Inmarsat is the world leader in global, mobile satellite communications. Their customers include governments, aid agencies, plus the maritime, aviation, agriculture and mining industries among many others.
Rapid growth in demand for their services prompted Inmarsat to seek an optimisation partner to ensure their satellites remain operating at peak capacity. Smith Institute’s expertise in algorithm design and mathematical optimisation combined with our particular knowledge of the complexities of radio communications spectrum management made us an attractive choice.
We worked with Inmarsat to construct and benchmark an optimised series of algorithms that maximise spare capacity responsively and minimise unmet demand, enhancing the customer experience and protecting Inmarsat’s bottom line.
The Story of Inmarsat
Established in 1979 by the International Maritime Organization (IMO) to develop a satellite communications network for protecting lives at sea, Inmarsat was the first operator to meet the stringent requirements of the Global Maritime Distress and Safety System (GMDSS) and International Civil Aviation Organization (ICAO) for global safety communications.
Since then, Inmarsat’s world-leading satellite networks have kept communities, companies and countries connected when are where they need it most – on land, at sea and in the air.
Today, Inmarsat’s resilient and wide-ranging portfolio of services and solutions provides its customers with highly reliable connectivity worldwide, including in the most remote and challenging locations.
Global satellite telephony and data traffic has expanded significantly in recent years, with accelerating growth forecast for the years ahead. Across all their markets, Inmarsat’s radio resource management team has seen a surge in the number of connected devices, a rapid expansion in the use of satellite-connected remote sensors and increasing use of data-rich applications.
The team understood that addressing these new challenges to their network whilst continuing to provide a great customer experience in all circumstances would take an innovative approach and the ability to harness technological advances such as increased processing power.
Maintaining quality of service as demand grows necessitates a review of every available option to optimise radio resource capacity. To achieve this optimal radio resource capacity management, Inmarsat required the ability to model and solve a complex set of scenarios with offline optimisation. The team at Inmarsat realised that this would require expertise in constructing the right models and the right algorithms, together with an understanding of how to transform these into solutions with a powerful mathematical solver such as Gurobi Optimizer. They approached Gurobi for advice, who recommended Smith Institute to help with the next stage of Inmarsat’s journey.
We worked closely with Inmarsat’s radio resources management team to understand fully the current operating methods and the challenges they faced. Once we had gained a good grounding in the specifics of their operation, we set about creating a model of the existing system against which we would be able to benchmark new methodologies. This would ensure we could prove the value of any proposed solution.
With an agreed model of the existing radio resource management system in place, we were able to begin to break down the end-to-end system into its constituent parts to look in depth at what mathematical techniques might be applied to enhance their flexibility and resilience.
Effective radio resource management for satellite communications needs to take into consideration how the specific capabilities of each device communicating with the satellite network is affected by a complex interplay of symbol rates, modulation options, error checking and correction, energy consumption and atmospheric conditions. Each of Inmarsat’s satellites needs to be able to offer a set of characteristics that will allow as smooth an experience as possible for all of the devices requesting its services under the prevailing conditions while having the flexibility to adjust those characteristics if conditions or capabilities change. In addition, the operation of Inmarsat’s service needs to be in compliance with its legal, regulatory and contractual obligations.
To manage this complexity, we took a two-stage approach. First, we constructed a genetic algorithm programmed to assemble and analyse a set of likely transmission scenarios from which it could determine a combination of traffic carriers favourable to meet anticipated demand in the prevailing conditions. The optimal traffic carrier combinations generated by our genetic algorithm could then be fed into our second stage algorithm which creates the bandwidth bundles best suited to meeting current, recognised demand. To create our bandwidth bundles, this second-stage algorithm, processed by Gurobi Optimizer, matches selected symbol rates to appropriate modulation and error correction options as determined by the capabilities of the devices currently transmitting to the satellites and the prevailing atmospheric conditions. Finally, the algorithm is processed again by the Gurobi Optimizer to assign these bandwidth bundles to the specific devices so that all devices get the optimal connection experience given their characteristics, the overall traffic, the prevailing conditions as they affect each device and Inmarsat’s service obligations.
The Gurobi Optimizer
Even when the model is expertly crafted, solving the complex real-world mathematical model we created for Inmarsat’s radio resources management problem is only feasible with specialist software. We have found that the Gurobi Optimizer is consistently the right tool for robust, high-powered optimisation at speed, and was ideally suited to processing our radio resource management model with its multitude of constraints and possible outcomes.
The solution is expected to allow Inmarsat to identify satellite beams with underperforming bandwidth bundles and optimise and reconfigure these to increase the utilisation efficiency. Initial results suggest that this will allow an increase in efficiency of 20 to 40% in the most congested geographical areas. Considering the scarcity of satellite resources, this represents a significant improvement in resource utilisation and end-customer satisfaction.