Base Stations, Antennas and Fibre Everywhere?

Nicola Marchetti

CPqD, Campinas, Brazil

November 6, 2014

Outline

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• Massive MIMO & FBMC • Dense Cell Deployments • Backhauling Mobile Systems with PON

Outline

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• Massive MIMO & FBMC • Dense Cell Deployments • Backhauling Mobile Systems with PON

• Massive MIMO: a multiuser system where M >> K

What is massive MIMO and why do we need it?

Base station

MT1

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MTK

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H11

HKM

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M

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• CMT modulation

Cosine Modulated Multitone (CMT)

(a) Spectra of baseband data streams (black) and vestigial side band (VSB) portion of each (other colors). (b) CMT spectrum consisting of modulated versions of the VSB spectra of the baseband data streams. VSB signals are modulated to the subcarrier frequencies f0, f1, · · · , fN−1.

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• The received signal at the base station from the ℓth user

• The received signal from all the users

• Matched Filter (MF) receiver

• MMSE receiver

CMT Application to Massive MIMO

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CMT Application to Massive MIMO

• Matched filter (MF) receiver

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CMT Application to Massive MIMO

• MMSE receiver

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CMT Application to Massive MIMO

• With the assumption of having a flat channel impulse response in each subcarrier band, SINR at the output of the MF and MMSE can be derived as

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• The channel responses at different received antennas will be averaged out through the MF and MMSE linear combining

Self-equalization

Channel gain across each subcarrier band will be nearly equalized through linear combining

𝑓

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• Signal enhancement through linear combining leads to the same results for both OFDM and CMT systems. However, CMT offers the following advantages over OFDM:

– More flexible carrier aggregation

– Higher bandwidth efficiency

Comparison with OFDM

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• Single user case

Numerical Results

(a) and (b) compare the signal to interference ratio (SIR) of the MF linear combining technique for the cases of 32 and 64 subcarriers, respectively, for different number of receive antennas, N.

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• Single user case

Numerical Results

SINR comparison between MMSE and MF linear combining techniques in the single user case with L = 32, when the user’s SNR at the receiver input is −1 dB for two cases of N = 128 and N = 32.

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• Multiuser case

Numerical Results

(a) and (b) depict the SINR comparison between MMSE and MF linear combining techniques when we have 6 users and N = 128 receive antennas for two cases of 64 and 32 subcarriers, respectively.

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Pilot Contamination Problem

• Present in TDD multi-cellular Massive MIMO networks

• Users of different cells cannot use orthogonal pilot sets

• Channel estimates at the base stations will be contaminated by the MTs using the same pilot sets located in adjacent cells

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Pilot Contamination Problem

• The received signal at base station j

• After correlating the received training symbols with the set of pilot sequences at the BS j

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Pilot Decontamination

• CMT has a blind channel equalization property

• Blind equalization technique of [1] can be extended and utilized in multicellular massive MIMO networks to tackle pilot contamination problem

[1] B. Farhang-Boroujeny, “Multicarrier modulation with blind detection capability using cosine modulated filter banks,” IEEE Transactions on Communications, vol. 51, no. 12, pp. 2057–2070, 2003.

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Pilot Decontamination

• We initialize the algorithm with matched filter tap-weight vector using contaminated channel estimates

• We update the combiner tap-weights using

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Simulation Results

SINR comparison of our proposed blind tracking technique with respect to the MF and MMSE

detectors having the perfect CSI knowledge.

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Simulation Results

Eye pattern of the combined symbols using the proposed blind tracking technique.

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Outline

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• Massive MIMO & FBMC • Dense Cell Deployments • Backhauling Mobile Systems with PON

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Network and Backhaul Densification

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More ASE and Less Power

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Effect of LOS/NLOS Propagation on ASE and EE of Small-Cells

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Area Spectral Efficiency

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Power

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Energy Efficiency

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A stochastic geometrical study of LOS/NLOS propagation in dense small cell deployments

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A stochastic geometrical study of LOS/NLOS propagation in dense small cell deployments

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Outline

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• Massive MIMO & FBMC • Dense Cell Deployments • Backhauling Mobile Systems with PON

Group Assured Bandwidth for Mobile Base Station Backhauling

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Bandwidth Types in XG-PON

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Group Assured Bandwidth

•Group assured bandwidth uses the resources assured to the mobile operator more efficiently.

•Group assured bandwidth allows mobile operators to make use of the properties of statistical multiplexing, enabling the same QoS for a smaller amount of assured traffic.

• It does this independently of the rest of the traffic on the PON (possibly competing mobile operators).

•Since this type of bandwidth is valuable, new interesting business models can arise.

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Group Transmission Containers in XG-PON

• To schedule group assured bandwidth, the OLT must be able to differentiate different groups of connections.

• For that we propose the grouped T-CONT (gT-CONT).

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Algorithm Development - gGIANT

• In GIANT [Han2008]: – Each T-CONT has a timer that is decreased every upstream frame.

– Each T-CONT has a byte counter that dictates how much bytes it can transmit.

– If the buffer of T-CONT is not empty when the timer reaches zero, bandwidth is assigned to it.

– When the timer reaches zero the byte counter is refreshed

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XG-PON Simulator

http://sourceforge.net/projects/xgpon4ns3/

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Experiment Description

• To demonstrate the benefits of group assured bandwidth, we conducted two experiments.

• In both experiments: – PON with 16 ONUs, each ONU with one T-CONT – Each T-CONT with 140 Mbps of individual assured bandwidth – Variable group size – Poisson Traffic

• In one experiment to illustrate more homogenous traffic, the load of all ONUs was increased equally.

• On the other, to illustrate more heterogeneous traffic, the load of one ONU is changed, while the other are kept constant at 120 Mbps.

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Average Delay Under Equal & Unequal Loads

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Lost Packets Under Equal &Unequal Loads

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Related Publications – Massive MIMO & FBMC

• A. Farhang, N. Marchetti, F. Figueiredo, J.P. Miranda, “Massive MIMO and Waveform Design for 5th Generation Wireless Communication Systems”, International Conference on 5G for Ubiquitous Connectivity (5GU), Nov. 2014

• A. Farhang, N. Marchetti, L. Doyle, B. Farhang-Boroujeny, “Filter Bank Multicarrier for Massive MIMO”, IEEE Vehicular Technology Conference (VTC), Sep. 2014

• A. Farhang, A. Aminjavaheri, N. Marchetti, L. Doyle, B. Farhang-Boroujeny, "Pilot Decontamination in CMT-based Massive MIMO Networks", International Symposium on Wireless Communication Systems (ISWCS), Aug. 2014

• F. Bentosela, N. Marchetti, H. Cornean, “Influence of environment richness on the increase of MIMO capacity with number of antennas”, IEEE Transactions on Antennas and Propagation, vol. 62, no. 7, pp. 3786-3796, Jul. 2014

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Related Publications – Small Cells

• C. Galiotto, N. Pratas, N. Marchetti, L. Doyle, “A Stochastic Geometry Framework for LOS/NLOS Propagation in Dense Small Cell Networks”, IEEE International Conference on Communications (ICC), (submitted)

• C. Galiotto, I. Gomez-Miguelez, N. Marchetti, L. Doyle, “Effect of LOS/NLOS Propagation on Area Spectral Efficiency and Energy Efficiency of Small Cells”, IEEE Global Telecommunications Conference (GLOBECOM), Dec. 2014 (accepted for publication)

• C. Galiotto, N. Marchetti, L. Doyle, “The Role of the Total Transmit Power on the Linear Area Spectral Efficiency Gain of Cell-Splitting”, IEEE Communications Letters, vol. 17, no. 12, pp. 2256-2259, Dec. 2013

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Related Publications – Optical/Wireless

• J. Arokkiam, X. Wu, K. Brown, C. Sreenan, P. Alvarez, M. Ruffini, N. Marchetti, L. Doyle, D. Payne, “Design, Implementation, and Evaluation of an XG-PON Module for ns-3”, Simulation Modelling Practice and Theory, Elsevier (submitted)

• P. Alvarez, N. Marchetti, D. Payne, M. Ruffini, “Backhauling Mobile Systems with XG-PON Using Grouped Assured Bandwidth”, European Conference on Networks and Optical Communications (NOC), Jun. 2014

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Publications on 5G in General & Other Aspects

• N. Marchetti, “Towards the 5th Generation of Wireless Communication Systems”, ZTE Communications, accepted for publication, to appear

• I. Macaluso, C. Galiotto, N. Marchetti, L. Doyle, “A Complex Systems Science Perspective on Cognitive Networks”, Systems Science and Complexity, Springer, accepted for publication, to appear

• F. Paisana, N. Marchetti, L. DaSilva, “Radar, TV and Cellular Bands: Which Spectrum Access Techniques for Which Bands?,” IEEE Communications Surveys and Tutorials, vol. 16, no. 3, pp. 1193-1220, Aug. 2014

• I. Gomez-Miguelez, E. Avdic, N. Marchetti, I. Macaluso, L. Doyle, “Cloud-RAN platform for LSA in 5G networks - tradeoff within the infrastructure,” International Symposium on Communications, Control, and Signal Processing (ISCCSP), May 2014

• F. Paisana, J.P. Miranda, N. Marchetti, L. DaSilva, “Database-aided Sensing for Radar Bands,” IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Apr. 2014

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