Symbol distortion

We consider the Log-Likelihood ratio and I/Q signals in the frequency domain to evaluate the symbol distortion in a C-RAN. First, the Log-Likelihood ratio figure divides the conste-llation plane by lines equidistant from adjacent points, and it is used to analyze the symbol distortion. We consider different models for the multipath channel, such as the Additive Whi-te Gaussian Noise (AWGN) and the ExWhi-tended Pedestrian A (EPA). Then, we use the iperf tool for network performance measurements [49] to send User Datagram Protocol (UDP) downlink traffic from the EPC to the UE. After that, we use multipath channel models to infer how the distortion of I/Q signals behaves with time-domain and frequency-domain pro-cessing. Second, we analyze I/Q signals (subframe 0), to obtain the Noise-Signal Ratio and visualize the distortion for time-domain and frequency-domain processing. In the following, we present the analysis of the Log-Likelihood ratio for the PBCH, PDCCH, and PDSCH, and then we study I/Q signals, both in the frequency domain.

PDSCH

Figures 4-12 and 4-13 present the Log-Likelihood ratio for the AWGN channel model for time-domain and frequency-domain methodologies respectively. Visually, it is easy to con-clude that the symbol distortion for both methodologies are very similar, and their block error rates with time-domain and frequency-domain processing should be similar. However, the time-domain methodologies have a better distortion performance because it has bigger values of Log-Likelihood ratio.

Figure 4-12: PDSCH Log-Likelihood ratio Time-domain methodologies using the AWGN channel.

Figure 4-13: PDSCH Log-Likelihood ratio Frequency-domain methodologies using the AWGN channel.

Nevertheless, when we utilize the EPA model as displayed in Figure 4-14 and Figure 4-15 with time-domain and frequency-domain processing, respectively, the scenario is different.

We observe a problem concerning the frequency-domain processing. In that case, the receiver has difficulties identifying symbols because some of them overlap themselves. Even though the distortion exists, the effect does not affect the system-level emulation. As a result, the emulation will have lower throughput and higher Block Error Rate (BLER) for the same values of SNR compared to the AWGN channel.

Figure 4-14: PDSCH Log-Likelihood ratio Time-domain methodologies using the EPA channel.

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Figure 4-15: PDSCH Log-Likelihood ratio Frequency-domain methodologies using the EPA channel.

PDCCH

This crucial physical control channel carries the Downlink Control Information (DCI), and use a QPSK modulation scheme. As in the PDSCH case, we expect a higher symbol distor-tion in the frequency-domain processing compared to the time-domain. However, the lower modulation scheme allows the receiver to capture all the control information successfully in both cases. Figures 4-16 and 4-17 shows the PDCCH Log-Likelihood ratio for time-domain and frequency-domain methodologies using the EPA channel model.

Figure 4-16: PDCCH Log-Likelihood ratio Time-domain methodologies using the EPA channel.

Figure 4-17: PDCCH Log likelihood ratio. Frequency-domain methodologies using the EPA channel.

PBCH

This essential physical channel carries information about the Master Information Block (MIB). As well as the PDCCH, this channel is QPSK modulated. It has a similar symbol distortion, but there are not obstacles that jeopardize the successful signal decoding. Figures 4-18 and 4-19 shows the PDCCH Log-Likelihood ratio for time-domain and frequency-domain methodologies using the EPA channel model.

Figure 4-18: PBCH Log-Likelihood ratio Time-domain methodologies using the EPA chan-nel.

Figure 4-19: PBCH Log-Likelihood ratio Frequency-domain methodologies using the EPA channel.

Noise-signal ratio

In the frequency domain with normal prefix cyclic, we have 14 symbols in one subframe.

From them, symbols 0, 4, 7, and 11 correspond to reference signals. Then, symbols 5 and 6 have SSS and PSS information. After that, symbols 7- 10 have information about the PBCH.

Finally, the remaining symbols are related to the PDSCH. We display those transmitter sym-bols in Figure 4-20 top side. The Middle and bottom sides of Figure 4-20 correspond to signals in the frequency domain at the receiver. The middle side shows the received subfra-me 0 with frequency-domain processing, and the bottom side displays the sasubfra-me subfrasubfra-me with time-domain processing. Symbols are divided by vertical red lines to facilitate their location. We found a higher PDSCH distortion in terms of the signal-noise ratio and Inter-Symbol Interference (ISI) with frequency-domain processing. That reduces the throughput

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10 100

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txdataF [dB]

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rxdataF [dB]

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(1,.., symbols per tti * ofdm symbol size = 7168 subcarrier indices)

Figure 4-20: Signals in the frequency domain for subframe 0. Top side: transmitted sig-nal, middle: received signal using frequency-domain processing. Bottom side:

received signal employing time-domain processing.

and degrades the Block Error Rate (BLER), but it does not present problems to capture the transmitted information at the receiver.

It is essential to mention that if we apply CFR in the frequency domain, the equivalent CIR in the time domain is the application of a circular convolution, where the CP is included. In this scenario, when the CP has a length greater or equal to the CIR, both methodologies must provide similar results. Notwithstanding, we obtained different values of the Log-Likelihood ratio for both, frequency-domain and time-domain methodologies, and we left to future work the explanation of this issue.

Distortion impact on the performance of RAN algorithms

The signal detection is the process of extracting data from a received signal corrupted by the channel correlation, noise distortion, multipath propagation, fading, interference, and multiplexing gain. This process, composed of Turbo encoding and interleaving, is the most affected when we use our proposal with frequency-domain processing. As a consequence, a trade-off between distortion sensitivity and computational complexity appears. To reduce the performance degradation, we require advanced non-linear estimators, but the complexity increases with the modulation scheme order. This thesis does not solve the previous trade-off, and it remains as future work.