A Note on Computational Effort

In Section 4.3.3, we have shown that our SIC receiver is 25% more time efficient than our PIA receiver given only one transmission round. For multiple rounds the SIC receiver sends fewer requests for single codeword retransmissions in high SNR regime, meaning that the overall processing time is further reduced. On the other hand, the SIC detection clearly requires higher memory consumption since we need to store the Matched Filter outputs and channel coefficients for each retransmission to be able to reconstruct the signal from preceding rounds.

5.5 Conclusion

Inspired by the drive test measurement campaign, we have presented an HARQ proto-col methodology for our Reduced Complexity Parallel Interference Aware receiver and Successive Interference Aware Canceling receiver. The protocol was then implemented in our OAI downlink simulator LTE TM 4 setup. We quantified the throughput gains after multiple retransmission rounds in frequency selective environment and performed an analysis of the reliability of the downlink data traffic after multiple HARQ rounds.

The SIC receiver achieves higher throughput in all the fading scenarios. However, despite the LLR combining which is accessed through the multi-round SIC procedure, its gains are delivered by the first two rounds of transmissions, while the PIA receiver clearly benefits from all four retransmission rounds. For both receivers, the first transport block achieves huge performance improvements in the low SNR regime thanks to HARQ (up to 10 dB reduction to achieve BLER levels at 10⁻¹), since the price of spectral efficiency degradation with every retransmission round, as the space-frequency resources are reused by the same data stream.

The Ericsson eNodeB that was used to perform the drive tests was configured in TM 3 and applied Alamouti precoding as the only option allowed by the DCI format 2A for the retransmissions of the single codeword. Our analysis for the DCI format 2 showed that Alamouti precoding is favorable for the retransmissions of the single codeword in TM 4 from the reliability point of view, but there is no noticeable preference to any of the retransmission schemes from the throughput point of view.

PHY Abstraction for the R-ML PIA and SIC Receivers

Physical layer abstraction provides an access to high speed link-level computations by quantifying the system performance through efficient techniques that rely on the trans-mission parameters and the channel conditions. The transtrans-mission parameters may include a transmission mode, modulation and coding schemes, and a redundancy version. The performance is evaluated in terms of Block Error Rate (BLER).

The simulation time is reduced by replacing the expensive procedures in the transmit-ter/receiver chain with mapping blocks capable of predicting the link level performance.

In traditional LTE simulators, the full set of the physical layer procedures such as coding, modulation, convolution, demodulation and decoding occupies more than 80% of simu-lation time [Bilel et al., 2011], and is not affordable in terms of consumed time and CPU;

the use of PHY abstraction may reduce computational time by a factor of 100 [Latif et al., 2013].

The second application of the physical layer abstraction is Link Adaptation: taking into account channel conditions, the UE calculates the Rank Indicator, the Precoder Matrix Indicator and the Channel Quality Indicator which maximize the instantaneous system throughput [Schwarz and Rupp, 2011].

The two widely studied techniques on physical layer abstraction are the Exponential Effective Signal-to-Noise Ratio Mapping (EESM) [Tuomaala and Wang, 2005] and the Mutual Information Effective Signal-to-Noise Ratio Mapping (MIESM) [Lei and Jin-bao, 2010; Sayana et al., 2007] methodologies. The EESM strategy appears light-weight and straight forward to implement, but the accuracy is not always satisfying. In contrast, the MIESM approach is more challenging to implement but promises higher accuracy.

To be eligible for real-time devises, the abstraction algorithms should satisfy certain requirements, such as low memory consumption, time efficiency, and feasible accuracy.

Furthermore, it should be generalized for most common propagation scenarios and trans-mission settings, since the UE is not always capable of identifying the distribution of the channel coefficients. To switch dynamically between the calibration coefficients tuned for a particular value of the Modulation and Coding Scheme (MCS) in a particular channel model is also not always possible. In the state-of-art solutions, the researchers often

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phasize on the accuracy, while the practical constrains do not receive sufficient attention.

Contributions In this chapter, we investigate the abstraction methodologies for Physical Downlink Shared Channel in Single-User Multiple-Input-Multiple-Output (MIMO) Long Term Evolution (LTE) systems using our Reduced Complexity Maximum Likelihood (R-ML) Parallel Interference Aware and Successive Interference Aware Canceling receivers, studied in Chapter 4. We develop and validate the MIESM methodology based on look-up tables (LUT) with levels of Mutual Information, initially proposed for multi-user MIMO by [Latif et al., 2012]. An analysis of look-up table quantization analysis is performed, and abstraction results are compared with equivalent results obtained from direct precise computations of the mutual information levels. We detail the advantages and drawbacks of the MEISM look-up table methodology, as well as the feasibility of its deployment for real-time transmissions. Finally, we apply a light-weight EESM methodology and approximate the per-stream performance of our receivers with the Minimum Mean Square Error (MMSE) receiver and the receiver with perfect interference canceling, and provide the analysis of the calibration coefficients.

Contents

6.1 State of the Art . . . 100 6.2 Scenario Description . . . 101 6.3 An Information Theoretic Analysis of ML PIA and SIC Detection 102 6.4 PHY Abstraction Methodologies . . . 103 6.5 Mutual Information Effective SNR Mapping . . . 105 6.5.1 MIESM Methodology . . . 105 6.5.2 Results of the MIESM Approach . . . 109 6.6 Exponential Effective SNR mapping . . . 113 6.6.1 EESM Methodology . . . 113 6.6.2 Results of the EESM Approach . . . 114 6.7 Discussion and Conclusion . . . 119

6.1 State of the Art

Physical later abstraction is a powerful tool for quantifying system performance and can be used in link level simulations as well as in real time measurements. Furthermore, it serves as a part of the UE and eNodeB emulators in radio network performance prediction devices.

The two most studied PHY abstraction approaches are the EESM [Tuomaala and Wang, 2005], which is widely used for linear receivers, and the MIESM [IEEE, 2009; Lei and Jin-bao, 2010; Sayana et al., 2007], which reflects the nature of the ML-family of receivers. Both methods estimate the post-processed per-subcarrier Link Quality Metric (LQM) that can be represented by the SINR or mutual information level. The MIESM approach is proven to outperform EESM [Hanzaz and Schotten, 2013], but is lacking in terms of computation complexity [Galiotto et al., 2015]. Moreover, the EESM approach is

a weak choice in the presence of non-Gaussian interference [Latif et al., 2012; Stancanelli et al., 2011], since interference is — in this case — absorbed into Gaussian noise.

While post-processed SINR computations for the linear receivers are straightforward, it remains a formidable challenge for the non-linear ones, where joint detection is per-formed over all spatial layers. The post-processed SINR for a ML receiver can be esti-mated via bounding with upper- and lower-limits provided by the SINR of respectively Interference-free (IF) and MMSE receivers and applying calibration coefficients [Moon et al., 2012]. This approach was then extended to the R-ML IA case in [Lee et al., 2014], where the adjusting coefficients also depend on the interference strength. Another tech-nique to estimate post-processed SINR for ML receiver through polynomial approximation was presented in [IEEE, 2009], but is not well adapted to MIMO and does not consider the interference-aware case. PHY abstraction for MIMO ML-receivers was developed in [Ramesh et al., 2009] based on QR and QL factorization of the channel matrix. The au-thors compute an upper and lower bound for the performance of each of the streams and then use the average value in order to characterize the achievable performance. To avoid the time consuming online mapping between channel gains and mutual information, [Latif et al., 2012] proposed to store channel statistics and the corresponding mutual information levels in a look-up table for a Multi-User MIMO system with R-ML IA receiver.