Exploiting Cyclic Prefix for Turbo-OFDM Receiver Design

Xu, Lingyun; Yang, Jindan; Huang, Defeng; Cantoni, A.

doi:10.1109/access.2017.2734962

Cited by 4 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, not all the CP is polluted by ISI in most cases, therefore, the CP, as a redundancy of main signal, can be exploited to improve the receiver performance. This work shows that except existing analytical approaches [24]- [27], exploiting the CP to enhance receiver performance can be learned by DCCN.…”

Section: B Wireless Channelmentioning

confidence: 92%

“…At the receiver side, several works explored the use of Cyclic Prefix (CP) to enhance the performance of OFDM receiver [24]- [27]. CP is a redundancy of time-domain OFDM symbol which is necessary to mitigate intersymbol interference (ISI), but takes up a portion of spectrum resource in time domain.…”

Section: B Ofdm System and Its Enhancementsmentioning

confidence: 99%

“…A key of exploiting CP is to determine the unpolluted length in CP. Our work is a complementary of existing analytical approaches, such as Maximum Likelihood [24], [26], Factor Graph [25], [27] in exploiting CP.…”

Section: B Ofdm System and Its Enhancementsmentioning

confidence: 99%

See 2 more Smart Citations

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks

Zhao

Vuran

Guo

et al. 2018

Preprint

View full text Add to dashboard Cite

Recent explorations of Deep Learning in the physical layer (PHY) of wireless communication have shown the capabilities of Deep Neuron Networks in tasks like channel coding, modulation, and parametric estimation. However, it is unclear if Deep Neuron Networks could also learn the advanced waveforms of current and next-generation wireless networks, and potentially create new ones. In this paper, a Deep Complex Convolutional Network (DCCN) without explicit Discrete Fourier Transform (DFT) is developed as an Orthogonal Frequency-Division Multiplexing (OFDM) receiver. Compared to existing deep neuron network receivers composed of fully-connected layers followed by non-linear activations, the developed DCCN not only contains convolutional layers but is also almost (and could be fully) linear. Moreover, the developed DCCN not only learns to convert OFDM waveform with Quadrature Amplitude Modulation (QAM) into bits under noisy and Rayleigh channels, but also outperforms expert OFDM receiver based on Linear Minimum Mean Square Error channel estimator with prior channel knowledge in the low to middle Signal-to-Noise Ratios of Rayleigh channels. It shows that linear Deep Neuron Networks could learn transformations in signal processing, thus master advanced waveforms and wireless channels.

show abstract

Section: B Wireless Channelmentioning

confidence: 92%

Section: B Ofdm System and Its Enhancementsmentioning

confidence: 99%

See 1 more Smart Citation

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks

Zhao

Vuran

Guo

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, SOTA implementations of the Long Term Evolution (LTE) cellular telephony standard have a physical layer latency, which significantly exceeds the 100 µs target of the Tactile Internet [13,17]. In order to achieve a high throughput and reliability compared to predecessor schemes, 3GPP LTE employs OFDM [18][19][20] for mitigating echoes in the wireless channel, as well as a turbo code for correcting any remaining transmission errors [21][22][23][24][25][26] Recently, a URLLC mode of operation has been introduced in the LTE standard, which maintains the combination of OFDM and turbo coding, but aims to reduce the processing time available for these operations by 7 times [27]. Motivated by this, we adopt the combination of OFDM and turbo coding in this paper, although this combination can also be expected to have applicability to other FFT-based modulation schemes and other iterative decoding channel codes, such as low density parity check (LDPC) codes.…”

Section: Introductionmentioning

confidence: 99%

“…Owing to this, the FFT produces all of its outputs simultaneously, preventing the turbo decoding process from beginning until after the FFT has been completed. In practical LTE deployments, the transmission latency incurred while receiving, the processing latency incurred while performing the FFT and the processing latency incurred while performing turbo decoding are each around 70 µs [13,23], allowing pipelining as shown in Figure 1(a). The sum of these latencies is 210 µs, which already exceeds the above-mentioned 100 µs target, even without considering the latency associated with propagation, channel estimation, Multiple-Input Multiple-Output (MIMO) detection and transmitter processing.…”

Section: Introductionmentioning

confidence: 99%

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Xiang

Maunder

Hanzo

2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

The Ultra-Reliable Low Latency Communication (URLLC) applications have been proposed in recent years, targeting a round-trip end-to-end latency less than 1 ms with high reliability. Therefore, an order of magnitude improvements are needed in all layers of the wireless communication stack. This is a particular challenge for the physical layer, where typically a processing time of the order of microseconds is required for the computationally intensive demodulation and error correction processing, among other operations. Conventionally, the reception of signals, the demodulation processing and the error correction processing are performed consecutively at the receiver. However, this approach is associated with processing times on the order of hundreds of microseconds, preventing URLLC. Therefore, this paper proposes a novel processing architecture, which is capable of performing reception, Orthogonal Frequency Division Multiplexing (OFDM) demodulation and turbo decoding concurrently, rather than consecutively, hence significantly reducing the processing time. In order to achieve concurrent operation, the OFDM demodulation is performed using a novel cumulative Fast Fourier Transform (FFT), which produces successively more reliable estimates of all transmitted symbols in each successive clock cycle. At the same time, a Fully-Parallel Turbo Decoder (FPTD) is used to recover successively more reliable estimates of all bits in each successive clock cycle.

show abstract

Exploiting CP in BICM-ID OFDM System

Thang

2019

Advances in Engineering Research and Application

View full text Add to dashboard Cite

Exploiting Cyclic Prefix for Turbo-OFDM Receiver Design

Cited by 4 publications

References 29 publications

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks

Concurrent OFDM Demodulation and Turbo Decoding for Ultra Reliable Low Latency Communication

Exploiting CP in BICM-ID OFDM System

Contact Info

Product

Resources

About