Qoherent develops software and engineering tools for applying machine learning to radio signals and software-defined communications. Its work connects the practical stages of an RF application: generating or recording signal data, organizing datasets, training and evaluating models, and integrating inference into radio equipment. RIA Toolkit supplies Python libraries for this workflow; RIA Hub adds a collaborative environment for datasets, code, packages and experiment development.
RIA RAN applies that approach to open 5G radio access networks. The documented ESA project places Qoherent's inference engine alongside the distributed-unit processor in a modified srsRAN architecture, with USRP radios, ARM or x86 computing and an open5GS core. The objective is to adapt radio-resource assignment using local inference. This is an integration component within a wider radio network, whose behaviour also depends on the scheduler, radio hardware, core network and deployment configuration.
The company also participates in NATO DIANA's 2026 cohort with an adaptive 5G payload for mobile ad hoc networking. That programme describes a related development direction; it does not establish that the DIANA payload and the ESA non-terrestrial-network configuration are the same deliverable.
Operating context
Qoherent's workflow makes the relationship between RF data and deployed models visible. RIA Hub describes synthetic-data generation through Python, GNU Radio and MATLAB, live radio recordings, testbed control and plug-ins, model training and optimization, and integration testing. Its recording tutorial uses SigMF data and metadata pairs, while Git LFS and package versioning help manage large recordings and reproducible software dependencies.
The former ria-core repository was archived after the project moved to RIA Toolkit OSS. PyPI records version 0.1.7 uploaded on May 26, 2026, with an Alpha classifier. The OSS foundation uses AGPLv3, with alternative licensing available from Qoherent; the Hub application and tools are advertised as a private beta. These are different access arrangements from a supported RIA RAN integration.
ESA's June 10, 2025 project update describes preliminary design, hardware benchmarking and early field tests informing requirements while scheduler development and testbed construction continued. It provides an architectural and development record, rather than a current completion report. A useful integration discussion therefore starts with the selected radio, driver and RAN versions, the model's data rights, and how the team will measure network behaviour after introducing inference.
Canadian footprint
Qoherent's current company and contact material identifies Toronto, Ontario. The February 2024 FedDev announcement identified Qoherent Inc. in Markham for the supported project; that historical recipient location should not be treated as a second current operating facility. The public evidence supports a Canadian radio-software and engineering business, without a verified current headcount or detailed facility inventory.
Toronto, Ontario is the published city-level context for Qoherent. It does not imply a street address or exact facility location.
Qoherent is relevant where a radio team needs to carry an RF model from recordings and synthetic data into an SDR or open 5G implementation. Its value lies in that engineering workflow and in placing inference alongside radio functions. The ESA architecture gives a concrete integration starting point, while public toolkit releases provide accessible development tools. Buyers should distinguish those assets from a supported RIA RAN deployment and establish the radio, driver, data-rights and network-performance requirements for their own configuration.
Interpretation of the public record. Not qualification, endorsement or evidence of buyer interest.
Machine-learning platform for software-defined radio and 5G RAN
Read technical summary
RIA RAN brings machine-learning inference into software-defined radio and open 5G radio-access functions. Rather than supplying an entire 5G network, it provides software and integration work that operates alongside the radio stack. ESA's RIA RAN NTN project documents a modified srsRAN plug-in on the distributed-unit processor, USRP radio hardware, an ARM or x86 host, open5GS and Qoherent's inference engine for adaptive radio-resource assignment.
RIA Toolkit supports the surrounding engineering process: creating and curating datasets, training and evaluating models, benchmarking an application, and integrating and deploying it. RIA Hub describes synthetic data from Python, GNU Radio and MATLAB alongside live SDR recordings. SigMF pairs retain signal data with recording metadata, while Git LFS and package versioning support collaboration around large RF datasets and software dependencies.
ESA's published June 2025 status records design, benchmarking and early field work informing requirements, with scheduler and testbed development continuing. The architecture is therefore useful as a concrete integration reference. Current performance and delivery maturity depend on the selected hardware, radio-stack versions and model. NATO DIANA's separately described adaptive 5G mobile-ad-hoc payload should not be assumed to be this same configuration.
Evidence of maturity
An integration and engineering offering with an ESA-documented development programme. ESA's June 2025 status records continuing scheduler and testbed work. RIA Toolkit OSS 0.1.7 was published in May 2026 with an Alpha classifier; that software release does not establish completed RIA RAN deployment.
Commercial availability
RIA RAN is available for demonstration and project integration. RIA Toolkit OSS has a public release; RIA Hub applications and tools are advertised as a private beta. Confirm supported radio and driver versions, licensing and maintenance within the proposed project.
Core features
Machine-learning inference alongside radio functions on the distributed-unit processor
Modified srsRAN integration for adaptive radio-resource assignment
USRP radio, ARM or x86 computing, open5GS and Qoherent's inference engine in the ESA-documented architecture
Dataset generation, curation, model training, evaluation, integration and deployment through RIA Toolkit
Synthetic and live IQ recordings with SigMF metadata; Git LFS and package versioning in the Hub workflow
Public Python toolkit distribution under AGPLv3, with alternative licensing available from Qoherent
RIA RAN is offered through demonstrations and integration engagements. RIA Toolkit OSS has a public 0.1.7 release dated May 26, 2026; RIA Hub's application and tools remain advertised as a private beta. Delivery, support and licensing terms depend on the chosen software and project configuration.
Financing and disclosed activity
FedDev Ontario announced C$113,811 in repayable support for Qoherent Inc. on February 22, 2024, for quantum-algorithm work in radio sensing, with five jobs projected. The announcement identified the recipient in Markham; the amount is government support, not disclosed equity financing or evidence of cash received.
CanadaBuys records competitive DND IDEaS contract CW2428432 at C$287,362, awarded December 8, 2025, with an expiry of June 26, 2026. Its recorded term has ended. The award establishes a government contracting relationship but does not disclose amounts paid or prove acceptance of the full RIA product family. Public revenue and equity-financing amounts remain undisclosed in the reviewed material.
Ecosystem relationships
IDEaS contract recipient
Department of National Defence
CanadaBuys records Qoherent as the recipient of a competitive DND IDEaS research contract, while NATO DIANA independently lists the company in its 2026 Canadian cohort.
Reviewed connections
Mission area · Reviewed connectionEvidence strength: High
Deploying RF inference directly into radio-access equipment could support low-latency spectrum awareness and network adaptation at the edge; this mapping is derived.
Radio Intelligence Applications for 5G RAN may help teams working on Major-event and critical-infrastructure cyber defence by contributing to detection and situational awareness, resilient communications and command, and cyber and digital operations.
Reviewed connections indicate possible relevance based on public evidence. They do not indicate procurement direction, eligibility, endorsement or customer interest.
Questions for a first conversation
01
Which radio, driver and RAN versions, dataset rights and software licence apply to the intended RIA integration?
The toolkit has moved to RIA Toolkit OSS, with version 0.1.7 published in May 2026. Hardware support varies across documentation and release history, while Hub access and supported RIA RAN delivery have separate terms.
02
How will the integration team establish that local inference improves the intended radio behaviour without disrupting scheduler or core-network operation?
ESA documents a distributed-unit plug-in architecture and development work. A project-specific evaluation can connect that architecture to measurable behaviour under the buyer's traffic, RF and connectivity conditions.
Original sources 13 linked records
These records support the organization, its owned capabilities and their reviewed connections. A source count is not a count of independent confirmations.
RIA Toolkit supports dataset generation and curation, model training and evaluation, application integration, benchmarking and deployment as a Python library or through RIA Hub. RIA Toolkit OSS is distributed under AGPLv3, with alternative permissive and commercial licensing available on request. Qoherent distributes Python and Conda packages and tests Conda compatibility with RadioConda for GNU Radio and common SDR devices.
Supports: Profile · commercial status
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
Supports: Profile · description
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation and curation, model training and evaluation, application integration, benchmarking and deployment as a Python library or through RIA Hub. RIA Toolkit OSS is distributed under AGPLv3, with alternative permissive and commercial licensing available on request. Qoherent distributes Python and Conda packages and tests Conda compatibility with RadioConda for GNU Radio and common SDR devices.
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
NATO DIANA selected Qoherent for its 2026 Canadian cohort for an adaptive 5G mobile-ad-hoc-network payload, and CanadaBuys records a current competitive DND IDEaS contract.
Supports: Profile · operating context
NATO DIANA lists Qoherent in its 2026 cohort under Advanced Communication Technologies for an intelligent, adaptive and resilient 5G payload for mobile ad hoc networking. The listing establishes accelerator participation, not a NATO procurement award.
CanadaBuys records Qoherent as the recipient of a competitive DND IDEaS research contract, while NATO DIANA independently lists the company in its 2026 Canadian cohort.
Supports: Profile · disclosed financing summary
CanadaBuys identifies Qoherent as recipient of competitive DND IDEaS contract CW2428432, solicitation WS5008404104, with total value C$287,362, award date December 8, 2025 and expiry June 26, 2026. The recorded term has ended; absent an extension, it cannot establish an active September 2026 contract, amounts paid or operational deployment.
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation and curation, model training and evaluation, application integration, benchmarking and deployment as a Python library or through RIA Hub. RIA Toolkit OSS is distributed under AGPLv3, with alternative permissive and commercial licensing available on request. Qoherent distributes Python and Conda packages and tests Conda compatibility with RadioConda for GNU Radio and common SDR devices.
Supports: Profile · operating context
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation and curation, model training and evaluation, application integration, benchmarking and deployment as a Python library or through RIA Hub. RIA Toolkit OSS is distributed under AGPLv3, with alternative permissive and commercial licensing available on request. Qoherent distributes Python and Conda packages and tests Conda compatibility with RadioConda for GNU Radio and common SDR devices.
Supports: Profile · executive relevance summary
Source location: executiveRelevanceSummary
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
RIA Toolkit supports dataset generation and curation, model training and evaluation, application integration, benchmarking and deployment as a Python library or through RIA Hub. RIA Toolkit OSS is distributed under AGPLv3, with alternative permissive and commercial licensing available on request. Qoherent distributes Python and Conda packages and tests Conda compatibility with RadioConda for GNU Radio and common SDR devices.
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · core features
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
RIA Toolkit spans datasets, model evaluation and integration as a Python library or RIA Hub; the OSS version is AGPLv3 with alternative terms on request and Conda/RadioConda support. Toolkit licensing is distinct from RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · core features
RIA Toolkit spans datasets, model evaluation and integration as a Python library or RIA Hub; the OSS version is AGPLv3 with alternative terms on request and Conda/RadioConda support. Toolkit licensing is distinct from RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · summary
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · summary
RIA Toolkit spans datasets, model evaluation and integration as a Python library or RIA Hub; the OSS version is AGPLv3 with alternative terms on request and Conda/RadioConda support. Toolkit licensing is distinct from RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · core features
RIA Toolkit supports dataset generation, curation and testing; building, training and evaluating models; and application integration, benchmarking and deployment. It is available as Python libraries and through RIA Hub. The OSS foundation uses AGPLv3; alternative permissive or commercial licensing is available on request. Toolkit licensing does not establish RIA RAN delivery terms.
Supports: Radio Intelligence Applications for 5G RAN · core features
RIA Toolkit spans datasets, model evaluation and integration as a Python library or RIA Hub; the OSS version is AGPLv3 with alternative terms on request and Conda/RadioConda support. Toolkit licensing is distinct from RIA RAN delivery terms.
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Profile · commercial status
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Profile · executive relevance summary
Source location: executiveRelevanceSummary
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Profile · description
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Profile · operating context
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Radio Intelligence Applications for 5G RAN · maturity
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Radio Intelligence Applications for 5G RAN · summary
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
Qoherent describes RIA Hub as combining Python/GNU Radio/MATLAB signal generation, SDR testbed control and over-the-air emulation, processing and deep-learning plugins, model development, testing and deployment. Gitea, Git LFS and package registries support shared versioned artifacts. Hub applications and tools are in private beta; public OSS repositories and packages have separate access.
NATO DIANA lists Qoherent in its 2026 cohort for an intelligent, adaptive and resilient 5G payload for mobile ad hoc networking. The listing establishes accelerator participation and the described development direction, not a NATO procurement award, independent product qualification or operational deployment.
Supports: Profile · current activity
NATO DIANA selected Qoherent for its 2026 Canadian cohort for an adaptive 5G mobile-ad-hoc-network payload, and CanadaBuys records a current competitive DND IDEaS contract.
Supports: Radio Intelligence Applications for 5G RAN · summary
NATO DIANA lists Qoherent in its 2026 cohort for an intelligent, adaptive and resilient 5G payload for mobile ad hoc networking. The listing establishes accelerator participation and the described development direction, not a NATO procurement award, independent product qualification or operational deployment.
Source location: relationships.0.publicSummary
CanadaBuys records Qoherent as the recipient of a competitive DND IDEaS research contract, while NATO DIANA independently lists the company in its 2026 Canadian cohort.
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Profile · operating context
RF dataset generation and management; Model development and testing; Low-latency inference at the radio edge; Integration with SDR and open-RAN stacks
Qoherent's public evidence establishes its current role and offering, but it does not answer every access, configuration, eligibility, maturity or buyer question needed for a first business-development conversation.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · core features
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technology readiness level
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · name
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · capability type
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · summary
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · summary
Qoherent's radio-intelligence tooling brings machine-learning model development, data management and low-latency inference into software-defined radio and open 5G radio-access-network environments.
Supports: Radio Intelligence Applications for 5G RAN · maturity
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
Deploying RF inference directly into radio-access equipment could support low-latency spectrum awareness and network adaptation at the edge; this mapping is derived.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
RIA RAN pairs a radio application and gNodeB with local ML inference and redeployable models for sensing, communications and NTN resilience. Demo/integration engagement is offered; low-latency claims do not specify a generally guaranteed certified timing budget.
Supports: Radio Intelligence Applications for 5G RAN → Major-event and critical-infrastructure cyber defence
Qoherent's radio-intelligence tooling brings machine-learning model development, data management and low-latency inference into software-defined radio and open 5G radio-access-network environments.
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Profile · executive relevance summary
Source location: executiveRelevanceSummary
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Profile · operating context
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Profile · description
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · capability type
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · name
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technology readiness level
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · core features
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · summary
ESA RIA RAN NTN project 3F.047 describes a modified srsRAN plug-in on the distributed-unit processor, USRP hardware on ARM/x86, open5GS core and Qoherent inference engine for low-SWaP adaptive resource assignment. Its June 10, 2025 status is ongoing with scheduler/testbed work, not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · maturity
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · maturity
ESA RIA RAN NTN project 3F.047 describes a modified srsRAN plug-in on the distributed-unit processor, USRP hardware on ARM/x86, open5GS core and Qoherent inference engine for low-SWaP adaptive resource assignment. Its June 10, 2025 status is ongoing with scheduler/testbed work, not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · core features
ESA RIA RAN NTN project 3F.047 describes a modified srsRAN plug-in on the distributed-unit processor, USRP hardware on ARM/x86, open5GS core and Qoherent inference engine for low-SWaP adaptive resource assignment. Its June 10, 2025 status is ongoing with scheduler/testbed work, not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · summary
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · core features
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technicaldomainslugs
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · defence applications
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · core features
ESA RIA RAN NTN project 3F.047 describes a modified srsRAN plug-in on the distributed-unit processor, USRP hardware on ARM/x86, open5GS core and Qoherent inference engine for low-SWaP adaptive resource assignment. Its June 10, 2025 status is ongoing with scheduler/testbed work, not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · core features
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN · technical tags
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Supports: Radio Intelligence Applications for 5G RAN → Edge Data Processing
ESA describes RIA RAN NTN as a modified srsRAN plug-in on the distributed-unit processor, using USRP radios, ARM or x86 compute, open5GS and Qoherent's inference engine for adaptive resource assignment. Its June 10, 2025 update marked the project ongoing: preliminary design, hardware benchmarking and field tests informed requirements while scheduler development and testbed construction continued. It is not a September 2026 completion record.
Qoherent is a Toronto radio-intelligence company developing machine-learning software, datasets, engineering services and deployment tools for software-defined radio, open 5G networks and RF sensing.
The official public contact pathway for Qoherent is https://qoherent.ai/.
Supports: Profile · operating context
Qoherent is a Toronto radio-intelligence company developing machine-learning software, datasets, engineering services and deployment tools for software-defined radio, open 5G networks and RF sensing.
Supports: Profile · editorial profile version
Qoherent has sufficient mapped public evidence for private reviewer consideration of the shared editorial profile template.
Supports: Profile · description
Qoherent is a Toronto radio-intelligence company developing machine-learning software, datasets, engineering services and deployment tools for software-defined radio, open 5G networks and RF sensing.
Supports: Profile · current activity
True North Map classifies the legacy undated product description as operating context rather than a material current development, so the optional Recent Activity field is cleared.
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
Supports: Profile · commercial status
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
Supports: Profile · operating context
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
Supports: Radio Intelligence Applications for 5G RAN · maturity
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
Supports: Radio Intelligence Applications for 5G RAN · summary
PyPI directly lists Qoherent RIA Toolkit OSS version 0.1.7, uploaded May 26, 2026, with AGPL-3.0-only licensing, Python 3.10 or newer and Alpha development status. The archived ria-core repository directs users to the renamed project. Current public README and indexed release notes differ about SDR-driver support, so the latest package date does not establish a definitive supported-device list.
FedDev Ontario announced a C$113,811 repayable investment for Qoherent Inc., then listed in Markham, to expand a quantum-computing algorithm for radio-signal sensing, with five jobs projected. This is dated repayable programme support, not an equity round, cash-disbursement record or confirmation of a current Markham office.
Supports: Profile · disclosed financing summary
FedDev Ontario announced a C$113,811 repayable investment for Qoherent Inc., then listed in Markham, to expand a quantum-computing algorithm for radio-signal sensing, with five jobs projected. This is dated repayable programme support, not an equity round, cash-disbursement record or confirmation of a current Markham office.
Qoherent's recording tutorial describes reproducible synthetic signal generation and live SDR IQ recording, producing SigMF metadata/data pairs and retaining recordings through RIA Hub and Git LFS. This documents a development workflow, not a validation result for any particular radio-AI model.
Supports: Radio Intelligence Applications for 5G RAN · summary
Qoherent's recording tutorial describes reproducible synthetic signal generation and live SDR IQ recording, producing SigMF metadata/data pairs and retaining recordings through RIA Hub and Git LFS. This documents a development workflow, not a validation result for any particular radio-AI model.
Supports: Radio Intelligence Applications for 5G RAN · core features
Qoherent's recording tutorial describes reproducible synthetic signal generation and live SDR IQ recording, producing SigMF metadata/data pairs and retaining recordings through RIA Hub and Git LFS. This documents a development workflow, not a validation result for any particular radio-AI model.
Supports: Radio Intelligence Applications for 5G RAN · commercial availability
The official qoherent/ria-core repository is marked archived September 11, 2025 and read-only. The current company site still offers RIA Toolkit access, so support and maintained release selection need confirmation; the repository archive does not prove company or product closure.
Supports: Radio Intelligence Applications for 5G RAN → Major-event and critical-infrastructure cyber defence
High-profile events and their host organizations create concentrated cyber-risk and require coordinated planning, monitoring, incident response, exercises and secure information sharing across federal, provincial, municipal and private partners.
Supports: Radio Intelligence Applications for 5G RAN → Major-event and critical-infrastructure cyber defence
Reviewer synthesis of the public end state in the cited material: Event partners should be able to prepare for, detect and respond to cyber incidents through integrated threat intelligence, technical support, exercises and operational coordination before and during major events.
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