Adaptive Multi-Input Medium Access Control (AMI-MAC) Design Using Physical Layer Cognition for Tactical SDR Networks

Tactical software defined radio (SDR) networks demand stringent requirements of latency, throughput, and reliability. In the past, significant efforts have been made to achieve maximal efficiency with modifications and improvements either in an individual layer or through the cross-layer design of its working protocol. In this paper, we propose a novel cross-layer design consisting of adaptive multi-input medium access control (AMI-MAC) layer along with an intelligent channel allocation scheme supported by a multiband multimode physical layer. A cognitive engine further empowers this cross-layer design approach to achieve high throughput, improved quality of service (QoS), and adaptive range capabilities. The proposed physical layer exhibits a mixed use of narrowband and wideband waveforms accommodating different range requirements as per demanded QoS. The uniqueness of the proposed physical layer enables SDR to operate in hybrid topology by receiving multiple narrowband signals of different bandwidths with the same configuration of wideband RF front end. The proposed AMI-MAC design ensures a reduction in both control and data phase latency. MAC layer ensures the maximal utilization of the time and frequency spectrum. Bandwidth and delay optimization is also managed by the proposed trio of the physical layer, MAC, and cognition to reduce latency and achieve desired QoS. Simulation results are presented to show the superiority of the proposed design over conventional tactical radio MAC.

[1]  Shoab A. Khan,et al.  A novel hybrid narrowband/wideband networking waveform physical layer for multiuser multiband transmission and reception in software defined radio , 2019, Phys. Commun..

[2]  Lee Pucker CHANNELIZATION TECHNIQUES FOR SOFTWARE DEFINED RADIO , 2003 .

[3]  Srikanth Kundrapu,et al.  Characteristic Analysis of OFDM, FBMC and UFMC Modulation Schemes for Next Generation Wireless Communication Network Systems , 2019, 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA).

[4]  Jian Wang,et al.  Software Defined Radio and Wireless Acoustic Networking for Amateur Drone Surveillance , 2018, IEEE Communications Magazine.

[5]  Luciano Leonel Mendes,et al.  5G Waveforms for IoT Applications , 2019, IEEE Communications Surveys & Tutorials.

[6]  Martin Haardt,et al.  MIMO Signal Processing in Offset-QAM Based Filter Bank Multicarrier Systems , 2016, IEEE Transactions on Signal Processing.

[7]  Shoab A. Khan,et al.  Digital hopping of narrowband waveform using wideband frontend , 2017, 2017 19th International Conference on Advanced Communication Technology (ICACT).

[8]  Shoab A. Khan,et al.  Self-Forming Multiple Sub-Nets Based Protocol for Tactical Networks Consisting of SDRs , 2020, IEEE Access.

[9]  Gernot Hueber,et al.  The design of a multi-mode/multi-system capable software radio receiver , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[10]  Ahmed El Shafie,et al.  On Orthogonal Band Allocation for Multiuser Multiband Cognitive Radio Networks: Stability Analysis , 2014, IEEE Transactions on Communications.

[11]  Ahmed El Shafie,et al.  Stability Analysis of an Ordered Cognitive Multiple-Access Protocol , 2013, IEEE Transactions on Vehicular Technology.

[12]  Wouter A. Serdijn,et al.  Circuits and Systems for Future Generations of Wireless Communications , 2009 .

[13]  Aleksandar Tasic,et al.  Design of Adaptive Multimode RF Front-End Circuits , 2007, IEEE Journal of Solid-State Circuits.

[14]  Cyril Leung,et al.  Downlink Scheduling Schemes for CDMA Networks with Adaptive Modulation and Coding and Multicodes , 2007, IEEE Transactions on Wireless Communications.

[15]  Jans Hendry,et al.  Performance Analysis of Audio Data Transmission on FBMC - Offset QAM System , 2019, 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT).

[16]  Hanna Bogucka,et al.  Trends in Adaptive Modulation and Coding , 2010 .

[17]  Yuefeng Ji,et al.  CSO: cross stratum optimization for optical as a service , 2015, IEEE Communications Magazine.

[18]  Shoab A. Khan,et al.  A Cross-Layer Design for a Multihop, Self-Healing, and Self-Forming Tactical Network , 2019, Wirel. Commun. Mob. Comput..

[19]  Shoab A. Khan,et al.  A Mathematical Model for Cross Layer Protocol Optimizing Performance of Software-Defined Radios in Tactical Networks , 2019, IEEE Access.

[20]  J.R. Long,et al.  Adaptive multi-standard circuits and systems for wireless communications , 2006, IEEE Circuits and Systems Magazine.

[21]  T. Ulversoy,et al.  Software Defined Radio: Challenges and Opportunities , 2010, IEEE Communications Surveys & Tutorials.

[22]  B. Daneshrad,et al.  Energy-aware link adaptation for MIMO-OFDM based wireless communication , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[23]  Yongli Zhao,et al.  Cross-layer restoration with software defined networking based on IP over optical transport networks , 2015 .

[24]  C.-E. Sundberg,et al.  Continuous phase modulation , 1986, IEEE Communications Magazine.

[25]  Shing-Chow Chan,et al.  The design and multiplier-less realization of software radio receivers with reduced system delay , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[26]  M. Zorzi,et al.  Link adaptation in retransmission-based cognitive radio systems , 2012, 6th International Symposium on Telecommunications (IST).

[27]  Muhammad Zeeshan,et al.  Parametric Analysis of FBMC/OQAM under SUI Fading Channel Models , 2020, 2020 22nd International Conference on Advanced Communication Technology (ICACT).

[28]  Muhammad Zeeshan,et al.  A Novel Fuzzy Inference-Based Technique for Dynamic Link Adaptation in SDR Wideband Waveform , 2016, IEEE Transactions on Communications.

[29]  A.C. Tribble The software defined radio: Fact and fiction , 2008, 2008 IEEE Radio and Wireless Symposium.