Backward Compatible Low-Complexity Demapping Algorithms for Two-Dimensional Non-Uniform Constellations in ATSC 3.0

Hong, Hanjiang; Xu, Yin; Wu, Yiyan; He, Dazhi; Gao, Na; Zhang, Wenjun

doi:10.1109/tbc.2020.2985008

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Since then, other articles published target topics of very much traction lately. For example, researchers interested in innovative 5G solutions involving machine learning are advised to read articles [28]- [31] and those interested in solutions targeting the latest media formats papers [16], [33], [34]. Other papers in broadcasting-related areas of focus include [A1], [35], [36].…”

Section: Media Production and The Transition Toward Ip And Cloudmentioning

confidence: 99%

IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

Gómez-Barquero¹,

Giménez²,

Muntean

et al. 2022

IEEE Trans. on Broadcast.

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Section: Media Production and The Transition Toward Ip And Cloudmentioning

confidence: 99%

IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

Gómez-Barquero¹,

Giménez²,

Muntean

et al. 2022

IEEE Trans. on Broadcast.

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“…In order to determine optimal symbol points locations, a set of symbols minimizing (11), the optimizer uses a metaheuristic evolutionary algorithm which is based on swarm intelligence to find out optimized irregular constellations over different T th values. Particle swarm optimization (PSO) technique is chosen in this paper because of its less computational load and fewer tuning parameters as compared to the other evolutionary algorithms.…”

Section: Optimized Irregular Constellations For Finite Block Lengthmentioning

confidence: 99%

“…In order to find optimized irregular constelllations which minimizes (11), after utilizing [29], γ o can be calculated from 29), [29] and δ is chosen as 0.1 as in [29]. The parameters used in PSO optimizer in [30] are chosen in constellation search and in order to keep the average transmitted symbol energy not exceeding certain threshold and fair comparison with the reference constellations, the following constraint is also taken into account during the process.…”

Section: Algorithmmentioning

confidence: 99%

“…Recently, the availability of optimization techniques results in existing growth of using unequally spaced, irregular, constellations and the superiority of those constellations over conventional symmetric constellations were presented in [11]. A comprehensive overview about deploying irregular constellation designs and its practicability issues was given in [12].…”

Section: Introductionmentioning

confidence: 99%

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Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations

et al. 2021

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Current wireless systems offer various use-cases where conventional channel capacity focused performance criteria might not apply. Following the similar perspective, convolutional encoding can find more room due to its low complexity and low decoding delay. Besides, it has been also shown that error performance of a convolutional encoder can be improved further by using adaptive irregular constellations. A new performance measure, data-oriented approach, was recently proposed for the transmission of small data packets, i.e. mission-critical IoT applications, over fading channels. In this paper, delay performance gain resulting from convolution coding optimized irregular constellations is investigated. Then, we derive a new performance criterion based on delay and finite block length constraints. Based on this criterion, we design irregular constellations together with convolutional coding for short packet transmission.

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“…[ 14 ] proposed an algorithm by taking full advantage of the symmetric characteristics of symbol mapping. For massive-order non-uniform constellations, low-complexity demapping algorithms were proposed in [ 15 , 16 ] for one- and two-dimension constellations, respectively, and [ 17 ] proposed a universal low-complexity demapper for non-uniform constellations. For index modulation, a low-complexity LLR calculation algorithm was proposed in [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

2021

Entropy

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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%.

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Backward Compatible Low-Complexity Demapping Algorithms for Two-Dimensional Non-Uniform Constellations in ATSC 3.0

Cited by 10 publications

References 22 publications

IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

IEEE Transactions on Broadcasting Special Issue on: 5G Media Production, Contribution, and Distribution

Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations

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

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