What are you looking for?

Category Archives: Featured

AgWireless’26: Accelerating Concerted 6G, AgTech & Multi-Use Innovation!

Author Hongwei Zhang Posted on

6G is expected to “connecting the unconnected” while advancing service frontiers such as integrated AI and communications, non-terrestrial networks (NTN), and hyper-reliable, low-latency communications (HRLLC). Pioneering use cases such as collaborative aerial and ground vehicles for precision AI weed control, agriculture is an ideal industry vertical for piloting 6G technologies towards their real-world adoption. With worldwide 6G efforts transitioning from requirement investigation to solution development, 2026 is a critical year to solidify the strategy and to build the ecosystem partnership for addressing the last-acre connectivity challenge of agriculture while advancing 6G and its multi-use applications in defense, public safety, transportation and so on!

With the world’s premiere AgTech event Farm Progress Show coming to its coverage area during September 1-3, 2026 and with its first-of-its-kind platforms supporting at-scale, high-capacity wireless x-haul, heterogeneous wireless access, and LEO satellite communications, ARA serves as a living lab for fostering transformative collaboration across wireless and application ecosystems, including academia, industry, government, and communities. Building on the success of prior ARA community events such as AraFest’24, the mission of AgWireless’26 is to celebrate progress across wireless, AgTech and other application domains (e.g., defense, public safety, and transportation), to strengthen collaborative communities and scaffolding, and to ignite wireless, AgTech and multi-use innovation initiatives. For more details, please check here.

Revisiting TV White Space for Rural Broadband: Nationwide Availability and ARA Field Validation

Author Taimoor Islam Posted on

The TV White Space (TVWS) spectrum in the 470–608 MHz band travels far, penetrates well, and can cover large rural areas with modest infrastructure. The FCC authorized unlicensed secondary access more than a decade ago, yet rural deployments never took off, and the UK framework collapsed in 2024. With broadcast license renewals approaching (US 2028–2031, UK 2034) and 6G renewing the push to connect the unconnected, there is a narrow window to rethink how the band is governed. Across the country, the spectrum is broadly available and concentrated in rural areas. About 89.4% of US counties have at least one open channel, with a national median near 44 MHz per county. Tribal areas, among the most underserved, also skew toward high availability. 

The harder problem is the gap between what the database reports and what the radio environment actually looks like. At ARA’s Agronomy Farm base station, the database reported three available channels while an RF sensor showed roughly 15 free at the site. That conservatism directly limits coverage. Running our TVWS massive MIMO system at 563 MHz on a channel we confirmed was empty, the database-mandated 16 dBm EIRP reached only 2–3 km, while 42 dBm EIRP extended coverage to 6–8 km at throughput near 120 Mbps. A 26 dB power restriction on empty spectrum cost an order of magnitude in covered area. Current rules also leave secondary users to interfere with one another, which we observed when two ARA base stations transmitted on the same database-reported channel. 

We trained a Physics-Informed Neural Network on 39 spatial samples across a 516 square mile area (RMSE 1.3 dBm) and used the resulting Radio Environment Maps to select channels for maximum coverage, while still validating incumbent protection against measured interference. Measurement-driven sharing delivers usable throughput across a far larger fraction of the region than the database-driven approach. On the policy side, we argue the FCC and NTIA should recognize the 470–608 MHz band as a strategic rural broadband resource and modernize Part 15 from fixed protection contours toward a dynamic, risk-informed framework that integrates near-real-time sensing. 

The county-level dataset used in this study is openly available on IEEE DataPort: TVWS Spectrum Availability. Check the full paper here. 

Measurement Study of Dynamics and Liquid Data Transport in OneWeb LEO Satellite Networks

Author Taimoor Islam Posted on

Authors: Evan Gossling, Owen Perrin, Daji Qiao, Hongwei Zhang

This work conducts a measurement study of the Eutelsat/OneWeb Low Earth Orbit (LEO) Satellite Network (LSN) using ARA’s backhaul OneWeb user terminal (UT). OneWeb has been an under-studied network, so we first conduct a measurement study of the OneWeb LSN from a user’s perspective, and then examine the potential benefits of liquid data transport to increase the reliability of LSNs, as some LSN deployments may observe up to 2% packet loss1.

Our topology figure showcases ARA’s backhaul UT deployed on the rooftop of Iowa State University’s Wilson Residence Hall. Through ARA’s online portal1, we can run experiments from a Dell PowerEdge server directly attached to the OneWeb indoor unit (IDU). Additionally, we utilize a virtual machine (VM) from the Google Cloud Platform near Ashburn, Virginia.

The UT connects via the Ku band to the OneWeb satellite constellation. The connected satellite then connects back to OneWeb ground stations via the Ka band in a bentpipe fashion. These ground stations transmit to and from OneWeb points of presence (PoPs), which are larger data centers where traffic within the OneWeb network is peered with the wider Internet. The service-level agreement (SLA) for ARA’s OneWeb connection is 100 Mbps downlink and 20 Mbps uplink.

We experiment with liquid data transport over our existing OneWeb LSN and deploy both endpoints of the transport in our architecture between the ARA server on ISU campus in Ames, Iowa, and the Google Cloud VM at Ashburn, Virginia.

We examine the statistical performance of liquid data transport in comparison to the baseline transport of TCP with no added coding scheme (aside from any coding which OneWeb may internally utilize). Our results figure compares the performance of liquid data with that of un-encoded TCP with BBR and CUBIC congestion control algorithms. We examine a time series of transferring data at a target rate of 10 Mbps over the LSN, which is subject to an artificial loss rate of 1%.

This additive 1% loss is used to demonstrate the effectiveness of liquid data in instances where the LSN drops packets, as is possible with LSNs such as Starlink, and to a certain extent, OneWeb. Overcoming this packet loss is important due to the network requirements of real-time applications, of which such applications will incur performance degradation when retransmissions must be made, which is exasperated when using an LSN.

Our figure shows that the performance of CUBIC increases significantly when liquid data transport is utilized compared to without it. To better understand this, we can further analyze the cwnd shown in the figure. As we can see, the cwnd value never increases to a suitable quantity for CUBIC (with no coding) when consistent losses are experienced. Essentially, these loss-based congestion control mechanisms prevent any substantial growth of the cwnd. On the other hand, when a tunnel is established between the TCP sender and receiver by the liquid data transport, packet losses are overcome inside the tunnel using the redundant repair data generated by the erasure code, thus shielding the TCP endpoints. As a result, the cwnd remains around a constant high value that corresponds to the target data rate of 10 Mbps.

BBR’s performance is comparatively similar in both cases as BBR is a delay-based protocol and its model of the link is not drastically affected by these packet losses; not to the degree that CUBIC’s loss-based model is. However, as the loss rate increases, the amount of retransmissions BBR (with no coding) must make increases correspondingly2, whereas liquid data transport is able to overcome the packet loss without incurring additional retransmissions. For the traces shown in Fig. 9, BBR with no coding incurs 1767 retransmissions throughout the trace to transmit the application data, while BBR with liquid data transport only experiences 81 retransmissions.

Using liquid data transport, packet losses are shielded from the TCP endpoints where the congestion control algorithm is executed. It also eliminates or significantly reduces the amount of packet retransmissions. Both factors allow for natural growth and steady-state moderation of the cwnd size, which, in turn, results in increased and stable throughput. Overall, we find that OneWeb generally fulfills its service-level agreements, and liquid data transport may be used to further increase the reliability of TCP flows over LSNs.

Check the full article here.

[1] F. Michel, M. Trevisan, D. Giordano, and O. Bonaventure, “A first look at starlink performance,” in Proceedings of the 22nd ACM Internet Measurement Conference, 2022, pp. 130–136.
[2] Y. Cao, A. Jain, K. Sharma, A. Balasubramanian, and A. Gandhi, “When to use and when not to use bbr: An empirical analysis and evaluation study,” in Proceedings of the Internet Measurement Conference, 2019, pp. 130–136.

At-Scale, Real-World srsRAN Experiments in ARA

Author Taimoor Islam Posted on

ARA is pleased to announce new long-range and field-validated open-source 5G and open RAN capabilities using srsRAN across rural and agricultural environments. This large-scale, outdoor deployment demonstrates stable, reliable and high-throughput connectivity using programmable base stations and commercial-off-the-shelf (COTS) UEs. Leveraging high-power and low-noise amplifiers at the programmable base stations, ARA now supports over-the-air coverage up to 1.3km using NI N320 software-defined radios and Quectel RM500Q-GL UEs. Recent outdoor trials demonstrate stable attachment, high throughput, and robust link reliability, even in complex terrains such as valleys and cell edges—showcasing ARA’s ability to expose rich propagation diversity and realistic interference dynamics. Single-cell results shown in Figure 1 confirm strong throughput  scaling with distance and terrain, while multi-cell trials reveal predictable patterns of throughput, latency, and jitter. Figure 3 shows reliability in terms of packet delivery rate in multi-cell interference scenarios. The setup for the results presented in Figure 3 is illustrated in Figure 2. These characteristics make ARA uniquely suited for benchmarking, optimization, and future 6G and Open RAN research in real-world conditions. 

Beyond connectivity, ARA’s srsRAN deployment enables end-to-end measurement, prototyping, and multi-cell experimentation across distributed UE sites and multi-sector gNBs. Researchers can capture per-UE and per-packet metrics—such as throughput, packet delivery reliability, latency, jitter, and interference effects at kilometer-scale ranges. The ARA platform thus enables rigorous exploration of URLLC, HRLLC, multi-UE contention, rural macro-cell behavior and interference modeling under rich outdoor channel conditions leveraging srsRAN software stack. With such capabilities, ARA is well positioned as one of the few living labs to directly support the mission of the Linux Foundation Open Centralized Unit Distributed Unit  (OCUDU) initiative by providing a  large-scale, field-deployed and fully programmable open RAN platform that accelerates open-source ecosystem adoption, reduces reliance on proprietary infrastructure, and enables rigorous next-generation wireless research and prototyping in strategic agricultural and rural settings. Example experiments can be conducted by following the detailed ARA user manual. 

Figure 1: Multi-UE downlink throughput
Figure 2: Multi-Cell deployment scenario 
Figure 3: Packet delivery rate in multi-cell interference scenarios

ARA-to-ICICLE: Remote Data Sourcing for Agricultural AI Processing

Author Sarath Babu Posted on

Modern agricultural AI relies on timely, high-quality field data. Yet collecting and processing this data in real time, especially in rural environments, remains a critical challenge. The ARA-to-ICICLE pipeline addresses this gap by enabling live agricultural data streaming from remote locations directly into an AI processing pipeline, unlocking faster insights and more responsive, data-driven farming.

This collaboration between the ARA project at Iowa State University, ICICLE at The Ohio State University, and the San Diego Supercomputer Center combines expertise in wireless networking, distributed systems, and AI-driven agricultural analytics, demonstrating how cross-disciplinary innovation can transform farming operations.

From a Soybean Field in Iowa to an AI Pipeline in Ohio 

Our demonstration focuses on a practical use case: monitoring a remote soybean field in Ames, Iowa, located at Iowa State University’s Agronomy Farm. A drone equipped with ARA wireless capabilities flies over the field, capturing high-resolution live video. Instead of storing the data for later analysis, the video is streamed in real time to the ICICLE AI pipeline at The Ohio State University, enabling immediate preprocessing and analysis. The live stream is broken into individual frames, geo-tagged, and processed for AI-driven tasks such as weed detection, allowing actionable insights to be generated in near real time. 

ARA Wireless: Enabling Edge Connectivity in Rural Environments

This demonstration is built around the ARA wireless network, which provides the connectivity between the field-deployed drone and downstream computing infrastructure, as illustrated in the figure below.. The drone carries an ARA User Equipment (UE) payload, consisting of an onboard compute node (Raspberry Pi 5 16GB RAM), a high-resolution camera (Arducam OwlSight 64 MP Auto Focus), and a cellular wireless module (Quectel RG530). As the drone flies over the soybean field, it connects to a nearby ARA base station and continuously streams video to the ARA Data Center through ARA x-haul links.

ARA-to-ICICLE architecture.

Rural deployments present unique challenges: unlike urban areas, base stations are often not connected by optical fiber. ARA addresses this limitation by providing long-range, high-capacity wireless backhaul (shown as AraHaul (Wireless Backhaul) in the figure above), leveraging technologies such as millimeter-wave, microwave, and free-space optical communication. This approach demonstrates that even fiber-scarce rural areas can be fully integrated into high-throughput AI pipelines, enabling real-time agricultural insights anywhere.

At the ARA Data Center, a dedicated video streaming server receives the incoming feed. ICICLE then retrieves the stream directly from this server for further processing and analysis.

ICICLE: Turning Remote Data into Actionable Insights

Once the video reaches ICICLE, the system generates individual frames from the live stream and geo-tags them using metadata embedded in the video. These processed frames are then fed into the ICICLE AI pipeline.

In our demo, we focus on weed detection, considering two weed classes. The AI engine analyzes the incoming frames and identifies weed regions, visually highlighting detected weeds for each class. Snapshots of weed detection results at ICICLE are shown in the figure below. This end-to-end workflow, from drone-based sensing to AI inference, demonstrates how remote agricultural environments can be tightly integrated with modern AI systems.

ARA-to-ICICLE: Weed detection snapshots.

Impact and Future Directions: Wireless and Agricultural Innovation Together

The ARA-to-ICICLE pipeline exemplifies a scalable, flexible framework for remote data sourcing in agriculture, providing:

  • Faster insights: Live streaming and real-time processing reduce the time from data capture to actionable recommendations.
  • Precision farming: AI-driven weed detection, crop health monitoring, and yield estimation support targeted interventions such as selective pesticide application.
  • Connectivity solutions for rural areas: Long-range wireless networks overcome infrastructure limitations, enabling AI adoption in remote fields.
  • Extensibility: Beyond weed detection, the system supports a wide range of applications including phenotyping, environmental monitoring, and precision crop management.

By combining ARA’s wireless infrastructure with ICICLE’s AI capabilities, this work illustrates how data-driven, responsive farming is possible anywhere, even in the most remote agricultural landscapes.


Authors: Sarath Babu, Vincent Lee, Joscif Raigne, and Hongwei Zhang (Iowa State University), Yatish S. M., Matt Lieber, and Hari Subramoni (The Ohio State University), and Martin Kandes (San Diego Supercomputer Center)

1 2 3
Iowa State University wordmarkNIFA Logo | PVAMU Home     ARA is funded in part by the NSF award 2130889, NIFA award 2021-67021-33775, and PAWR Industry Consortium.