System and Payload Co-Design using Adaptive Coding and Modulation Schemes

Status date

The main goal of the activity is devising a joint system, payload and antenna co-design methodology for maximizing pre-determined performance figures of merit in ACM broadband systems. Although the explicit target of the joint design approach is the system capacity maximization, also cost aspects are expected to be duly taken into account. In particular, platform limitations in terms of mass, power and accommodation are to be included in the design as additional constraints to the optimization problem. The final outcome is the implementation of the devised optimization methodology in a software co-design tool and its validation by means of a simulation campaign applied to a number of representative system/payload architectures.


The key issue allowing the assessment of the performance of Flexible Systems consists of the system optimization leading to the optimum System Configuration, i.e. optimum Frequency, Polarization (and Power, if needed) Plans, which meet at best the target Capacity Requirements, generally non-uniformly distributed, with the assigned resources (Bandwidth and Power Budget).

Such optimization approach is envisaged to be carried out on a combinatorial basis by means of a hybrid procedure generally starting with a Genetic Algorithms step which is followed by a Neighbourhood Search refinement.

The optimization approach is intended to be of the constrained type in order to make the optimum System Configuration complying with the above mentioned performance and platform limitations.

When possible, parallel implementation is addressed in order to allow getting optimization results with acceptable computation time even for scenarios with high number of beams, otherwise prohibitive in terms of serial search.

Another key issue is assessing the level of flexibility of the system in terms of capability to fit changes in traffic distribution due to market evolution during satellite mission lifetime. This can be verified by making the system optimization to target different sets of capacity requirements which are representative of potential market developments over the selected coverage area on a specified time period.


The activity is mainly aimed at providing a SW tool able to exploit at their maximum the resources made available to payloads for Ka-Band Multi-Beam Broadband Satellite Systems in order to generate a Throughput Capacity Performance which meets to the highest level a specified Capacity Requirement distribution over the service area. Such a Co-Design Tool can be particularly useful for commercial satellite operators, which are recently requested to face non-uniform traffic demand distributions over the coverage and their evolution during the satellites mission lifetime.

It allows the design of flexible payloads, i.e. payloads able to allocate non-uniform bandwidth and power to beams in order to meet non-uniform traffic demand distributions, along with the assessment of their capacity performance. The benefits of introducing on-board satellite payload flexibility w.r.t. more classical payload design methods based on uniform bandwidth/power allocation can be pointed out by the comparison of the capacity and consumption performance of the two approaches.


The main driver of the activity is the identification of the most suitable optimization procedure able to cope with the environment under investigation. Due to the need of managing the frequency/polarization plan, the natural approach is expected to fall into the category of combinatorial optimization methods which, in case of a complex scenario (e.g. high number of beams), generally implies high computational effort with no guarantee of achieving the global optimum. Applicable optimization strategies can be found in the area of Evolutionary Algorithms (e.g. Genetic Algorithms), Neighbourhood Search, Simulated Annealing, etc.. Among them, or combination of them, the most appropriate approach, in terms of computational affordability, efficiency, etc, is to be selected.

Moreover such a combinatorial optimization has to comply with the constraints imposed by the problem (e.g. bandwidth and power budget made available by the selected payload/platform configuration, or system availability figures required by the service link). Therefore a constrained version of the combinatorial optimization procedure implementing direct and/or indirect techniques of compliance verification is to be addressed also taking into account their efficiency from the computational point of view.

Another basic aspect to be addressed is the maximum computational efficiency of the system, payload and antenna models used in the system performance assessment. This is required by their iterated use within an optimization loop so that computational effort can be minimised. In this respect a proper trade-off between modelling accuracy vs. computational efficiency has to be carried out.


The study is divided in 4 tasks as described below:

  • Task 1 - System and Payload Architectures Review and Selection
    In this task the following activities are addressed:
    1. Critical review of the set of preliminary system and antenna/payload architectures, which are to be complemented as required and consolidated.
    2. Definition, according to the state-of-the-art design approach, of a set of values for the identified system/payload/antenna parameters for each of the selected architectures in order to constitute, together with their system performances, the reference benchmark configurations for the following optimization.
    3. Identification of the key system, payload and antenna parameters to be optimised for each of the selected system and antenna/payload architectures.
    4. Identification of the constraints for the antenna and payload parameters which ensure an affordable complexity of the two subsystems considering a short/medium term technological development.
    5. Formulation, starting from the optimization objectives, of the system performance figures of merit that will be maximised during the optimisation process. These performance figures of merit will also be used in order to assess the added value of the jointly designed configuration with respect to the benchmark case.
  • Task 2 - Payload/System Co-Design Methodology Definition
    In this task the following activities are addressed:
    1. Accurate modelling of each subsystem involved in the optimization in order to establish the equations that link the system and antenna/payload parameters to the performance figures of merit identified in Task 1. When an analytical formulation is not available, description of the adopted approximations and demonstration of the impact on system performance accuracy. Discussion of The validity boundaries of the models and their suitability to the objectives of the activity.
    2. Review of the optimization algorithms suitable for complex non-linear multi-dimensional problems and justification of their applicability to the System/Payload Co-Design problem. Execution of a preliminary selection of candidate algorithm(s) for the joint System/Antenna/Payload optimization.
    3. Definition, starting from the identified candidate algorithms, of a methodology to perform the System/Antenna/Payload co-design for each of the system/payload architectures selected in Task 1 targeting the maximisation of the identified figures of merit. Preferably, one global optimisation methodology for the overall System/Antenna/Payload Co-Design is expected to be devised, but if the overall optimization problem results to be too complex to be processed in one single optimisation methodology, the overall optimisation problem subdivision in several optimisation sub-problems, involving each a subset of parameters, can be envisaged.
  • Task 3 - Payload/System Co-Design Software Tool Development
    In this task the following activities are addressed:
    1. Definition of the requirements of a software tool that implements the co-design optimisation methodology.
    2. Design of the software tool architecture based on the tool requirements.
    3. Implementation of the system/subsystem models and the optimization algorithms selected in Task 2.
    4. Development of the software tool according to the above-mentioned architecture and software requirements.
    5. Execution of preliminary simple functional tests to prove the proper working of the tool.
    6. Preparation of a preliminary version of the User Manual for the software tool.
  • Task 4 - Payload/System Co-Design Methodology Validation
    In this task the following activities are addressed:
    1. Application of the Co-Design methodology to the System/Payload architectures identified in Task 1 through the developed software tool.
    2. Definition of the optimised configurations and their performance results in terms of figures of merit as identified in Task 1, based on the outputs of the tool.
    3. Comparison of the optimization results with the benchmark system/payload configurations obtained in Task 1 following a state-of-the-art design approach, and assessment of the benefits of the joint co-design methodology.
Current status

The project has been successfully completed.