Software-Defined Radio Architectures for Cognitive, Adaptive, and Reconfigurable Communication Networks

Abstract

Software-Defined Radio (SDR) architectures provide the foundational platform for constructing
cognitive, adaptive, and reconfigurable communication networks capable of dynamic spectrum access, protocol agility,
and evolving service demands. This paper presents an integrated survey of pre-2019 SDR architectures that enable
cognitive capabilities, adaptability, and reconfiguration. We analyze hardware platforms (GPPs, DSPs, FPGAs),
middleware standards such as the Software Communications Architecture (SCA), and practical cognitive-radio
implementations (e.g., GNU Radio with USRPs). We explore architectural features supporting cognitive functions—
such as spectrum sensing, decision-making, and dynamic waveform switching—and assess trade-offs in energy
consumption, processing power, and system flexibility. Drawing on literature including Akeela and Dezfouli’s 2018
SDR architecture survey, MASTR V modular base stations, and dynamic modulation schemes on Model-Based Design
platforms, the paper synthesizes multi-level design trends: from hardware configurability to software modularity. A
general methodology for developing SDR-based cognitive systems is proposed, detailing workflow from hardware
platform selection through cognitive algorithm integration and reconfigurable deployment. Key findings indicate that
FPGA-based hybrid architectures achieve better real-time flexibility but incur higher complexity, while GPP-based
systems offer ease of development at the cost of latency and energy use. Middleware standards like SCA improve
portability but also add overhead. Advantages include rapid protocol prototyping, interoperability, and future-proofing;
disadvantages involve power inefficiencies, development complexity, and regulatory concerns. We conclude with
recommendations for leveraging hybrid architectures, adopting modular standards, and aligning SDR design with
cognitive network objectives. Future research should explore AI-driven cognitive engine integration, low-power FPGA
acceleration, and standards for regulatory-safe reconfigurability.