Wireless Network Virtualization and Resource Allocation

Wireless network virtualisation decouples the physical infrastructure of a communication network from the services it delivers, enabling multiple virtual networks to coexist on a shared substrate. Through abstraction of spectrum, compute and storage resources, infrastructure providers can lease customised slices to mobile virtual network operators and application providers. Resource allocation within this framework must balance the competing demands for throughput, latency and reliability, while ensuring isolation between slices and efficient utilisation of the underlying hardware. Advances in optimisation theory, machine learning and auction mechanisms have enabled dynamic, fine-grained allocation of radio resource blocks, transmit power and edge computing capacities. Such capabilities are essential to meet the heterogeneous requirements of emerging applications such as massive Internet of Things deployments, ultra-reliable low-latency communications and immersive multimedia. By integrating virtualisation with real-time analytics and predictive algorithms, operators can adapt resource distribution to changing traffic patterns, maximise end-user Quality of Service and reduce operational costs across 5G and beyond networks.

Research from Nature Portfolio

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Research from all publishers

Recent work has proposed an intelligent slicing and auction-based strategy for private edge cloud systems that formalises resource allocation as a hierarchical challenge involving mobile network operators, virtual operators and end devices. A multi-hop progressive auction algorithm assigns spectrum and compute slices among operators, while a particle swarm-guided terminal allocation strategy optimises power and bandwidth distributions, achieving measurable gains in bidding efficiency and user satisfaction under realistic traffic loads.

Another study has addressed resource distribution in beyond-5G networks by combining heterogeneous parallel processing with graph optimisation techniques. This approach formulates resource assignment as a parallelisable graph problem, allowing dynamic addition and sharing of critical resources. Case studies validate the model across various node configurations, demonstrating resource utilisation improvements while maintaining error margins within acceptable bounds for power and user-centric constraints.

A foundational investigation into joint radio resource allocation and content caching in heterogeneous virtualised wireless networks has shown that integrating cache placement at base stations with radio block scheduling can substantially reduce user-experienced delay. By modelling the problem as a non-convex mixed-integer nonlinear programme and applying a block successive upper-bound minimisation algorithm, the framework outperforms baseline schemes by up to 20 percent in end-to-end latency metrics while lowering backhaul congestion.

Wireless Network Virtualization and Resource Allocation publication trend

The graph below shows the total number of articles in wireless network virtualization and resource allocation across all publications each year (not limited to Nature Index journals).

Technical terms

Wireless network virtualisation: Abstraction of physical communication resources into multiple independent virtual networks.

Network slicing: Logical partitioning of network resources to support distinct service requirements.

Mobile Virtual Network Operator (MVNO): Entity that leases virtual network slices from an infrastructure provider to offer services to end users.

Mixed-Integer Nonlinear Programming (MINLP): Class of optimisation problems combining discrete and continuous decision variables with non-linear constraints.

Edge caching: Storage of popular content at network edge nodes to reduce latency and backhaul load.

References

1. An intelligent resource allocation strategy with slicing and auction for private edge cloud systems. Future Generation Computer Systems (2024).
2. Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms. Cluster Computing (2024).
3. Joint Radio Resource Allocation and Content Caching in Heterogeneous Virtualized Wireless Networks. IEEE Access (2020).