Literature Database Entry

anwar2021physical


Waqar Anwar, "On Physical Layer Abstraction Modeling for 5G and Beyond Communications," PhD Thesis, Faculty of Electrical and Computer Engineering, Technical University of Dresden (TUD), June 2021. (Advisor: Gerhard P. Fettweis; Referees: Gerhard P. Fettweis and Falko Dressler)


Abstract

The fifth generation (5G) and beyond technologies require ultra-reliable communications to enable various advanced use cases such as industrial automation, tactile internet, vehicle-to-everything (V2X), and safety-critical applications. To meet the stringent re- quirements of these use cases new standards and technologies are being developed such as 5G new radio (NR) and IEEE 802.11bd. The most important step while developing or deploying technology is to evaluate its performance in various scenarios to ensure its suitability for the desired use case. These evaluations are usually carried out through system-level simulators. However, the system-level simulations become time-intensive and require highly complex computations, if the data packets are processed through a complete transmit and receive physical layer (PHY) chain. As it requires many multiplication and division operations while encoding and modulation of data and convolution operation with the channel. Therefore, to speed up these simulations, the PHY is commonly abstracted using a link modeling tecnique also known as physical layer abstraction (PLA). In PLA, the impact of various fading and interference effects on PHY performance is modeled as a function of the received signal-to-interference-plus-noise ratio (SINR). Consequently, the accuracy of system-level evaluations greatly depends on the modeling accuracy of PLA. Furthermore, with the evolution of wireless technologies, new enhancements are expected in upcoming standards. As a result, PLA needs to be comprehensive enough to model their impact on PHY performance. The main focus of this thesis is to enhance the accuracy of existing PLA by developing new algorithms, and extend its modeling capabilities to future proof technologies such as candidate multi-carrier modulation techniques and multi-connectivity communications. To improve accuracy, a new PLA technique is developed using a tighter upper bound on symbol error rate (SER). Further, to model the performance of various multi-carrier modulation techniques, i.e., orthogonal frequency division multiplexing (OFDM), discrete Fourier transform (DFT)-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM), and orthogonal time frequency space (OTFS), the received SINR expressions are derived under the influence of inter-carrier interference (ICI). Then PLA is extended to abstract the performance of these multi-carrier modulation techniques, where the impact of different fading conditions on the received data symbols is modeled. A further extension to PLA is made to abstract the performance of multi-connectivity communications, which is a key enabler for ultra-reliable communications. For the case of multi-connectivity, the joint performance of multiple links is modeled considering multi- ple diversity combining techniques. The accuracy of the developed PLA is evaluated by comparing the simulated and estimated packet error rate (PER) performance. The results show a close match between results obtained from PHY simulation and results predicted through PLA. Finally, the applications of PLA are demonstrated such as performance comparison among multi-carrier modulation techniques, performance comparison of cur- rent and upcoming V2X technologies, and link adaptation in a time-varying channel.

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@phdthesis{anwar2021physical,
    author = {Anwar, Waqar},
    title = {{On Physical Layer Abstraction Modeling for 5G and Beyond Communications}},
    advisor = {Fettweis, Gerhard P.},
    institution = {Faculty of Electrical and Computer Engineering},
    location = {Dresden, Germany},
    month = {6},
    referee = {Fettweis, Gerhard P. and Dressler, Falko},
    school = {Technical University of Dresden (TUD)},
    type = {PhD Thesis},
    year = {2021},
   }
   
   

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Last modified: 2024-04-18