Search arXivSearch

arXiv · 2609.06812

An FPGA-in-the-Loop Testbed for MU-MIMO OFDM Beamforming over Ray-Traced Wireless Channels

Abstract

Wireless networks face ever expanding throughput demands from heterogeneous, high-density user populations, requiring beamforming algorithms that adapt to channel conditions with low latency. Validating such algorithms requires either costly over-the-air testbeds or simulation environments that lack the timing and resource constraints of real hardware, leaving a gap between algorithm design and hardware-realizable deployment. This work presents a hardware-in-the-loop (HIL) testbed that closes that gap by coupling an FPGA-based implementation of OFDM Waveforms with MU-MIMO beamforming to NVIDIA Sionna's ray-tracing channel simulator, enabling a physical base-station architecture to transmit and receive against a Sionna-rendered digital-twin propagation environment in real time. Unlike prior work that validates beamforming algorithms either purely in simulation or on full RF testbeds, this architecture allows beamforming logic running on actual FPGA fabric to be evaluated under realistic, controllable, and repeatable channel conditions, including UE mobility and site-specific multi-path, without requiring an anechoic chamber or live RF front end. We detail the FPGA OFDM transmit/receive pipeline, the synchronization and data interface between the FPGA and the Sionna environment, and validation of signal quality under AWGN and ray-traced channel conditions, establishing this testbed as a platform for hardware-validated beamforming research.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Drew Schlesener, Tolunay Seyfi, Fatemeh Afghah. 2026-09-06. An FPGA-in-the-Loop Testbed for MU-MIMO OFDM Beamforming over Ray-Traced Wireless Channels. https://arxiv.org/abs/2609.06812

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

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

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

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

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