Mohsen Jamalabdollahi, Reza Zekavat

2

Cities in the Equator
Transmission Line Needed
3

LEO Satellite Networks
4

Ground Contact/Energy Transmission Period
International collaboration for SSP à Worldwide Ground station implementation
•   Total satellite ground contact
•   Total ground contact period with sunlight
•   Average path loss of wireless power transmission
•   Difference in impact comparing SSO LEO, MEO, GEO;
Distributed ground station coverage area
SSO = Sun-synchronous Orbit (low-earth orbit zone)
MEO = medium earth orbit
GEO = geosynchronous orbit
5

LEO Implementation
Pros
•   Lower altitudes à lower power loss
•   Lower transmission power per unit à Less Environmental Impact
•   Higher Number of Satellites à Higher reliability
•   Lower Cost of Development and Launching
Cons
•   LEO has 10 to 15mins contact period per ground stations
à Multiple Ground Stations Needed à Handoff Process Needed
•   LEO may use multiple satellite transmission à Synchronization Needed
•   International Cooperation and Unified Policy;
•   Routing or Battery Storage if a cluster is not in ground station field of view;
Applications:
SSP to remote and local area
6

—
—
—
—
—
S. T. Guh, S. A. Zekavat and O. Abdelkhalik, “Space-based Solar Power transfer via LEO satellites network: Doppler Effect Analysis”, IEEE Trans. on Aerospace and Electronic Systems , vol. 51, no. 1, Jan.
2015.
S. T. Guh, S. A. Zekavat, “Space solar power orbit design and cost analysis,” proceedings Recent
Advances in Space Technologies (RAST), 2015 7 th International Conference on , 16-19 June 2015, pp.
753 – 758, Istanbul, Turkey.
S. T. Guh and S. A. Zekavat, “Space-based Solar Power via LEO Satellite Networks:
Synchronization Efficiency Analysis,” proceedings IEEE Aerospace Conference , Big Sky, MT, March
03-09, 2013.
S. T. Guh, S. A. Zekavat, and O. Abdlekhalik, “Space-Based Wireless Solar Power transfer via a network of LEO satellites: Doppler Effect Analysis” proceedings IEEE Aerospace Conference , Big
Sky, MT, March 03-09, 2012 .
S. A. Zekavat, and O. Abdlekhalik, “An Introduction to Space-Based Power Grids: Feasibility Study,” proceedings, IEEE Aerospace Conf.
, Big Sky, MT, Mar. 06-12, 2011 .

LEO Implementation: Direct Transmission
Multi-Satellite Synchronization?
•   Different Doppler
•   Different distance to ground
8

0
-0.5
-1
1
0.5
0
-0.5
-1
3
1
0.5
0
-0.5
-1
1
0.5
2
1
0
-1
-2
-3
Time Synchronization s
T
( t ) =
m s m
( t − τ m
)
—
Time offsets:
{ }
K m = 1
-1
1
0.5
0
-0.5
-1
3
-1
1
0.5
0
-0.5
1
0.5
0
-0.5
0
-3

1
0.5
0
-0.5
-1
1
0.5
0
-0.5
-1
1
0.5
0
-0.5
-1
3
2
1
0
-1
-2
-3
Frequency Synchronization r
T
( t ) =
m e j 2 π ( f c
+ Δ f m
) t
—
Frequency offsets:
-1
1
0.5
0
-0.5
1
0.5
0
-0.5
-1
1
0.5
0
-0.5
-1
3
2
1
0
-1
-2
-3
{ }
K m = 1

Idea !
◦
Design a proper training waveform for time synchronization which
–
–   Handles multi-satellite scenario
Could be easily detectable
OFDMA [1]
◦
Use the detected waveform to estimate frequency offset which
–   Handles the unknown channel Impulse response
–   Computationally efficient
RLS [2]
[1] M. Jamalabdollahi and S. A. R. Zekavat, "Joint Neighbor Discovery and Time of Arrival Estimation in
Wireless Sensor Networks via OFDMA," Sensors Journal, IEEE, vol. 15, pp. 5821-5833, 2015.
[2] M. Jamalabdollahi and S. Salari, "RLS-based estimation and tracking of frequency offset and channel
coefficients in MIMO-OFDM systems," Wireless personal communications, vol. 71, pp. 1159-1174, 2013.

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

System Model
—
Received signal by target (power station at earth or the leader satellite):
Channel impulse response (CIR)
Frequency offset (FO)
Time offset r
T
( t ) =
m e j 2 π Δ f m t h m s m
( t − τ m
) + ν
—   s ( t satellite
) m-th
( t )

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

Time Synchronization
—
Allocating a set of OFDMA subcarriers to the m-th satellite: s m
( kT s
) =
p ∈ κ m e j 2 π p Δ fkT s
, 1 ≤ k ≤ N
—
—
N = κ
Δ f =
1
NT s

Time Synchronization
)𝒔 (1,1) (𝑁 − 𝑛)/
∗
)𝒔 (1,𝑁 𝑠
)
⋮
(𝑁 − 𝑛)/
∗
|.|
|.|
τ ˆ m
= arg k max
⎧
⎨
⎩
p ∈ κ m s m
H r k : k + N − 1
⎫
⎬
⎭ argmax 𝑘 ∗
1
)𝒔 (2,1) (𝑁 − 𝑛)/
∗
)𝒔 (2,𝑁 𝑠
)
⋮
(𝑁 − 𝑛)/
∗
)𝒔 (𝑀,1) (𝑁 − 𝑛)/
∗
)𝒔 (𝑀,𝑁 𝑠
)
⋮
(𝑁 − 𝑛)/
∗
|.|
|.|
|.|
|.| argmax
⋮ argmax 𝑘 ∗
2 𝑘 ∗
𝑀

Time Synchronization

—
G
PAPR
= max s k
( m )
1
N
k s k
( m )
2
2
N s
N s
= 1, G
PAPR
= 0 dB
N s
= 2, G
PAPR
= 3dB
N s
= 4, G
PAPR
= 6dB
N s
= 8, G
PAPR
= 9dB
N s
= 16, G
PAPR
= 11dB

Weighted OFDMA waveform
—
Weighted OFDMA waveform s m
( kT s
) =
p ∈ κ m w ( p m ) e j 2 π p Δ fkT s
, 1 ≤ k ≤ N
( m ) c k
( m ) =
= arg w
( max m )
{ c (
N m )
∏ p ∈ κ m
− 2 c
1 :
( m
N
)
− 1
( w ( p m ) s m
)
H w ( p m ) r k : k + N − 1
− γ G
PAPR
}
G
PAPR
= max s k
( m )
1
N
∑ k s k
( m )
2
2

Weighted OFDMA waveform
)𝑤
(1)
1 𝒔 (1,1) (𝑁 − 𝑛)0
∗ τ ˆ m
= arg max
⎧
⎪⎩
∏ p ∈ κ m
( w ( p m ) s m
)
H w ( p m ) r k : k + N − 1
⎫
⎪⎭
)𝑤
(1)
1 𝒔 (1,1) (𝑁 − 𝑛)0
∗
)𝑤
(1)
𝑁 𝑠 𝒔 (1,𝑁 𝑠
)
⋮
(𝑁 − 𝑛)0
∗
)𝑤
(2)
1 𝒔 (2,1)
)𝑤
(2)
𝑁 𝑠 𝒔 (2,𝑁 𝑠
(𝑁 − 𝑛)0
∗
)
⋮
(𝑁 − 𝑛)0
∗
1/𝑤
(1)
1
1/𝑤
(1)
𝑁 𝑠
1/𝑤
(2)
1
1/𝑤
(2)
𝑁 𝑠
|.|
|.|
|.|
|.| argmax argmax 𝑘 ∗
1 𝑘 ∗
2
)𝑤
(𝑀)
1 𝒔 (𝑀,1) (𝑁 − 𝑛)0
∗
)𝑤
(𝑀)
𝑁 𝑠 𝒔 (𝑀,𝑁 𝑠
)
⋮
(𝑁 − 𝑛)0
∗
1/𝑤
⋮ (𝑀)
1
1/𝑤
(𝑀)
𝑁 𝑠
|.|
|.| argmax 𝑘 ∗
𝑀

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

Frequency Synchronization
Time Synchronization
Select the training waveform ℎ" 𝑘
RLS based channel
Estimation
RLS based frequency offset Estimation 𝜀̂ 𝑘

Frequency Synchronization
—
RLS based channel estimation minimizes:
C
( )
= n k
= 0
λ (
1 n − k ) e ( h | n
ε
)
2
Where: e ( h | n
ε
) = e
− j
2 π k ε ˆ k
N
− 1 r k
− h
ˆ k − 1 s k
Linear function respect to h
ˆ
—
RLS based frequency offset estimation minimizes:
C =
Where: n k
= 0
λ (
2 n − k ) e
ε
( n
| h
)
2 e
ε
( n )
| h
= r k
− e j
2 π k ε ˆ k − 1
N r k
− h
ˆ k s k
Non-Linear function respect to
ε ˆ

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

Simulation Results
—
Time Synchronization performance:
MSE
τ
K
= m = 1
τ m
K
− τ ˆ m
2
—
Frequency synchronization and Channel estimator performance:
MSE
ε
K
= m = 1
ε m
K
− ε ˆ m
2
MSE h
K
= m = 1 h m
K
− h
ˆ m
2

Simulation Results

Outline
—
System Model
—
Proposed Technique
◦
Time Synchronization and Weighted OFDMA waveform
◦
Frequency Synchronization
—
Simulation Results
—
Future Works and Conclusion

Future Works
—
Proposing a frame-based structure for the proposed waveform in order to enable time-frequency offset tracking
—
Extend the proposed method to handle frequency dispersive channels such as ionosphere layers

Conclusion
—
Time-frequency Synchronization is vital for SSP
—
Weighted OFDM sub-carriers for ToA estimation which handles
◦
Multi- satellite scenario
◦
Low PAPR
—
Joint CIR and CFO estimation
—
Low computational Complexity