Achievable Rate With 1-Bit Quantization and Oversampling Using Continuous Phase Modulation-Based Sequences

Landau, Lukas T. N.; Dörpinghaus, Meik; Lamare, Rodrigo C. de; Fettweis, Gerhard

doi:10.1109/twc.2018.2865579

Cited by 42 publications

(69 citation statements)

References 34 publications

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“…The system model considered in this study is an extension of the discrete system model proposed in [1] for CPM with 1-bit quantization and oversampling at the receiver. Fig.…”

Section: The System Modelmentioning

confidence: 99%

“…For the proposed CPM system, the lower bound on on the achievable rate presented in [1] serves as the base information for choosing the code rate for channel coding. The result of the achievable rate computation in [1] shows that the scenarios with higher modulation order, e.g., M cpm = 8 and M = 3, require channel coding in order to provide communication with low probability of error, because the achievable rate at high SNR is significantly lower than the input entropy. With this, a coding scheme must satisfy the following inequality…”

Section: Channel Coding and Iterative Decodingmentioning

confidence: 99%

“…In this study we consider the design and analysis of CPM schemes with 1-bit quantization and oversampling at the receiver, employing convolutional codes in scenarios with higher modulation order. For such cases, based on the achievable rates computed in [1] channel coding is essential for establishing reliable communications. In this context, our main contribution is the extension of the discrete system model for CPM signals received with 1-bit quantization and oversampling, presented in [1], for a sophisticated coding and decoding scheme.…”

Section: Introductionmentioning

confidence: 99%

“…For such cases, based on the achievable rates computed in [1] channel coding is essential for establishing reliable communications. In this context, our main contribution is the extension of the discrete system model for CPM signals received with 1-bit quantization and oversampling, presented in [1], for a sophisticated coding and decoding scheme. The proposed channel coding method implies the processing of soft information using a BCJR algorithm which is part of an iterative decoding strategy.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Phase Modulation With 1-Bit Quantization and Oversampling Using Iterative Detection and Decoding

Alencar

Landau

Lamare

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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A channel with continuous phase modulation and 1bit ADC with oversampling is considered. Due to oversampling, higher-order modulations yield a higher achievable rate and this work presents a method to approach this with sophisticated coding. A tailored bit mapping is proposed where two bits can be detected reliably at high SNR and only the third bit suffers from an error floor. With this, a convolutional code is only applied to the third bit. Our experiments with a turbo receiver show that the proposed method corresponds to a lower BER in comparison to the case of conventional channel coding.

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“…The system model considered in this study is an extension of the discrete system model proposed in [1] for CPM with 1-bit quantization and oversampling at the receiver. Fig.…”

Section: The System Modelmentioning

confidence: 99%

Section: Channel Coding and Iterative Decodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous Phase Modulation With 1-Bit Quantization and Oversampling Using Iterative Detection and Decoding

Alencar

Landau

Lamare

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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“…In a scenario where a huge amount of data is collected by large receive antenna arrays, an often adopted approach is to compress signals with a lower number of bits to deal with the limited-capacity fronthaul links. Therefore, to become these technologies commercially viable, the use of low-resolution ADCs is unavoidable [76], [77], [78], [79]. Due to the need to minimize the distortions arising from the quantization an AGC is used, before the quantizers, to adjust the analog signal level to the dynamic range of the ADC.…”

Section: Introductionmentioning

confidence: 99%

Joint AGC and Receiver Design for Quantized Large-Scale MU-MIMO Systems with C-RANs

Cunha

Lamare

Ferreira

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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In this work, we propose the joint optimization of the automatic gain control (AGC), which works in the remote radio heads (RHHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works on the baseband unit (BBU) pool, for large-scale multi-user multiple-input multiple-output (MU-MIMO) systems with coarsely quantized signals in cloud radio access networks. We develop successive interference cancellation receivers based on the proposed joint AGC and LRA-MMSE (AGC-LRA-MMSE) approach. An analysis of the achievable sum rates is also carried out. Simulations show that the proposed design has better error rates and higher achievable rates than existing techniques.Index Terms-C-RAN, large-scale MU-MIMO systems, coarse quantization, AGC.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

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1,210

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Achievable Rate With 1-Bit Quantization and Oversampling Using Continuous Phase Modulation-Based Sequences

Cited by 42 publications

References 34 publications

Continuous Phase Modulation With 1-Bit Quantization and Oversampling Using Iterative Detection and Decoding

Continuous Phase Modulation With 1-Bit Quantization and Oversampling Using Iterative Detection and Decoding

Joint AGC and Receiver Design for Quantized Large-Scale MU-MIMO Systems with C-RANs

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

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