arXiv subjects

Gerhard P. Fettweis

Publications and source records attributed to Gerhard P. Fettweis.

24 records · Page 2Linked to original sources

arXiv2022

Analysis and Performance Evaluation of Mobility for Multi-Panel User Equipment in 5G Networks

Frequency Range 2 (FR2) has become an integral part of 5G networks to fulfill the ever increasing demand for user data throughput. However, radio signals in FR2 experience high path and diffraction loss in mobile environments. To address this issue, multi-panel user equipment (MPUE) is adopted for FR2 whereby multiple antenna panels are placed on the UE body to leverage gains from antenna directivity. In contrast to traditional UEs with isotropic radiation patterns, signal measurements of cells in the network may not be available on all panels simultaneously for MPUE, which may result in outdated signal measurements that affect the reliability of mobility decisions. In this paper, we investigate the mobility performance of two different MPUE schemes following different paradigms for signal measurement and compare their performance with traditional UEs. This performance evaluation is based in multi-beam 5G networks operating in FR2 where there are multiple simultaneously active beams per cell to realize the high throughput requirements. Furthermore, an in-depth analysis of the mobility performance is carried out to determine the best mobility parameter combinations for the different MPUE schemes. Results have shown that both MPUE schemes offer considerable mobility performance gains as compared to traditional UEs. Moreover, it is seen that the MPUE schemes require different mobility parameter settings for the best mobility performance.

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arXiv2021

RACH Optimization with Decision Tree Based Supervised Learning for Conditional Handover in 5G Beamformed Systems

Higher frequencies that are introduced in 5G networks cause rapid signal degradation and challenge user mobility. In recent studies, a conditional handover procedure has been adopted for 5G networks to enhance user mobility robustness. In this paper, mobility performance of the conditional handover is analysed for 5G mm-Wave systems with beamforming. In addition, a resource efficient random access procedure is proposed that increases the chance of contention-free random access during handover, which reduces signaling and interruption time. Moreover, simple, yet, effective decision tree based supervised learning method is proposed to minimize the handover failures that are caused by beam preparation phase of random access procedure. Results reveal the trade-off between contention free random access and handover failures. It is also shown that the optimum operation point of random access is achievable with proposed learning algorithm for conditional handover.

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arXiv2019

Low Complexity Channel Model for Mobility Investigations in 5G Networks

Millimeter-wave has become an integral part of 5G networks to meet the ever-increasing demand for user data throughput. Employing higher carrier frequencies introduces new challenges for the propagation channel such as higher path loss and rapid signal degradations. On the other hand, higher frequencies allow deployment of small-sized antenna elements that enable beamforming. To investigate user mobility under these new propagation conditions, a proper model is needed that captures spatial and temporal characteristics of the channel in beamformed networks. Current channel models that have been developed for 5G networks are computationally inefficient and lead to infeasible simulation time for most user mobility simulations. In this paper, we present a simplified channel model that captures the spatial and temporal characteristics of the 5G propagation channel and runs in feasible simulation time. To this end, coherence time and path diversity originating from fully fledged Geometry based Stochastic Channel Model (GSCM) are analyzed and adopted in Jakes channel model. Furthermore, the deviation of multipath beamforming gain from single ray beamforming gain is analyzed and a regression curve is obtained to be used in the system-level simulations. We show through simulations that the proposed simplified channel model leads to mobility results comparable to Jakes model for high path diversity. Moreover, the multi-path beamforming gain increases the interference in the system and in turn number of mobility failures.

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arXiv2017

On Preambles With Low Out of Band Radiation for Channel Estimation

In this paper, we investigate preamble designs for channel estimation, that jointly address the estimation efficiency in terms of MSE of the channel estimates, and the OOB radiation of the transmit preambles. We provide two novel design techniques, based on a convex optimization problem, to obtain optimal preambles for a single carrier and provide a juxtaposition based method to extend their application to multi-carrier systems. The obtained preambles are shown to have 10 dB to 35 dB lower OOB radiation than the existing preamble based estimation techniques. We also show the fundamental trade-off between the estimation efficiency and the OOB radiation and highlight that the improved OOB performance comes at a cost of increased estimation error. Finally, as a case study, the estimated channel values are used in equalization of a MIMO GFDM system that is aimed for transmit diversity.

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arXiv2015

SINR Model with Best Server Association for High Availability Studies of Wireless Networks

The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable to describe all parts of the SINR distribution in detail, especially the left tail which is of interest for studies of high availability.

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arXiv2010

Asymptotically Regular LDPC Codes with Linear Distance Growth and Thresholds Close to Capacity

Families of "asymptotically regular" LDPC block code ensembles can be formed by terminating (J,K)-regular protograph-based LDPC convolutional codes. By varying the termination length, we obtain a large selection of LDPC block code ensembles with varying code rates and substantially better iterative decoding thresholds than those of (J,K)-regular LDPC block code ensembles, despite the fact that the terminated ensembles are almost regular. Also, by means of an asymptotic weight enumerator analysis, we show that minimum distance grows linearly with block length for all of the ensembles in these families, i.e., the ensembles are asymptotically good. We find that, as the termination length increases, families of "asymptotically regular" codes with capacity approaching iterative decoding thresholds and declining minimum distance growth rates are obtained, allowing a code designer to trade-off between distance growth rate and threshold. Further, we show that the thresholds and the distance growth rates can be improved by carefully choosing the component protographs used in the code construction.

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