In this chapter, we have studied two physical layer abstraction methodologies for our Re-duced Complexity Maximum Likelihood PIA and SIC receivers: the interference aware look-up table based MIESM approach and the EESM approach based on the approxi-mation with the MIMO MMSE receiver and the IF receiver. Both methodologies were evaluated through the link-level simulations carried out by the OAI downlink link-level simulator.
The MIESM approach was built upon the interference-aware strategy [Latif et al., 2012], where the channel statistics is stored in the form of look-up tables, and the link quality metrics is computed via accessing the tables with the direct and reverse mapping functions. This strategy provides satisfying levels of accuracy, but has a number of weak points. This methodology greatly depends on the receiver implementation, fixed-point or float-point architecture, look-up table granularity and channel models since the calibration coefficients might significantly vary from one fading environment to another [Chen et al., 2011a].
Moreover, even in our current setup the size of the look-up tables exceeds the practi-cally feasible limits, and cannot be loaded into the memory of the UE at the same time for all the possible constellations. Furthermore, the practical LTE system system is capable of switching between the two transport blocks and single transport blocks transmission. In our setting, it would require further loading the look up tables for other transmission sce-narios. Unfortunately, taking into account the time that is consumed to load the look-up table into memory of the device, it would be impossible to access them dynamically. All the tables would thus need to be loaded at the very beginning of the transmission, which would take a few hundred megabytes of memory, making it an unfeasible solution for real-time systems. Nevertheless, it is possible to use the look up tables in the simulators to speed-up the link-level computations.
To circumvent the drawbacks of the MIESM method, we evaluated a straightforward and light-weight EESM abstraction based on the approximation of the ML PIA and SIC receivers with the MIMO MMSE and IF detection. The abstracted performance is only slightly lower than the one of MIESM method for the low modulation order. In contrast with the MIESM approach, whose accuracy degrades with the increase of the modulation order, the MSE of the EESM approach remains at the low level even for high modulation orders.
The majority of the state-of-art abstraction methods are based on the calibration coefficients, which depend on the employed MCS and fading environment. However, in
practical Link Adaptation scenarios it is not always possible to obtain the correct values of the calibration coefficients. We have thus evaluated the if the proposed abstraction methodology is adequate without performing a calibration. Unlike MIESM mapping, the EESM approach provides practically acceptable levels of accuracy in this settings.
Link Adaptation for the R-ML PIA and SIC Receivers
The concept of adaptive feedback communication for wireless systems was originally pro-posed in 1968 for noiseless and delay-free feedback channels [Hayes, 1968]. The idea behind an adaptive transmission consists in adjusting some key transmission parameters to guarantee the optimum key performance indicators (such as throughput, reliability, en-ergy efficiency) depending on the variations of the wireless medium over time, frequency, and space. Non-adaptive systems are designed to support the worst-case conditions, which restricts them from reaching their full potential. At the same time, an adaptive coded modulation with turbo codes brings the performance in fading channel close to the Shannon capacity limit in AWGN channel [Goldsmith and Chua, 1998]. Fundamen-tal theoretical studies have demonstrated that low complexity rate adaptation techniques perform close to the joint rate and power adaptation [Alouini and Goldsmith, 1999].
In modern wireless networks, the adaptive transmission techniques and the corre-sponding protocols are regrouped under the term of Link Adaptation. In Long Term Evolution (LTE) systems, the link adaptation can be performed in open or closed loop cycles. During an open loop cycle, the UE is not directly involved in the adaptation and the eNodeB determines the transmission parameters based on the ACK/NACK rates statistics [Gilbert et al., 1996]. However, the statistics must be collected during a certain period of time, which makes this strategy relatively slow [Tao and Czylwik, 2011].
Closed loop adaptation cycles are based on the transmission parameters suggested by the UE as a result of the calculations performed with the estimated channel coefficients.
These parameters form the Channel State Information (CSI), and, depending on the configuration, the base station may or may not take them into account.
The CSI includes three informational items that are sent to the base station via one of the uplink channels: the Channel Quality Indicator (CQI), the Rank Indicator (RI) and the Precoder Matrix Indicator (PMI). During a single antenna transmission, only the CQI is required, while the PMI and the RI are necessary for optimization of the transmission via multiple antennas.
The problem of real-time feedback estimation is one of the challenging points in receiver design. Although the outdated CSI degrades the system performance compared to an ideal
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or predicted CSI, it still provides significantly better performance than a blind Round-Robin scheduler assuming that the outdated channel is not completely independent from the current channel state [Goldenbaum et al., 2011].
The calculation of the CSI is not a trivial task, and various metrics and criteria have been proposed to provide an adequate degree of accuracy with the affordable time and complexity restrictions [Love and Heath, 2003; Schwarz et al., 2010]. However, the prac-tical implementation for the integration into real chipsets remains up to the vendor.
Contributions In this chapter, we study a low-complexity Link Adaptation strategy compatible with our Reduced Complexity Parallel Interference Aware receiver and Suc-cessive Interference Aware Canceling receiver. In LTE Single-User systems with spatially multiplexed schemes, the CSI computations strictly depend on the number of the trans-mitted transport blocks. Indeed, if both codewords are active the UE operates with the precoder matrices, rather than vectors, and the CQI reporting is done for each transport block individually. Based on this, we follow a natural path to first decide on the RI, and then to perform a joint estimation of the PMI and the CQI. Our investigations demonstrate that the design of our receivers allows for the successful detection of both codewords, even if the channel is poorly-conditioned. Furthermore, our sub-optimal light-weight precoder calculation based on evaluation of the correlation coefficients of the channel matrix is numerically proven to perform closely to the optimal Maximum Mutual Information (MI) based criteria. Finally, we propose a light weight CQI adaptation based on abstraction techniques developed in the previous chapter and study joint CQI and PMI adaptation.
We demonstrate that our SIC receiver always reports higher SNR on the second stream, than our PIA receiver given the the same radio conditions.
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