Advanced radio access solutions for the new 5G requirements

Advanced radio access solutions for the new 5G requirements

Soumaya HamoudaAssociate Professor, University of Carthage

Tunis, [email protected]

IEEE Summit – 5G in Future Africa. May 3th, 2017 – Pretoria, South Africa

IEEE 5G Summit, SA 2017 Dr. Soumaya Hamouda 2

Identified 5G scenarios and services

Multimedia services will require more and more bandwidth

Mobile cloud services are growing rapidly

Augmented /virtual reality services will become common

Social networking services will be expanded with rich contents

M2M devices proliferate and high data rate M2M service will grow

Personalized services will be integrated into daily life

Dr. Soumaya Hamouda 3

Devices’ number increase

X 10


Wireless networks legacy and new challenges

3G (HSPA)

Voice

WCDMA

2G: Voice

3G: Data



3.9G (LTE)

2G: Voice

3G: Data

4G: Mobile data

OFDMA



2G: Voice

3G: Data

4G: Mobile data

4G (LTE-A)

LTE-A (100 MHz = 5 x 20 MHz)

Carrier Aggregation

OFDMA

Massive MIMO

MU-CoMP



2G: Voice

3G: Data

4G: Mobile data

4G (LTE-A)

PicoCell

FemtoCell

Relay

HetNet

eICIC



4G (LTE-A)

Ultra Dense Small Cells

New Radio Access Challenges

9


• Accommodate traffic and bandwidth explosion– Traffic volume will be increased 1000 times (Video traffic portion will be major)

• Handle the interference explosion and optimize the radio resources allocation– Enhance the interference coordination between the ultra-dense small cells– Manage the hotspots of traffic and high mobility

• Accommodate signaling explosion – Mobility signaling explosion by Small Cell– Massive connectivity due to increased number of devices– Bearer control caused by ‘always-on-apps’ services– Higher paging complexity caused by heterogeneous wireless and wire-line

accesses

• Satisfy operator‘s requirement of profitable network infrastructure– Legacy network can hardly reduce CAPEX and OPEX– Need to accommodate new services in the same physical network


5G requirements

4G4G

5G5G

R1

R2

R3

R4R5

R6

R7

Mapping new technologies with 5G requirements

Cloud-RAN and H-CRAN

mmWaves

Full duplex radio

New multiple access technique (SCMA) or new waveforms (f-OFDM, FBMC…)

Edge Caching

D2D Communication

Massive MIMO and beamforming

R7R6R5R4R3R2R1Enabling technologies

Massive MIMO and beamforming

New multiple access technique (SCMA) or new waveforms (f-OFDM, FBMC…)

Full duplex radio

mmWaves

Cloud-RAN, H-CRAN and SCNs

D2D Communication

Edge Caching


Enhancing Energy Efficiency with CRAN/ H-CRAN

RRH

…

RRH

RRH Fiber links

Remote Radio Header

BBU pool

BBU


5G – GW Data plane

5G – GW Data plane

BBU pool


Virtualized Control plane



• With a virtualized control plane, the C-RAN/ H-CRAN help:– better coordinate the inter-cell interference– better coordinate the MU-CoMP– enhance the traffic balancing– coordinate the traffic offloading

• But also:– Better handle the Small Cell sleep and wake up– A few number of VMs is processing for the whole

network.

Improve SE and EE

Reduce operational expenditures (OPEX)


D2D reduces latency, increases throughput and batteries life, but…!

Cellular Communication

D2D Communication

How to increase D2D communications while respectingthe quality of service of cellular users’ communications?


Proposed noise-rise based D2D Communication mode selection

0

50

100

150

200

0

0.2

0.4

0.6

0.8

1

-80

-60

-40

-20

0

20

1/d (km)D (km)

Rec

eive

d si

gnal

stre

ngth

(dB

)

Optimization problem:

( )ii

ii R

DdAI 10log=

( )

N

RDdAN

NIN i

i

i

ii

10log+=

+=η

Noise rise :

Interference impact :

( )∑=

+DU

ii

11logmax

2γ

Threshηη <s.t

E.Fakhfakh, S.Hamouda, S.Tabbane, «Joint Mode Selection and Power Allocation for Better Traffic offloading in D2D Communications», accepted in Annals of Telecommunications Journal, Springer, en March 2017


17

Total system throughput

10 20 30 40 50 60 70 805

10

15

20

25

30

35

40

45

50

Tota

l thr

ough

put o

f D2D

and

cel

lula

r use

rs (M

bps)

Number of users in cell

Proposed Selection SchemeCommon Selection Scheme


A good tradeoff between the number of D2D users and the system radio capacity

10 20 30 40 50 60 70 803

4

5

6

7

8

9

10

11

12

13

Noi

se ri

se (d

B)

Number of users in the cell


Noise rise


18

Impact of the noise rise threshold

4 6 8 10 12 14 16 18 2015

20

25

30

35

40

Tota

l thr

ough

put o

f D2D

and

cel

lula

r use

rs (M

bps)

Noise rise threshold (dB)



Impact of the mobiles’ distribution

Hotspots at the cell edge Hotspots at the cell center0

10

20

30

40

50

60

70

80

Num

ber o

f D2D

use

rs

Proposed Selecion SchemeCommon Selection Scheme

D2D mode selection is more efficient when it is applied at the cell edge.


Cognitive radio for D2D communications

Cellular Communications

Secondary network

Considering the underlay mode, how to enhance a D2D communication performance while reducing the interference caused in the network?

D2D

D2D

Primary network


Proposed bargaining games for D2D communications

S. Fourati, S. Hamouda, B.T.Maharaj, « Bargaining Solutions for Energy Efficient and Fair Power Allocation in Cognitive D2D Communications », International Journal of Computer Applications (IJCA), Vol 155 (6), pp 24-31, December 2016

Cooperative Bargaining Games G={I,S,U}• Players: the SUs Tx (D2D TX-UE)• Strategy: the SUs’ transmit powers set• New objective function

Constraint taking into account PU’s quality of service.

∑=

− −−=N

iiimiiiii phpRNppU

1)log(),( αβ

Optimization problem (Non Cooperative game):

( )max ,i

i i ipU p p−

miniγ γ≥Thres

N

iiim Iph ≤∑

=1

max0 , 1,...,ip P i N≤ ≤ =s.t and


21

Pareto-optimal convergence

Proposed bargaining games for D2D communications

The Utilitarian Game Solution achieves the best EE

Energy efficiency

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

utopia point

utility u1

utili

ty u

2

NEUSNBSESKSBS

NE US NBS ES KSBS0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Ene

rgy

Effi

cien

cy(M

b/W

)


Saving energy with multi-cast caching

How to download the required content at the edge while reducing the power cost?

Backhaul

Graph Theory

S. Mrad, S. Hamouda, H.Rezig, «Graph Theory Based Multicast Caching for Better Energy Saving in Dense Small Cell Networks », accepted in IEEE International Wireless Communications and Mobile Computing Conference (IWCMC’17), Spain, June 2017


Saving energy with multi-cast caching

Energy Efficiency EE gain boundary

A tradeoff between the number of the small cells involved in the graph and the cache size in order to guarantee an efficient energy system.

Thank you

Documents

Advanced radio access solutions for the new 5G requirements