1

HAP Smart Antenna

George White , Zhengyi Xu, Yuriy Zakharov

University of York

2

HAP smart antenna: Progress update

Optimisation of antenna array beampatterns for cellular coverage

Adaptive HAP multiple beam downlink:• Joint optimisation of user beams• Effect of channel allocation methods

Train tracking from HAPs:• Root MUSIC for DOA estimation • Extended Kalman filtering

3

Should we try to equalise SINR for all users? Should we jointly evaluate weight vectors?

Adaptive HAP multiple beam downlink

X(dB)

Pr(

SIN

R

X)

E.g. Desirable CDF

• One beam per user under power constraint:

What is best strategy for calculating antenna element weights and power distributions for all users?

It would be power-efficient for HAP payload to provide just enough power to satisfy desired SINR of all users.

4

Adaptive HAP multiple beam downlink (2)

Capon beamforming on downlink can mitigate interference for single beam case by steering beam nulls towards co-channel interferers.

E.g.

8x8 square array (half-wavelength spaced omni-directional elements)

Plot represents coverage for one of N user-beams, where N is number of co-channel users.

• Black cross is desired user

• White crosses are co-channel users

5

Adaptive HAP multiple beam downlink (3)

But Capon method not necessarily optimum in multiple beam scenarios. SINRs of users are typically not equal, even with power control (path-loss compensation), because....

User U moves in steps from SPP to ECP.

Interferers stay still

Directivity of user-beam in directionof U drops sharply near interferers

Directivity of Capon beampattern in direction of desired user varies with relative positions of interferers:

6

Adaptive HAP multiple beam downlink (4)

Example: • Desired user U (black cross) nearinterferer B (white cross)• U receives much lower power in this case

Hence channel allocation methodsto ensure spatially separated co-channel users are crucial to thismethod.

Ripple allocation method proposed in [1] and shown to greatly increase capacity for multiple beam Capon downlink. Simple, effective, deterministic.

[1] G. White, J. Thornton, D. Grace, Y. Zakharov and T. Tozer, “Adaptive Beamforming for Communications from High-Altitude Platforms,” submitted IEEE Trans. on Wireless Communications, Feb 2005.

7

Adaptive HAP multiple beam downlink (5)

[2] M. Schubert and H. Boche, “Solution of the Multiuser Downlink Beamforming Problem with Individual SINR Constraints,” IEEE Trans. on Vehicular Technology, Vol. 53, No. 1, Jan 2004

Back to the question:

What is best strategy for calculating antenna element weights and power distributions for all users?

M. Schubert and H. Boche [2] have proposed method which jointly evaluates downlink antenna element weights and power distributions to achieve equalised SINR on adaptive beamforming downlink. Iterative technique based on eigendecomposition of spatial correlation matrix.

We compare:

1) Capon beamforming, no power control: same power in all beams

2) Capon beamforming with power control to compensate for differing free-space path losses.

3) Equalised SINR method

8

Adaptive HAP multiple beam downlink (6)

But is equalised SINR desirable?

Yes, if co-channel users are well separated

No, if they are not

9

Adaptive HAP multiple beam downlink (7)

Equalised SINR witharbitrary channelallocations performspoorly.

With ripple allocation,better than Capon with power control for low probability ofoutage, e.g. SINR>3dB for Pr (outage) <5%

10

Train tracking from HAPs

Goal: Track DOA of multiple trains simultaneously to enable reliable broadband links to be established.

System should:• Cope with motion of trains – changes in speed and direction• Cope with trains crossing and overtaking on adjacent tracks• Cope with shadowing and tunnels• Achieve fast acquisition of new trainsSimple scenario to begin:Train speeds 300km/h (constant)Uncorrelated signals from trains No pilot information

11

Train tracking from HAPs (2)

Example scenario:Root-MUSIC (computationally efficient for large scanning ranges)Tracking using extended Kalman filter (EKF)8x8 array of omni-directional elements

DOA estimates:Can estimate steering angles: elevation and azimuthEstimates will have larger variances towards edge of coverage or with larger numbers of trains

Tracking:Kalman filterPredict current position update with current DOA estimatesCan reduce variance of DOA estimatesCan be extended to linearise relationship between trackedquantity (position, velocity) and measured quantity (angles)

12

Train tracking from HAPs (3)

Root-MUSIC only Root-MUSIC + Kalman Filter

13

Optimisation of antenna array beampatterns for cellular coverage (method)

Generating a ground masking filter to describe the cell footprint (2D Gaussian function)

Transforming the ground masking filter to the angle masking filter

Approximation of a continuous aperture distribution corresponding to the angle masking filter

Space sampling the continuous aperture distribution to elements of the antenna array.

14

Optimisation of antenna array beampatterns for cellular coverage (results)

-35 -25 -15 -5 5 15 25 35

-30

-20

-10

0

10

20

30

3 2 3 2 3 2 3 2 3 2 3

1 4 1 4 1 4 1 4 1 4 1 4

2 3 2 3 2 3 2 3 2 3 2

1 4 1 4 1 4 1 4 1 4

2 3 2 3 2 3 2 3 2

1 4 1 4 1 4 1 4

3 2 3 2 3

4 1 4 1 4 1 4 1 4 1 4 1

2 3 2 3 2 3 2 3 2 3 2

4 1 4 1 4 1 4 1 4 1

2 3 2 3 2 3 2 3 2

4 1 4 1 4 1 4 1

3 2 3 2 3

X Distance (km)

Y D

ista

nce

(km

)

10 12 14 16 18 20 22 24 26 28 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CIR (dB)

Cov

erag

e (%

)121 aperture antenna

average cell using 424 antenna array

worst cell using 424 antenna array

best cell using 424 antenna array

15

Future work

Train tracking (to continue)

Beemforming for non-circle cells