Software Radio Architecture: A Mathematical Perspective

  • Anandakumar Haldorai
  • Umamaheswari Kandaswamy
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


The Software Radio Architecture (SRA) signifies the advanced technological aspect in the communication industry, which has been enhanced to anticipate transitions in the issues of compatibility protocols and standards. With the evaluation of the Software Defined Radio (SDR), it is fundamental that hardware speculation due to transition in these protocols is diminished to achieve user-friendly communication service expenditure. The evaluation of incontestable properties necessitates a mathematical perspective related to the architecture of software radio. This contribution concentrates at the software radio architecture, applying mathematical frameworks, which signify the rapidly emergent technological initiatives. The article introduces the fundamental conceptions of software radio and illustrates the relevant similar technological aspects such as the programmable digital radio. Software radios are significant in delivering services that are programmable and processed dynamically, including its definition and capacity of a mathematical Turing machine structure. The bordered recursive features which are a collection of the overall recursive feature appear to the extreme category of the Turing and computable features exhibited by the provable software radio capability relative to pay and plug scenarios. Acknowledging the relevant topological features of software radio architecture facilitates the enhancement of the play and plug functions, which are cost-friendly to re-apply.


Software Radio Architecture (SRA) Software Defined Radio (SDR) Analogue and Digital Radio Topological Computability 


  1. 1.
    Mitola, J.: Software radio architecture: a mathematical perspective. IEEE J. Select. Areas Commun. 17(4), 514–538 (1999)CrossRefGoogle Scholar
  2. 2.
    Ozone, M., Hiramatsu, T., Hirase, K., Iizuka, K., Tomisawa, S.: Reconfigurable processor based on ALU array architecture for software radio. Int. J. High Perform. Syst. Archit. 3(1), 33 (2011)CrossRefGoogle Scholar
  3. 3.
    Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 20(2), 1505–1515 (2017)CrossRefGoogle Scholar
  4. 4.
    Haldorai, A., Ramu, A.: Cognitive social mining applications in data analytics and forensics. In: Advances in Social Networking and Online Communities (2019)Google Scholar
  5. 5.
    Anandakumar, H., Umamaheswari, K.: Energy efficient network selection using 802.16g based GSM technology. J. Comput. Sci. 10(5), 745–754 (2014)CrossRefGoogle Scholar
  6. 6.
    Li, S., Singhoff, F., Rubini, S., Bourdellès, M.: Scheduling analysis of tasks constrained by TDMA: application to software radio protocols. J. Syst. Archit. 76, 58–75 (2017)CrossRefGoogle Scholar
  7. 7.
    Mitola, J.: The software radio architecture. IEEE Commun. Mag. 33(5), 26–38 (1995)CrossRefGoogle Scholar
  8. 8.
    Zhang, L.: Software architecture evaluation. J. Softw. 19(6), 1328–1339 (2008)CrossRefGoogle Scholar
  9. 9.
    Lingaiah, D.: Software radio: a modern approach to radio engineering [book review]. IEEE Softw. 20(4), 86–95 (2003)CrossRefGoogle Scholar
  10. 10.
    Li, R., Dou, Y., Zhou, J., Deng, L., Wang, S.: CuSora: real-time software radio using multi-core graphics processing unit. J. Syst. Archit. 60(3), 280–292 (2014)CrossRefGoogle Scholar
  11. 11.
    Anandakumar, H., Arulmurugan, R., Onn, C.C.: Computational intelligence and sustainable systems. In: EAI/Springer Innovations in Communication and Computing (2019)Google Scholar
  12. 12.
    Arteaga, A.: Architecture of a spectrum monitoring system using software-defined radio. Sistemas y Telemática. 10(23), 83 (2012)CrossRefGoogle Scholar
  13. 13.
    Kalyanaraman, S., braasch, M.: GPS adaptive array phase compensation using software radio architecture. Navigation. 57(1), 53–68 (2010)CrossRefGoogle Scholar
  14. 14.
    Anandakumar, H., Umamaheswari, K.: Cooperative spectrum handovers in cognitive radio networks. In: EAI/Springer Innovations in Communication and Computing, pp. 47–63 (2018)Google Scholar
  15. 15.
    Anandakumar, H., Umamaheswari, K.: A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Comput. Electr. Eng. 71, 925–937 (2018)CrossRefGoogle Scholar
  16. 16.
    Sahukar, L., Madhavi, L.: Frequency domain based digital down conversion architecture for software defined radio and cognitive radio. Int. J. Eng. Technol. 7(216), 88 (2018)CrossRefGoogle Scholar
  17. 17.
    Qing, L., Kai, C., Ying-yong, L.: FPGA software architecture for software defined radio. Proc. Eng. 29, 2133–2139 (2012)CrossRefGoogle Scholar
  18. 18.
    Savic, D., Pavlovic, B., Sunjevaric, M.: Software: based radio architecture. Vojnotehnicki glasnik. 48(1), 48–54 (2000)CrossRefGoogle Scholar
  19. 19.
    Subramanian, N.: Software architecture interference—an important non-functional requirement for software ecosystems. Int. J. Softw. Archit. 1(1), 15–16 (2010)CrossRefGoogle Scholar
  20. 20.
    Suganya, M., Anandakumar, H.: Handover based spectrum allocation in cognitive radio networks. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 215–219 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anandakumar Haldorai
    • 1
  • Umamaheswari Kandaswamy
    • 2
  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Information TechnologyPSG College of TechnologyCoimbatoreIndia

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