DIRECT TO PHASE II: Modular Software Reference Implementation for Waveforms

SBIR · Phase: BOTH · Topic DON27BZ02-DV029 · Solicitation 27.BZ

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.

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2026.11.06

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