Resource Pooling via Dynamic Spectrum-level Slicing ...

Resource Pooling via Dynamic Spectrum-level Slicing across Heterogeneous Networks Anteneh A. Gebremariam1 , Mainak Chowdhury2 , Andrea Goldsmith2 Fabrizio Granelli1 1 University 2 Stanford

of Trento, Trento, Italy

University, California, USA

IEEE CCNC’17, Jan. 8-11

Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan.Elsewhere) 8-11Networks 1 / 16

Outline

1

Introduction Motivation

2

Problem Statement

3

Solution Dynamic Spectrum-level Slicing Results

4

Summary


Introduction By 2020 the global mobile data traffic is expected to increase by eightfold as compared to 2015

Source: Cisco


Motivation (1/3) The main contributing factors include, video content dominance, M2M, IoT, etc.


Motivation (2/3)

We need flexible and adaptable wireless network to to solve the aforementioned issues Network programmability and virtualization are the key enablers

Wireless network virtualization is sharing of physical infrastructure among multiple mobile network operators (MNOs) It also enables dynamic sharing of radio resources to further reduce CapEX and OpEX of the network


Motivation (3/3) Steps to enable sharing of radio resources Abstraction of radio resources Slicing radio resources


Problem Statement

The implementation of dynamic sharing of radio resource among VNs is computationally demanding: How often should we perform spectrum sharing in order to obtain a reasonable preformance gain? How far away form the PHY infrastructure should the network function module reside while fulfilling the time stringent operation is still unclear.


Dynamic Spectrum-level Slicing (1/3)

VRC responsible for taking different actions based on the state of the network VN is a segment of a network that owns a slice of radio resources Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan.Elsewhere) 8-11Networks 8 / 16

Dynamic Spectrum-level Slicing (2/3)

Traffic Load Ij P

Lkj =

i=1

i nRB kj

ψ(k−1)j

,

j ∈ {1, 2}, k ∈ N

(1)


Dynamic Spectrum-level Slicing (3/3) The Utility Functions Ukj = ln(ψkj ), and ψkj Ukj = . 1 + ψkj

(2) (3)

The Optimization Problem X

maximize ψkj

subject to

ln(ψkj ), j ∈ {1, 2}, k ∈ N

j

X

ψkj = NRB ,

j

ψkj ≥

(4) Ij X

i nRB , kj

i=1

ψkj ≥ 0. Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan. Elsewhere) 8-11 Networks 10 / 16

Results (1/5) The system dynamics changes every TTI Channel conditions Network traffic distribution UE location 700

Macro Base Station (MBS) Small Cell (SC) MBS UEs SC UEs

600 500 Y[m]

400 300 200 100 0 0

200

400

X[m]

600

800

1000

Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan. Elsewhere) 8-11 Networks 11 / 16

Results (2/5) Parameter settings Parameter System layout System Bandwidth Operating frequency Resource Partitioning Noise PSD Shadowing Noise figure Penetration Loss MSB transmit power SC transmit power MBS antenna gain SC antenna gain Beam pattern gain 3GPP path loss model for MSB 3GPP path loss model for SC

Value 5 omnidirectional BSs W = 20 MHz 2GHz Proportional fair −174dBm/Hz σ = 8dB 9dB 20dB 46dBm 37dBm 15dBi 5dBi 0dBi (ominidirectional antenna) r PL(r ) = 128.1 + 37.5 · log 1000 r PL(r ) = 140.7 + 36.7 · log 1000

Source: 3GPP TR 36.814 V9.0.0 (2010-03)


Results (3/5)

Key Performance Indicators %optEvents =

%droppedPkts =

Ntrigger . Ntraffic

SSLS DSLS Ndropped − Ndropped

Ntx

(5)

.

(6)


Results (4/5)

Percentage of Optimization Events (%optEvents)

100

U(ψkj ) = ψkj /(1 + ψkj ) − (MT) U(ψkj ) = ln(ψkj ) − (MT) U ∗ (ψkj ) = ψkj /(1 + ψkj ) − (HT) U ∗ (ψkj ) = ln(ψkj ) − (HT)

80

60

40

20

0

5

10 15 Round of Simulations (1 round = 10RFs)

20

The optimization trigger events occur on average 40% and 60% of the cases, i.e. every 2 TTIs Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan. Elsewhere) 8-11 Networks 14 / 16

Results (5/5)

Percentage of Dropped Packets (%droppedPkts)

10

U(ψkj ) = ψkj /(1 + ψkj ) − (MT) U(ψkj ) = ln(ψkj ) − (MT) U ∗ (ψkj ) = ψkj /(1 + ψkj ) − (HT) U ∗ (ψkj ) = ln(ψkj ) − (HT)

8 6 4 2 0

1

2

3

4 5 6 7 RF index (1RF = 10 TTIs)

8

9

10

The percentage of dropped packets in the case of SSLS with respect to DSLS Anteneh A. Gebremariam, Mainak Chowdhury,Resource Andrea Goldsmith, Pooling via Fabrizio DynamicGranelli Spectrum-level (Universities Slicing IEEE of across Somewhere CCNC’17, Heterogeneous and Jan. Elsewhere) 8-11 Networks 15 / 16

Summary

A DSLS algorithm is proposed for HetNets based on dynamically sharing spectrum among the coexisting VNs. How often we perform the optimization events gives an insight in deciding where to put the virtual network functions (e.g., DSLS module). DSLS out performs the SSLS reducing the amount of dropped packets Outlook Future developments will focuses on providing a more realistic user traffic model to accurately compute the inter-arrival time between optimization trigger events.
