scholarly journals Improving Log-Likelihood Ratio Estimation with Bi-Gaussian Approximation under Multiuser Interference Scenarios

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 784
Author(s):  
Yu Fu ◽  
Hongwen Yang
Keyword(s):  
Likelihood Ratio ◽  
Gaussian Approximation ◽  
Additive White Gaussian Noise ◽  
Closed Form Expression ◽  
Accurate Estimation ◽  
Decoding Complexity ◽  
Unknown Distribution ◽  
Log Likelihood ◽  
Log Likelihood Ratio ◽  
Non Gaussian

Accurate estimation of channel log-likelihood ratio (LLR) is crucial to the decoding of modern channel codes like turbo, low-density parity-check (LDPC), and polar codes. Under an additive white Gaussian noise (AWGN) channel, the calculation of LLR is relatively straightforward since the closed-form expression for the channel likelihood function can be perfectly known to the receiver. However, it would be much more complicated for heterogeneous networks where the global noise (i.e., noise plus interference) may be dominated by non-Gaussian interference with an unknown distribution. Although the LLR can still be calculated by approximating the distribution of global noise as Gaussian, it will cause performance loss due to the non-Gaussian nature of global noise. To address this problem, we propose to use bi-Gaussian (BG) distribution to approximate the unknown distribution of global noise, for which the two parameters of BG distribution can easily be estimated from the second and fourth moments of the overall received signals without any knowledge of interfering channel state information (CSI) or signaling format information. Simulation results indicate that the proposed BG approximation can effectively improve the word error rate (WER) performance. The gain of BG approximation over Gaussian approximation depends heavily on the interference structure. For the scenario of a single BSPK interferer with a 5 dB interference-to-noise ratio (INR), we observed a gain of about 0.6 dB. The improved LLR estimation can also accelerate the convergence of iterative decoding, thus involving a lower overall decoding complexity. In general, the overall decoding complexity can be reduced by 25 to 50%.

scholarly journals Performance of non-orthogonal multiple access downlink system using the Log-Likelihood ratio

2020 ◽  
Vol 4 (3) ◽  
pp. First
Author(s):  
Khoa Le Dang ◽  
Thanh Hai Ngo ◽  
Nghia Thai Cong Nguyen
Keyword(s):  
Likelihood Ratio ◽  
Interference Cancellation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Access ◽  
Additive White Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Time Frequency ◽  
Log Likelihood ◽  
Log Likelihood Ratio ◽  
Downlink System

Non-orthogonal multiple access (NOMA) is one of the potential technologies for fifth generation (5G) cellular networks. This technique can combine with other techniques such as Orthogonal Frequency Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO). In NOMA downlink, signals from multiple users are superposed in time-frequency domain. Hence, NOMA systems have a larger throughput than orthogonal multiple access systems. There are several schemes for NOMA detection. The successive interference cancellation (SIC) is commonly used to decode desired signals at the receivers. Some NOMA schemes with SIC are ideal SIC, symbol-level SIC and codeword-level SIC. The previous studies showed that the log-likelihood ratio (LLR) has a performance as ideal SIC. In this paper, we derive the bit error rate for a NOMA downlink system with 2 users (UE) using LLR receiver. This study considers the system over a Rayleigh fading channel and the presence of Additive White Gaussian Noise. The closed-form equations have been proposed for each user with QPSK mapping. The simulation results show that the performance of the system is consistent with the proposed formula

scholarly journals A Corpus-Based Study on the Use of the Logical Connector ‘Thus’ in the Academic Writing of Turkish EFL Learners

2017 ◽  
Vol 10 (2) ◽  
pp. 64
Author(s):  
Serpil Ucar ◽  
Ceyhun Yukselir
Keyword(s):  
Likelihood Ratio ◽  
Academic Writing ◽  
Native Speakers ◽  
American English ◽  
Advanced Learners ◽  
Efl Learners ◽  
Log Likelihood ◽  
Log Likelihood Ratio ◽  
Effect Relation ◽  
Academic Genre

This research was conducted to investigate how frequently Turkish advanced learners of English use the logical connector ‘thus’ in their academic prose and to investigate whether it was overused, underused or misused semantically in comparison to English native speakers. The data were collected from three corpora; Corpus of Contemporary American English and 20 scientific articles of native speakers as control corpora, and 20 scientific articles of Turkish advanced EFL learners. The raw frequencies, frequencies per million words, frequencies per text and log-likelihood ratio were measured so as to compare varieties across the three corpora. The findings revealed that Turkish learners of English showed underuse in the use of the connector ‘thus’ in their academic prose compared to native speakers. Additionally, they did not demonstrate misuse in the use of the connector ‘thus’. Nevertheless, non-native learners of English tended to use this connector in a resultative role (cause-effect relation) more frequently whereas native speakers used it in appositional and summative roles more as well as its resultative role. Furthermore, the most frequent occurrences of ‘thus’ have been in academic genre.

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