Capacity-fairness trade-off in small cells networks using … · Capacity-fairness trade-off in small cells networks using coordinated multi-cell transmission Virgile GARCIA , Nikolai

Capacity-fairness trade-off in small cells networksusing coordinated multi-cell transmission

Virgile GARCIA , Nikolai LEBEDEV, Jean-Marie GORCEADR Self-Net (INRIA/Bell Labs France)INRIA SWING / CITI Laboratory, INSA-Lyon

Supelec – 2010/01/20

General context

• 4th Generation (LTE-A, WiMax)

2GARCIA Virgile, Séminaire Supelec,

2010/01/20 - Fairness&CoMP

General context

• (Small) Cellular networks

– Getting denser

– Getting heterogeneous

– Want more traffic

– Want to be Green

Interference Management needed !



General context

• Self-Optimization Networks

– Interaction between cells

– “Plug and Play”

– Auto configurations

– Aware of its environment

Intelligent network



General context

• Virtualization of cells

– “Mutualisation” of small cells

– Objectives:

• Follow an user through the network

• Reduction of (hard) Hand-over

• Interference coordination



Outline

• General context

• Resource allocation problem

• Multi-cell Processing (CoMP, MCP, v-MIMO…)

• Fairness & CoMP

• Models and Simulations results

• Conclusion



Resource Allocation

• WiMax & LTE : OFDMA-based

– Wide-band spectrum (up to 5*20MHz)

– Adaptive:

• Modulation

• Block num.

– Diversity:

• Frequency

• Users



Resource Allocation

• Several views of the problem

• Exponential complexity (NP-hard)



Resource Allocation

• Signal to Noise and Interference (SINR)

– Small cells High interferences

– Unpredictable interferences

– Shadowing and fading effects

– Transmit Power

– …

Each user experience its SINR



How to share resources?

• Information needed– Channel State Information ?

– Users requirements

– Available bandwidth

– Impact of my transmission for other cells?

• Need a sub-optimal algorithm– Is this allocation ‘good’?

• Efficient?

• Fair?



Our approach

• No individual power control

– Power adaptation per cell

– As a first step, constant power

• Full bandwidth allocation (saturated)

Make interference stable over time

Simpler distributed allocation

• Multi-cell processing

– To manage edge-cell users



Capacity

• Depends of

– Allocated spectrum

– User SINR

• Different spectral efficiency used

– Ergodic capacity (Shannon)

– Outage capacity

– Modulation rates

– …



Ci = wi * a(γi)

Fairness

• How to quantify fairness?

– Jain’s index *Jain’98+

– α utility [Mo ACM/Trans.Netw.’2000+

– Min-max ratio

– Variance…

• How “fair” are they?

• Is the fairness worthy?

– Trade-Off between Fairness and Efficiency



Double-Objective

• Jain/Capacity trade-off (Reuse1, no CoMP)



Pareto optimality

• Jain :

– Proportional to (Cell capacity)² &(Cell variance)-1.

• For given possible values of C, we look for maximizing J.

– Equal to minimizing Variance.

– Constrained by resource total, cell capacity, positivity of allocated resources.

Lagrangian, with Kuhn-Tucker conditions



Alpha-fairness

• α-fairness is a way to allocate resource, not a measure.

• α-fair allocation in a cell:



Alpha-fairness

• α-fairness considers all users independently.

– Note: Ci = 0 impossible for α≥1.

• For given SINRS, it can be proved that the solution is:

• Advantage:

– Easier to compute

– Easier to parameterize



Alpha vs. double-objective

• α-fairness utility function is not Jain’s one.

• Closeness of the approach:



Outline

• General context




• Conclusion



Cell coordination

• Hard coordination:– Frequency planning (Reuse 1-3, FFR …)

• Need control

• Need maintenance

• Hardly adaptive

• Load balancing

• Dynamic frequency sharing

• Multi-cell processing [Tse Eurasip’08, GesbertICC’08+



Coordinated multiple points (CoMP)

• Key feature for LTE-A

– Objective:

• Extends coverage

• Guarantee QoS

• Not willing to increase much the peak data-rate

• Improve average throughput

• Needs:

– Cooperation between cells

– Synchronization



Coordinated multiple points (CoMP)

• Simple Alamouti scheme– Standard MISO : Matrix A

– No CSIT needed

• Macro-diversity– Strong against canal perturbations

• Objective: find the bestnumber of antennas.– Improve SINR

– Consume several resources



CoMP – Nx1 Alamouti’s SINR

• Gain in useful power and interference avoidance.• Other MCP techniques can improve SINR or capacity, but need CSIT or multiple antennas at Rx.• Without power control, interferences are stable.

;

γN : SINR for N antennas cooperation



Antennas association



SINR estimations

Resource allocation

BS Associations

Distributed measure (at mobile)

Distributed decision (at mobile or BS)

Semi-distributed algorithm (BS and/or coordinator)

Antennas association

• Mobile can sense the power SINR for it’s base station and also the power signal received from neighbor stations

• Possible since mobile already sense for handovers• Mobile at location ‘x’ can compute the expected γ(x) for different antennas association and deduce the lowest consumption method :

• Resource cost for a simple point-to-point link : w1(x)• Resource cost for a 2x1 cooperation : 2*w2(x)



Outline

• General context




• Conclusion



Parameters

• Cell radius: 100m

• Central frequency: 3.5GHz

• Bandwidth: 20MHz

• Transmit power+gain: 40dBm

• Thermal Noise: -174dBm/Hz

• Path-Loss exponent: 3.8

• Shadowing σ: 10dB (25m decorralation)



1D model, Uniform capacity (α→∞)

• Continuous model (i.e. mobiles are everywhere).• 1 or 2 antennas selection possible. Find the optimal switching point ε.

1x1 mode (Reuse 1) 2x1 mode (CoMP)



Uniform capacity: Simulations

29GARCIA Virgile, CITI Laboratory, INRIA, INSA Lyon, France.

INRIA/Alcatel-Lucent SON Seminar, Rocquencourt, 2009/10/20

No CoMP2x13x14x1

GARCIA Virgile, CITI Laboratory, INRIA, INSA Lyon, France. COGIS'09, Paris, 2009/11/17

29

CoMP mode

Uniform capacity: Channel effects

• Gain: MCP’s uniform capacity over FFR’s.

LOS : 3GPP urban Lign-Of-Sign parameters nLOS : 3GPP urban no-LOS parameters



What for other fairness objective?

• Is the uniform capacity criterion still true?

• How to choose the coordination

– To be α-fair?

– To be only “fairer”?

– To be only more efficient?



?

BS association

• Trade-off between gain in SINR and loss in available spectrum


2010/01/20 - Fairness&CoMPα=1

CoMP and Fairness



Conclusion

• Smart interference coordination is needed.

• CoMP provides:

– Usefulness of interference

– SINR improvement of cell-edge users

– Fairer and/or more efficient allocation

– Self-organization

• no frequency plan needed

• Automatically adaptive



Thank you for your attention!

• Email: [email protected]• [V Garcia, N Lebedev, and J-M Gorce. Multi-cell processing for uniform capacity

improvement in full spectral reuse system. In COGnitive systems with Interactive Sensors, Paris France, 11 2009. SEE, IET.]

• [V Garcia, N Lebedev, and J-M Gorce. Capacity-fairness trade-off in small cells networks using coordinated multi-cell transmission. Submitted to WiOpt 2010]

Questions ?



GARCIA Virgile, Séminaire Supelec, 2010/01/20 - Fairness&CoMP

36

Lagrangian

GARCIA Virgile, Séminaire Supelec, 2010/01/20 - Fairness&CoMP

37

Minimization of Variance:

Uniform capacity: Simulations

38GARCIA Virgile, CITI Laboratory, INRIA, INSA Lyon, France.

INRIA/Alcatel-Lucent SON Seminar, Rocquencourt, 2009/10/20

No CoMP2x13x14x1

Reuse1Reuse4

GARCIA Virgile, CITI Laboratory, INRIA, INSA Lyon, France. COGIS'09, Paris, 2009/11/17

38

FFR4 mode

CoMP mode

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Capacity-fairness trade-off in small cells networks using … · Capacity-fairness trade-off in small cells networks using coordinated multi-cell transmission Virgile GARCIA , Nikolai