Link abstraction for multi-user MIMO in LTE using interference-aware receiver

Mutual Information is the metric that is used to perform link adaptation, which allows to achieve rates near capacity. The computation of adaptive transmission modes is achieved by employing the mapping between the Signal to Noise Ratio and the Mutual Information. Due to the high complexity of the computation of the Mutual Information, this process is performed off-line via Monte Carlo simulations, whose results are stored in look-up tables. However, in Index Modulations, such as Spatial Modulation or Polarized Modulation, this is not feasible since the constellation and the Mutual Information are channel dependent and it would require to compute this metric at each time instant if the channel is time varying. In this paper, we propose different approximations in order to obtain a simple closed-form expression that allows to compute the Mutual Information at each time instant and thus, making feasible the link adaptation.

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“…Note that we assume that s and l are equiprobable. By substi-tuting (5) in (3), the expectation can be described as…”

Section: System Model and Mutual Informationmentioning

confidence: 99%

Channel Dependent Mutual Information in Index Modulations

Henarejos

Pérez-Neira

Tato

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The Vienna LTE-A simulator employs a Mutual Information based exponential SNR Mapping (MIESM) for the SINR-to-BLER mapping [32], [33], which already proved beneficial in Release 5 of UMTS [34], and was shown to outperform all other approaches (e.g., Exponential effective SINR mapping (EESM) [35]) in both complexity and performance. This method compresses the Signal-to-Interferenceplus-Noise Ratio (SINR) values of the assigned Resource Blocks (RBs) for each User Equipment (UE) and 1 ms-long subframe (subsequently also denoted as Transmission Time Interval (TTI)) into an effective SINR, yielding an AWGN-equivalent representation in terms of mutual information.…”

Section: Physical Layer Modelingmentioning

confidence: 99%

Runtime Precoding: Enabling Multipoint Transmission in LTE-Advanced System-Level Simulations

Taranetz¹,

Blazek²,

Kropfreiter³

et al. 2015

IEEE Access

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System-level simulations have become an indispensable tool for predicting the behavior of wireless cellular systems. As exact link-level modeling is unfeasible due to its huge complexity, mathematical abstraction is required to obtain equivalent results by less complexity. A particular problem in such approaches is the modeling of multiple coherent transmissions. Those arise in multiple-input-multipleoutput transmissions at every base station but nowadays so-called coordinated multipoint (CoMP) techniques have become very popular, allowing to allocate two or more spatially separated transmission points. Also, multimedia broadcast single frequency networks (MBSFNs) have been introduced recently in long-term evolution (LTE), which enables efficient broadcasting transmission suitable for spreading information that has a high user demand as well as simultaneously sending updates to a large number of devices. This paper introduces the concept of runtime-precoding, which allows to accurately abstract many coherent transmission schemes while keeping additional complexity at a minimum. We explain its implementation and advantages. For validation, we incorporate the runtime-precoding functionality into the Vienna LTE-A downlink systemlevel simulator, which is an open source tool, freely available under an academic noncommercial use license. We measure simulation run times and compare them against the legacy approach as well as link-level simulations. Furthermore, we present multiple application examples in the context of intrasite and intersite CoMP for train communications and MBSFN.INDEX TERMS Link abstraction, link quality model, runtime-precoding, 3GPP, LTE-A, Vienna LTE-A downlink system level simulator, MIESM, Vienna LTE-A downlink link level simulator, LTE transmission modes, coordinated multipoint, multimedia broadcast single frequency networks, high-user mobility.

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“…In this case the abstraction procedure is a bit different since instead of the SINR we now need to calculate the signal to noise ratio (SNR) and the signal to interference ratio (SIR) separately. Please refer to [14,16] for details.…”

Section: Phy Abstraction Overviewmentioning

confidence: 99%

“…The reverse information mapping function I −1 does not necessarily have to be the same as the forward function I, for example if the interference aware receiver abstraction is used [14].…”

Section: Phy Abstraction Overviewmentioning

confidence: 99%

“…They show results for the rate compatible punctured convolution codes and different MIMO antenna configurations. A new method for PHY abstraction for multiuser MIMO (MU-MIMO) in the framework of LTE using nonlinear interference aware receivers has been proposed in [14]. Although the scheme is targeted towards MU-MIMO systems but it can also be applied to MIMO systems employing nonlinear receivers.…”

Section: A Introductionmentioning

confidence: 99%

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On scalability, robustness and accuracy of physical layer abstraction for large-scale system-level evaluations of LTE networks

Kaltenberger

Latif

Knopp

2013

2013 Asilomar Conference on Signals, Systems and Computers

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We present an in-depth performance analysis of the gains of physical layer (PHY) abstraction when compared to a full implementation of the physical layer. The abstraction model uses either effective signal to noise plus interference (SINR) mapping or mutual information effective SINR mapping and covers different transmission modes as well as support for hybrid automatic repeat request. Using the OpenAirInterface LTE system level simulator we show that for a simple network with one base station and two user equipments these PHY abstraction techniques decrease the simulation time by a factor of up to 100 while providing the same accuracy as with the full PHY implementation.

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Link abstraction for multi-user MIMO in LTE using interference-aware receiver

Cited by 10 publications

References 8 publications

Channel Dependent Mutual Information in Index Modulations

Channel Dependent Mutual Information in Index Modulations

Runtime Precoding: Enabling Multipoint Transmission in LTE-Advanced System-Level Simulations

On scalability, robustness and accuracy of physical layer abstraction for large-scale system-level evaluations of LTE networks

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