How is mobile network performance assessed and improved with collected data? Good estimation of network performance requires a huge amount of data to be collected and analysed. Engaging with our customers over the past number of years, they’ve confirmed that enormous time is spent for both Drive Testing and the analysis of the logs.
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Is your network capacity correctly dimensioned for sudden traffic spikes during special events or extraordinary scenarios? Are you sure those key performance indicators are reflecting real end-user experience? How will your network perform during the next public bench-marking campaign? Those questions are continuously bothering many CTOs and CEOs around the globe. Answers aren’t that easy.
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How can you know how many more customers your Fixed Wireless Access (FWA) network can handle in a specific geographical area while still guaranteeing a good speed for everyone? To keep your customers happy and to minimize churn, your mobile network needs to be high performing.
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The road to realizing the negative downtime concept in mobile networks is not straightforward and there are many challenges. Automation and AI/ML techniques are forming a key part of the solution in the early detection and prevention of issues at network level scale that will help to deliver a real positive for customer experience.
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Oh, how I remember the good old days, working as a support engineer in telecoms during the late nineties, early two thousands. The demand for mobile phones seemed endless as did the money available to build and support the networks.
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In operations/network support the goal of many teleco’s is a 'zero-touch' network– a fully autonomous network that can operate, optimize and heal itself without the need for human maintenance or intervention. If you’re reading this blog, this goal is of interest to you too.
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The evolution of the connected world over the past 10 years has been mind blowing. It’s had a massive impact on people’s lives, mostly positive one might say. The impact for Mobile Network Operators (MNOs) has not been that positive though.
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At Aspire we believe that our innovation in network support is going to improve both user experience, by delivering better SLAs with shortened time for problem resolution, as well as operator experience, with new synergies and improvement of people, processes and tool efficiency, which is finally going to lead to OPEX reduction and lower the fundamental cost of network bits per second.
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Machine learning is a current ‘hot topic’, which has been headlining in technology discussion in recent years. What really is machine learning? Many people do not understand this fundamental topic.
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Can't wait to have gigabit throughput in your mobile phone? Keep on reading to find out more about the technology that will get you there.
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Crime fighting and mobile telecom optimization may not seem like they go hand in hand but you might be surprised how innovative solutions can cross the barrier.
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What do Taylor Swift, Atlanta US airport and London police have in common? Sure, Atlanta Airport has little ‘Blank Space’ in its busy air traffic schedule, but there’s more to it than that.
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What helps makes a good wine? Grapes, warm weather, a good vintner, NB-IOT?
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In my last post, I explored changing the model of an IPAM application to better describe an entity called VRF.
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