Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.17949

Open-source emulation-based test environment to settle O-RAN-compliant trials

Ramon Fontes·Allan Martins·Vicente Sousa·Kaio Dantas·Lucas Medeiros·Pedro Alves·Marcelo Fernandes·Iago Rego·Eduardo Aranha·Vinícius Filho·Mateus Goldbarg·Wysterlanya Barros·Roger Immich·Augusto V. Neto
View original source ↗Read source document ↗Export metadata ↓

Abstract

Experimental tools are a key factor in both academic and industrial research communities to create design evaluations of new networking technologies that involve troubleshooting or changing the planning of deployed networks. Physical Software-Defined Radio (SDR) experimental platforms enable a design solution for the quick prototyping of wireless communication systems. However, SDR-based experimental platforms incur high costs, which leads to scalability limitations in the experimental settings. Having said this, network simulators, emulators, and new testbeds have attracted increasing attention. Emulation-based research prototyping can be distinguished from real communication networks and SDR-based platforms by allowing a tradeoff between cost and flexibility. This paper examines the Mininet-RAN emulation tool, which, as well as Radio Access Network (RAN) modeling, provides a way to test Open RAN Intelligent Controller (RIC) services without the need to deploy an entire RAN infrastructure. The Mininet-RAN creates virtual network elements, such as hosts, L2/L3 devices, controllers, and links, by combining some of the best emulator features, hardware testbeds, and simulators. By running the current code of standard practice Unix/Linux network applications and network stack, the Mininet-RAN enables real-world network data traffic patterns to be delivered to the RIC, regarding the most significant aspect of the dynamic generation of wireless system's KPIs. We provide the basic code of Mininet-RAN for the first two O-RAN Alliance-defined use cases involving V2X and UAV. The xApps are being implemented in O-RAN SC near-RT RIC, with Mininet-RAN which provides a closed-loop validation environment.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ramon Fontes, Allan Martins, Vicente Sousa, Kaio Dantas, Lucas Medeiros, Pedro Alves, Marcelo Fernandes, Iago Rego, Eduardo Aranha, Vinícius Filho, Mateus Goldbarg, Wysterlanya Barros, Roger Immich, Augusto V. Neto. 2026-09-16. Open-source emulation-based test environment to settle O-RAN-compliant trials. https://doi.org/10.52953/vmbq6087

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Semord: Learned Semantic-Preserving Placement and Low-Fanout Routing for Distributed Vector Search

Vector databases are increasingly deployed in distributed settings where different users, sites, or domains maintain vector data. Existing vector databases rely on a coordinator to record which shards store which parts of the vector space and to route each query to those shards. In a decentralized setting, peers may join, leave, or move data without a trusted node tracking every change, and outdated routing information can therefore send queries to the wrong peers or require contacting many peers, reducing vector retrieval recall and increasing network latency. We present Semord, a decentralized vector search overlay system that achieves high recall by routing each ANN query to a small set of relevant peers, without relying on a centralized coordinator. Semord addresses this problem by making semantic locality routable: 1) We propose VHash to place semantically related vectors near each other in the overlay key space while avoiding load imbalance, so that each query only needs to contact a small neighborhood of peers for distributed local ANN ranking. 2) We design VecDHT, a communication protocol that maintains decentralized routing, region metadata, churn resilience, and VHash updates under membership and workload changes. Our extensive experiments on a real testbed show that Semord improves recall by more than 15% and reduces contacted peers by over 60% compared with decentralized baselines. Semord also approaches the recall and latency of a centralized oracle baseline while reducing peak peer-local ANN index memory by more than 2X. Controlled large-scale simulations further show that Semord scales across real-world embedding workloads and remains robust under churn for scoped vector retrieval as a decentralized overlay.

cs.NI↗
arXiv2026

Lizard: Bandwidth-Adaptive Real-Time Video Analytics through Content-Aware Packet Discarding at Last-Mile Edge Routers

The timeliness and accuracy of edge-based video analytics can be hindered by drastic reductions in available bandwidth (ABW) at last-mile edge routers, causing prolonged queuing delays. This work proposes Lizard, a system that leverages video-content-aware packet discarding to mitigate the negative effects of drastic ABW degradation that may frequently occur at a last-mile edge router by judiciously discarding packets that contain frame blocks less important to the analytics at the destination. To achieve this, we first devise a frame-block-aware RTP header extension to effectively decouple packet dependencies to encode frame blocks. Second, Lizard uses a priority-based feedback mechanism that dynamically evaluates packet priorities based on relative accuracy impacts. Third, we develop an adaptive phase-transition-based packet discarding strategy at the router to discard packets that represent unimportant blocks. Our evaluation of Lizard shows improvements over existing methods are substantial: 53.2% reduction in latency and 27.1% increase in analysis accuracy.

cs.NI↗
arXiv2026

Flux: Optimal Scheduling of Optical Circuit Switches for LLM Training

Optical Circuit Switching (OCS) offers high bandwidth density and energy efficiency for LLM training, but incurs a non-negligible reconfiguration delay. Prior work typically schedules optical circuit switches independently of compute, using aggregate traffic demand to determine which circuits to provision and when. We argue that this separation creates a fundamental inefficiency: reconfigurations that ignore the compute timeline can stall communication, resulting in low circuit utilization and large buffer requirements. In this paper, we present Flux, a scheduler that optimally schedules optical circuit switches based on the structure of the entire workload. Flux remains effective across a wide range of switching speeds by reusing circuits and amortizing reconfiguration delay behind compute and communication. We show that Flux reduces training iteration time by up to $10\times$ and peak NIC buffer requirements by more than three orders of magnitude compared to traditional periodic schedulers.

cs.NI↗