DoW/NAVY · DoW SBIR 2027 BAA
DON27BZ02-DV029 — DIRECT TO PHASE II: Modular Software Reference Implementation for Waveforms
Pre-Release | released 2026-11-04 | opens 2026-12-02 | closes 2027-01-06 | CMMC Level 2 (Self), ITAR, SBIR
SBIR.gov page | DSIP topics app (search DON27BZ02-DV029)
TPOC: Maulin Patel <[email protected]>; Patrick Cosco <[email protected]>
Keywords: Software-Defined Waveform; SDW; Modular Open Systems Approach; MOSA;, Forward Error Correction; FEC; Signal Processing; Vector Processor
Technology areas: None
Objective
Develop and demonstrate a modular software reference implementation of a government-specified tactical waveform on a Digital Transceiver to establish an open architecture that leverages novel processing to transition from legacy Field-Programmable Gate Array (FPGA)-based systems.
Description
The Multifunctional Information Distribution System (MIDS) Program Office (PMA/PMW-101) is driving a critical architectural pivot in tactical data link (TDL) communications. Historically, the defense sector has relied heavily on FPGAs to host complex tactical waveforms due to strict RF timing constraints. However, as noted in recent software-defined radio (SDR) surveys, this hardware-centric approach suffers from severe developmental friction [Ref 1]. Programming FPGAs via Hardware Description Languages (HDL) requires lengthy synthesis, complex place-and-route cycles, and difficult timing closures. Because software compilation bypasses these physical routing and synthesis barriers, transitioning from HDL to high-order software languages (C/C++) on general-purpose architectures typically reduces development timelines by 50% to 80%—compressing multi-year hardware upgrade cycles into a matter of months.
To overcome legacy constraints, this initiative mandates a transition toward a modular open systems architecture (MOSA). By decoupling the physical layer (PHY) from the underlying hardware, containerized Software-Defined Waveforms (SDWs) can be deployed across modern processing. Because standard Central Processing Units (CPUs) often lack the deterministic throughput required for heavy RF pipelines, modern SDR frameworks dynamically offload computationally intensive PHY tasks to coprocessors like GPUs, Neural Processing Units (NPUs), or adaptive System-on-Chips (SoCs).
This heterogeneous approach enables the execution of highly parallel mathematical pipelines via software Application Programming Interfaces (APIs), including:
Vectorized Signal Processing on GPUs or NPU cores:
Executing Fast Fourier Transforms (FFTs)
High-throughput Forward Error Correction (FEC)
Matrix Multiplications
AI-Driven Cognitive Radio: As demonstrated by O'Shea and Hoydis [Ref 2], traditional heuristic PHY blocks can be replaced by "neural receivers" and deep learning autoencoders. By reusing embedded AI inference blocks, radios can perform real-time channel estimation, signal classification, and non-linear error correction at the tactical edge.
By migrating to this software-first model, the Navy ensures rapid portability across joint-force platforms to provide highly scalable parallelism, allowing waveforms and AI-enhanced microservices to execute, reconfigure, and recover dynamically, ensuring maximum resiliency in contested electronic warfare environments.
Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVWAR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.
Phase I
For a Direct to Phase II topic, the Government expects that the small business will have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The following actions would be required to satisfy the requirements of Phase I:
Crucially, the evidence of this foundational research MUST NOT rely exclusively on previous or active federally funded SBIR/STTR contracts. Tangible proof of feasibility is an absolute prerequisite for award consideration.
To satisfy these Phase I prerequisites, the submitted documentation must substantiate that the offeror has successfully executed the following:
Validation of Feasibility: Comprehensive preliminary research into the development, integration, and demonstration of software-defined waveforms. This must be backed by empirical evidence, including theoretical modeling, simulation results, physical experimentation, and a clear mapping of anticipated technical risks.
Proven Engineering Capabilities: A documented track record in advanced digital signal processing (DSP) and the engineering of modular, hardware-agnostic software waveforms. Offerors must detail their practical experience in optimizing these waveforms for modern, heterogeneous compute environments—specifically multi-core CPUs, GPUs, and NPUs—and demonstrate scalability from enterprise server environments down to constrained tactical radio form factors.
Standardization and Interoperability: A deep, working knowledge of the governing interoperability standards for the specified military waveform family, ensuring the firm is fully equipped to engineer the required operational variants.
Phase II
Design, develop, and integrate a real-time, software-defined physical layer implementation of the government-specified waveform utilizing a Digital Transceiver Software Development Kit (SDK). Validate that the Domain-Specific Architecture (DSA) processing core on the transceiver can natively host the specified waveform family, verifying the performance, agility, and modularity advantages of a software-defined physical layer.
Architectural Integration: Port and optimize the modular waveform software to run directly on the transceiver's processing core, maximizing instruction-set efficiency and parallel execution.
Spectral Verification: Conduct over-the-air or over-the-wire RF transmission testing of the waveform using the transceiver SDK test set.
Performance Acceptance: Execute a comprehensive, multi-point validation campaign in accordance with a government-approved acceptance test plan to confirm functional communication limits, latency, and spectral compliance.
Work in Phase II may become classified. Please see note in the Description.
Phase III / dual use
Complete operational testing, interoperability verification against the government reference waveform, and transition the waveform baseline for deployment on MIDS Joint Tactical Radio System (JTRS) and other approved tactical radio platforms via the government’s accredited integration and release pipeline.
Commercial organizations increasingly require the ability to rapidly deploy, update, and scale high-performance wireless communications without relying on inflexible, proprietary hardware. Technologies developed under this topic may have broad applicability in 5G/6G telecommunications (Virtualized Radio Access Networks), commercial satellite communications (SATCOM), Non-Terrestrial Networks (NTN), private enterprise networks, emergency responder communications, autonomous vehicle networks, and industrial Internet of Things (IIoT) infrastructure.
References
- Sohrabizadeh, A., Bai, Y., Sun, Y., and Cong, J. “Automated Accelerator Optimization Aided by Graph Neural Networks.” Proceedings of the 2022 ACM International Conference on Supercomputing, 2022, pp. 55–60. doi: 10.1145/3489517.3530409. https://www.researchgate.net/publication/356294098_GNN-DSE_Automated_Accelerator_Optimization_Aided_by_Graph_Neural_Networks
- O'Shea, T. and Hoydis, J. "An Introduction to Deep Learning for the Physical Layer." IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, December 2017, pp. 563-575. https://ieeexplore.ieee.org/document/8054694
- “National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. 1993.”. https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004
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