{"id":3484,"date":"2025-07-05T00:10:47","date_gmt":"2025-07-05T04:10:47","guid":{"rendered":"https:\/\/lincslab.ca\/?p=3484"},"modified":"2026-02-20T23:16:26","modified_gmt":"2026-02-21T03:16:26","slug":"quantum-optimization-resource-allocation","status":"publish","type":"post","link":"https:\/\/lincslab.ca\/fr\/quantum-optimization-resource-allocation\/","title":{"rendered":"L\u2019optimisation quantique montre son potentiel comme outil puissant pour am\u00e9liorer l\u2019efficacit\u00e9 de l\u2019allocation des ressources dans les syst\u00e8mes d\u2019acc\u00e8s radio ouverts (Open RAN)"},"content":{"rendered":"\t\t
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Quantum optimization shows potential as a powerful tool for enhancing resource allocation efficiency in Open Radio Access systems (Open RAN)<\/b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A D-Wave quantum computer equipped with a quantum annealing processor.\nPhoto credit: D-Wave Quantum Inc.\n<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Open Radio Access Networks (Open RAN) are emerging as a key architectural approach in next-generation mobile networks. One of its key capabilities\u2014RAN slicing<\/strong>\u2014enables multiple virtual networks to operate on a shared radio infrastructure. This allows for improved flexibility, scalability, and interoperability<\/strong> across services with different Quality of Service (QoS)<\/strong> requirements.
In Open RAN environment, it\u2019s crucial to efficiently allocate the network resources between the different demands of applications like URLLC, eMBB and mMTC, ultimately improving their different QoS performance factors.
Several techniques have been introduced to manage sharing components in 5G and beyond networks, i.e.: using multi-access edge computing (MEC) and a platform as a service (PaaS) model for URLLC applications. Other techniques are priority-based aiming to maximize QoS within limited data rate and BW using genetic algorithms or Deep RL.<\/p>\n

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While these techniques have succeeded in dynamically allocating the RAN resources, they suffer from highly computational complexity, limiting their performance in real-time and also their adaptability to larger networks structures.

In their recent paper titled \u201cOpen RAN Slicing with Quantum Optimization<\/a>\u201d <\/strong>\u2014\u00a0[Available: IEEE<\/a>, arXiv<\/a>], Lincs lab PhD student<\/em>\u00a0Patatchona Keyela<\/a><\/strong>, in collaboration with\u00a0Prof. Soumaya Cherkaoui<\/a><\/strong>, \u00a0proposed a quantum annealing (QA) framework for multi-slice Open RAN resource allocation. Their goal is to maximize the overall system throughput while meeting the QoS constraints for both eMBB and URLLC services in 5G networks.

The study demonstrated that quantum optimization provides (near) optimal solutions quickly enough for real-time applications. To validate their approach, the authors conducted network simulations with various realistic scenarios of gNodeBs, RBs, and users, using parameters chosen to closely mimic real-world conditions.<\/p>\n

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The authors have received the\u00a0Best Paper Award<\/strong>\u00a0at the\u00a02025 15th Global Information Infrastructure and Networking Symposium (GIIS25)<\/a>. The symposium took place from February 25 \u2013 27, 2025, and was held in Dubai, UAE<\/em>.<\/p>\n

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