An hypothetical Telecommunication satellite system operating at 19.7 GHz is con- sidered to service the Europe area. The TLC antenna system is constituted by a beamforming network of feeds driven by a set of Multi Port Amplifier (MPA). An optimization algorithm has been realized in order to implement an adaptive process. This optimizer modifies the excitation coefficients of the MPA devices, to achieve the best SNIR profile over the served region, according to the meteorological data. To estimate the weather condition over the service area and to evaluate the performances of the system, the overall region has been sampled with a grid of points, called also “pixels”, as it is shown in Fig. 5.1
The reconfigurable system makes use of meteorological data as the inputs of the optimization process which are provided by the ERA40 Database [15]. This database collects information about weather conditions of the service area, provided as a set climatological information available every 6 hours. Once processed, those information allow to predict the meteorological conditions on the service area for the next 6 hours period. Rain attenuation conditions are obtained by means of two mathematical models:
• Rain field generator: calculate the space-time CCDF of the rain rate, i.e. the fraction of points where the 1-min averaged rain rate exceeds given thresholds during the 6-hour ERA40 slot;
• ExCell model [32]: turns the CCDF of rain rate into the CCDF of slant-path attenuation by simulating the evolution of the precipitation cells
An optimization algorithm has been realized in order to implement an adaptive process to cope with the weather evolution. This optimizer modifies the excitation coefficients of the MPA devices, trying to achieve the best Signal-to-Noise plus Inter- ference Ratio profile over the served region, according to the meteorological data. To estimate the weather condition over the service area and to evaluate the performances of the system, the overall region has been sampled with a grid of points, called also “pixels”, as it is shown in Fig. 5.1(a). The frequency reuse scheme of the antenna feeds has been depicted in Fig. 5.1(b). The geostationary satellite is located at Long. 33o E.
In the remaining part of the Chapter, one of the depicted beams in Fig. 5.1 will be analyzed as a test case. The excitation coefficient wj for the beam j is function of all the other beam coefficient wi6=j. Hence, the analysis of a single beam includes the situation experienced over the whole service area.
The SNIR figure is chosen as Merit Function for the assessment of the recon- figuration performance of the RA coupled with ACM. The SNIR is defined as:
SNIR= C(u, v)
N0Rs+I(u, v) (5.1)
where
C(u, v) = Gr(u, v)Ap(u, v)Am(u, v)PnGtn(u, v) (5.2)
is the carrier power actually received by the ground terminal, while
I(u, v) = Gr(u, v)Ap(u, v)Am(u, v)[X
i
PiGti(u, v)] (5.3)
is the aggregated co-channel interfering power actually received by the ground termi- nal. Specifically:
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Figure 9-12 a) Ground stations and service area b) Frequency Reuse Scheme for the beamforming network
The numerical values that have been defined to devise the system data are reported in the following
table:
Table 9-1 Reconfigurable System Parameters
Number of feeds
72
Number of pixels
1988
MPA order
4 x 4
Transmission Line Loss [dB]
0.5
LNA Temperature [K]
150
Symbol Rate [MBaud]
45
Rx Station Gain [dB]
42
Radiated Power [W]
2000
The reconfigurable system makes use of meteorological data as the inputs of the optimization
process which are provided by the ERA40 Database. This database collects files which give the
Figure 5.1: Service area Grid G6, satellite position 33oE. Pixels distribution (a) and beam division with frequency reuse scheme 4 (b) [2]
• Pn: Nominal power assigned to beam
• Gtn(u, v): Gain of the beam in the ground terminal direction(u, v)
• P
iPiGti(u, v): Sum of EIRP values extended to co-channel beams
• Gti(u, v): Gain of the co-channel interfering beam i in the ground terminal (u, v)direction
• Ap(u, v) = 4πRλ(u,v)2: Free-space path attenuation in the ground terminal direction (u, v)
• Am(u, v): Tropospheric fading in the ground terminal(u, v) direction
• Gr(u, v): Ground terminal antenna gain in the satellite direction
• N0: Received Noise power density
• Rs: Symbol Rate
The received noise power density is equal to:
N0 =KTs(u, v) (5.4)
where
• Ts(u, v) =Ta(u, v) +Tr: System temperature
• Ta(u, v) =Tm(1−Am(u, v)): Receiving antenna temperature
• Tm ≈270 K: from Recommendation ITU-R P.618-10 [24]
• Tr = 290(10(Lt/10)−1) +T
LN A10(Lt/10): Receiver temperature
• Lt ≈0.5 dB: Transmission line loss (typical)
5.2.1
Antenna optimization
The OBDPA optimization algorithm evaluates the optimal beam excitation coef- ficients to maximize a specific merit function, starting from the knowledge of the predicted attenuation of each pixel in the area. As detailed in Chapter 4, a dynamic optimization and climatological optimization are possible.
Concerning the dynamic optimization, the instantaneous weather information are elaborated by the system on a 6 hours basis (4 snapshots per day). Once the system receives the updated information concerning the weather conditions, a tropo- spheric attenuation value is calculated for each pixel of the service area. Subsequently the available power is reallocated, through the optimization of the excitation coef- ficients of the antenna. Such optimization modules consist of a standard gradient optimization algorithm aimed at maximizing the worst-case SNIR level computed on the selected grid of ground terminals.
The climatological optimization is based on the same gradient method and merit figure, with the only difference that the attenuation condition of the service area is derived by a long term attenuation distribution at probability0.1%.