Abstract
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%.
Highlights
The discovery of turbo codes [1,2], low-density parity-check (LDPC) codes [3,4], and polar codes [5] represent major milestones in channel coding
For the scenario where the global noise consists of Gaussian noise and multiuser interference and the channel state information (CSI) and MSC information of interfering signals are unknown, a bi-Gaussian distribution is proposed to approximate the global noise; A simple algorithm is proposed to estimate the two parameters of the BG distribution; The BG distribution together with the estimated parameters are used to calculate the likelihood ratio (LLR); We have conducted simulations to verify the advantages of the proposed BG approximation (BGA) over the existing Gaussian approximation (GA) and the results show that BGA outperforms GA in both word error rate (WER) performance and decoding complexity
We can see that the proposed BGA can significantly reduce the complexity, and the number of iterations can be reduced by 25∼50% for the signal-to-interference-plus-noise ratio (SINR) range of interest
Summary
The discovery of turbo codes [1,2], low-density parity-check (LDPC) codes [3,4], and polar codes [5] represent major milestones in channel coding. [33] proposed one-bit successive-cancellation soft-output (OSS) detectors for an uplink multiuser system, which can exploit the a priori information conveyed by channel decoders to improve the LLRs. ref. [33] proposed one-bit successive-cancellation soft-output (OSS) detectors for an uplink multiuser system, which can exploit the a priori information conveyed by channel decoders to improve the LLRs It adopts the iterative feedback of the previously decoded messages. Since interfering signals are drawn from a finite constellation as the desired signal, the real distribution is definitely non-Gaussian This implies that the existing method (i.e., approximating the global noise as Gaussian for LLR estimation) may incur some performance loss.
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