforms are studied for 5G

• But would a single waveform for all use-cases be

reasonable?

� CPM = an interesting alternative for battery powered

transmitter without recharge facility

� CPM issues are now well investigated:

• Complexity of the coherent receiver

• Robustness w.r.t. modulation index / phase noise

• Multiple Access

� Open Issues: Synchronization, Rayleigh Fading, Channel

estimation in Sporadic Communications




Thank you for your attention

Questions Are Welcome!



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Continuous Phase Modulation: a “New Waveform for 5G ... · Institut Mines-Télécom Reduced State...

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