This paper was given at the Ka and Broadband Communication Conference – 2015 – It is presented here as in the original proceedings’ submission and conference PDF distribution. [proceedings]


This post is the third in a three parts series. you can see part 1 and part 2 (follow links)
Part 1 – is an introduction and abstract and use cases of beam hopping system design.
Part 2 – covers beam hopping basic system consideration and illumination strategies.
Part 3 – covers beam hopping adaptation to demand, summary and conclusion.


4. Beam Hopping Adaptation to Demand

One of the key advantages of beam-hopping is its ability to adapt the resource allocation to the demand. To evaluate the advantages of BH in varying demand conditions and scenarios.

4.1 Broadband Service, Continental Coverage

In this case we refer to [6], which presents the results of analysis of a HTS Beam-Hopping system, performed over several demand distributions over Europe, and compare the results to other distributions and scenarios.

The demand density can be presented by the demand within each cell, where the cells partition the coverage area. Examples for such distributions are given in Fig.4, which shows the total distribution of capacity in each cell, as taken from [6] (expected demand at 2020), and, in addition, an artificial distribution, using similar parameters, but distributed according to the US population distribution, taken from open sources. The cells are sorted according to the demand. For the artificial US distribution, we used the same cell size and capacity as the one used in the study, and extended the number of cells.

The horizontal line in Figure 4 describes the case of a conventional system offering the same level of service, in terms of offered capacity to every cell. Four regions can be distinguished in this graph:

  • The “Usable” region, describing the actual traffic served.
  • The “Exceeding” region, where the offered traffic is above the demand.
  • The “Unmet” region where the demand exceeds the offered traffic.

Figure #4

We can also define the “Offered Traffic” as the total area under the horizontal line. A flexible system would be able to shift the allocated resources from the “Exceeding” region, into the “Unmet” region. The quality of the system depends on the maximal offered traffic available to a cell, and the granularity in the demand allocation it has.

To see the capability of a system based on frequency domain sharing, consider a multi-beam satellite, where the available spectrum, W, is shared among a cluster of Ncl cells, and assume that the minimal bandwidth that can be allocated to a beam is Wmin. Thus, assuming that all cells in a cluster are to be illuminated, the maximal bandwidth that can be allocated to a cell is:

Equation #4

In this paper, we will not elaborate on the actual implementation issues of sharing in the frequency domain, however it should be noted that implementation limits the achievable ratio between the minimal and maximal bandwidth. So, the adaptation is limited. Another aspect of frequency domain sharing is the frequency planning aspect, in systems where the total number of beams is larger than the cluster size- a typical case for HTS and VHTS satellites. In this case the designer is faced with a frequency planning problem, with non-uniform frequency allocation.

Similarly, for a periodic beam-hopping system, using a cycle time of TW a minimal dwell time per cell of Tmin and a cluster of Ncl cells, the maximal dwell time that can be allocated to a beam would be:

Equation #5

Figure 5 below shows the capacity per cell, against the demand per cell for the demand distributions shown above in Fig. 4 (albeit with the number of cells extended to 120 in the US case), and two types of flexible systems- bandwidth flexibility and beam- hopping.

For the bandwidth flexible system, the following parameters (taken essentially from [6]) were used:

  • 2 channel of bandwidth 250MHz (a reuse factor of 4 color scheme, 2 frequency channels, 2 polarizations)
  • Minimal Bandwidth – 62.5 MHz
  • Cluster size= 4
  • Average offered capacity per cell: 585 Mbps
Flexible Bandwidth Allocation Beam Hopping
Europe
USA

Figure 5: Demand distribution vs. Offered Capacity in Flexible Systems

For the beam-hopping system we used:

  • A single channel of 500MHz bandwidth
  • Cluster size = 4
  • A ratio of Tw / Tmin = 16

Using a single channel in the BH system, a lower Back-off can be taken for the power amplifier. Additionally, the beam hopping operation allows for lower interference and thanks to wider channel statistical multiplexing can be readily used. As a result, 20% increase in available capacity per cell can be safely assumed.

Table 1 below shows the total offered capacity, usable capacity, unmet capacity and exceeding capacity for the two methods and two distribution scenarios. Aside of the calculated values in Gbps, the percentage difference, relative to a conventional system is presented. The advantage of flexibility is very significant, and that of beam-hopping is even more so.

The two distributions shown are similar but there are some differences that merit noting:

  1. The population distribution in the US is characterized by several large metropolitan centers with very high capacity demand, many medium sized cities, which still pose a large demand. Together, these facts result in a “fat” high demand region within the distribution, compared to the case in Europe.
  2. The “tail” of the distribution, with low traffic demand, in the case of the US, falls more rapidly that of Europe, indicating large sparsely populated areas.

Table 1: Comparison of Flexible Systems

Offered Usable Unmet Exceeding
Distribution Method Gbps % Gbps % Gbps % Gbps %
Europe Convent. 40.9 0 30.1 0 20.6 0 10.7 0
Freq. Flex 38.1 -6.8 33.9 12.6 16.8 -18.5 4.1 -61.3
BH 42.7 4.6 39.3 33.7 11.4 -49.4 3.5 -77.3
US Convent. 73.5 0 43.9 0 47.3 0 29.6 0
Freq. Flex 56 -23.8 49.3 12.2 42 -11.4 6.7 -77.2
BH 64.9 -11.8 60.7 38.3 30.5 -35.5 4.1 -86

As a result, the improvement achieved by BH in the US, compared to the conventional system and the flexible system, is more impressive in reducing the “Exceeding” capacity relative to the improvement observed for the Europe distribution. On the other hand, the reduction of the “Unmet Capacity” is lower. However, it should be noted that the large and medium metropolitan areas are most likely to be served by terrestrial communication systems, thus the available market in those area for satellite communication is much lower.

4.2 Aeronautical Service- In Flight Communication

In Flight Communication (IFC) service, providing internet access to airliners via satellite is one of the main services that can benefit from beam hopping. Using a flight tracking application, a snap shot of airliners traffic over the north Atlantic corridor between the UK and Maine/ Quebec, is given in Fig. 6, together with a layout of 24 cells of a satellite system covering the area. This snap shot, together with snapshots taken at other time instants show a large variability in the number of planes in each beam as a function of time of day and beam location. Figure 7 shows a bar diagram of the number of planes per beam in the northern beams (numbered west to east from 1 to 12) and southern beams (numbered 13 to 24, west to east), in AM times and PM times taken at 7 time instants- 0:00, 01:00, 02:00, 03:00, 12:00, 14:00 and 15:00 UTC.

Figure 6: Snap shot – Airliner Traffic Over the Atlantic

 


 

Figure 7: Number of Airplanes in Beams. 7 Time instants

If we take those examples as representative of the load and distribution in this route, we can learn about the advantages of beam-hopping. Assuming full coverage, a conventional system would be designed to cover the maximal number of planes per each beam over the entire day, in this case 535 planes, while a BH system would need to cover the maximal number of planes simultaneously in the air at each instant. In this case this number amounts to 334. Thus, in terms of offered capacity, a beam hopping system would be designed for 38% less capacity than a conventional system. A beam-hopping system can also adapt itself very well to the varying scenario, enjoying the predictability of flight patterns. As an airplane crosses a beam in about 30 minutes, the beam hopping time plan can be updated every 15 minutes, in order to optimally adapt it to the instantaneous traffic pattern frequently enough.

Let us study a practical scenario to assess the limitations. Assume the following:

  • Required capacity per airplane: 25Mbps
  • Number of airplanes served: a quarter of overall airplane traffic
  • Available bandwidth: 500MHz, 2 Polarizations.
  • Spectral efficiency per beam: 2bps/Hz.
  • Revisit time constraint: 30msec

In this case the required capacity to be offered by the system would be 25 Mbps * 334/4= 2.1 Gbps. Since each channel provides 500 MHz * 2 bps/MHz = 1Gbps, we need at least 3 transmission channels to provide full coverage.

Figure 8: Beam to Cluster Allocation methods (a) adjacent allocation, (b) non-adjacent

In order to avoid interference, a good practice would be to allocate a cluster of beams covered by each transmitter to adjacent cells, while adjacent clusters would use orthogonal polarization. From the point of view of load, it is better to allocate distant cells to a cluster as it is expected that this would balance the traffic. Figure 8 depicts two example allocations where cells of the same cluster are surrounded by an ellipsoid of the same color and pattern. Fig. 8(a) shows adjacent cells allocation, while Fig. 8(b) shows nonadjacent allocation. Note that in the allocation in Fig. 8(a) the number of cells served by each transmitter is different, to balance the load.

In both cases, assuming adjacent clusters use orthogonal polarizations, the adjacent cell interference is limited, so independent hopping plans can be used for each cluster.

The beam hopping time plan would vary in time and among transmitters. Using the allocation of Figure 8 (a), one can summarize the different plan parameters for each transmitter, in each of the time instants as in Table 2:

Table 2: Cluster Size and Load Ratios at different time Instants

ToD: 0:00 1:00 2:00 3:00 12:00 14:00 15:00
Tx 1 Ncl 6 6 6 6 6 6 6
Ratio 9:01 6:01 7:02 9:02 2:01 6:03 6:03
Load (Gbps) 0.675 0.575 0.625 0.825 0.175 0.675 0.75
Tx 2 Ncl 6 8 7 8 6 9 9
Ratio 3:01 5:01 7:01 7:01 4:01 6:01 8:01
Load (Gbps) 0.225 0.5 0.625 0.85 0.375 0.725 0.75
Tx 3 Ncl 4 4 4 5 8 8 8
Ratio 3:01 2:01 2:01 4:01 8:01 6:01 6:01
Load (Gbps) 0.15 0.15 0.15 0.35 0.7 0.775 0.625

The table helps us determine the trade-off between the cycle time, Tw, and the minimal dwell time, Tmin. Figure 9 below depicts the beam hopping time plan for each of the three transmitters, at 3 of the time instants indicated above. The plan was made for a DVB-S2X waveform using the superframe structure, with roll-off of 20%, yielding a superframe time of 1.53ms. As none of the beams carry a load higher than 1Gbps, which can be provided by each transmitter, it is not necessary to fill up the entire cycle time, which was set here to be equal to the revisit time. The spare time can be traded off by, for example, a lower cycle time to reduce latencies, lower transmission power for non-active beams, just turn off the transmitter, or a combination of those measures.

It should be noted, however, that if a revisit time of 20msec would have been required, it would not be possible to use the granularity of 1.53ms imposed by the superframe waveform, and keep the revisit time window without trading off some capacity.

Figure 9: Beam Hopping Time Plans for 3 transmitters at 3 points in time

 

Summary and Conclusion

Beam Hopping is a technique which was considered for multi-beam satellite systems, such as HTS GEO Satellites, as well as for LEO and MEO constellations, for quite a long time, and is considered for implementation in many satellites in the very near future. The advantages of this technique in the flexibility it provides, which makes it possible to optimally balance the load at the satellite and enable cost-effective payload design, thus reducing the total life-cycle cost of the system as well as the cost of usage.

There is a large variety of types and flavors for beam-hopping systems, depending on the platform, payload types and application. In this paper, we introduced the principles of beam-hopping, in terms of the basic timing, efficiency and latency constraints and presented two quite different approaches periodic time plane and “point-and-shoot” strategies, which can achieve, in average, similar performance in terms of efficiency and latency, but are quite different in terms of implementation.

We also looked at some deployment scenario and the effects that varying demand distributions might have over the effectiveness of operation of beam-hopping in comparison to conventional and adaptable bandwidth systems. For the continental coverage case, following [6], we showed the difference in terms of capacity (useful, exceeding and unmet) the advantage of beam hopping over a frequency flexible system, in different demand distributions. We also introduce a test case for IFC deployment showing different beam allocation strategies and an example for beam hopping time plan design. The flexibility given by beam-hopping in this scenario cannot be matched by any other technique.

Beam-hopping is very advantageous for the new age of satellite communications, however, in order to achieve the advantages, a total ecosystem supporting it should be in place, including ground equipment and standardization.

5. References

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