PHY4: Modulation and coding
Thursday, 10 June 2021, 16:00-17:30, Zoom Room
Session Chair: Marwa Chafii (ENSEA, France)
Xiao Chen, Miao Liu and Guan Gui (Nanjing University of Posts and Telecommunications, China); Bamidele Adebisi (Manchester Metropolitan University, United Kingdom (Great Britain)); Haris Gačanin (RWTH Aachen University, Germany); Hikmet Sari (NJUPT & Sequans, France)
Advanced signal detectors pose a lot of technical challenges for designing signal detection methods in orthogonal frequency division multiplexing (OFDM) with index modulation (IM). Traditional signal detection methods such as maximum likelihood have an excessive complexity, and existing deep learning (DL) based detection methods can reduce the complexity significantly. To further improve the detection performance, in this paper, we propose a complex deep neural network (CDNN) and a complex convolution neural network (C-CNN) based intelligent signal detection method for OFDM-IM. Specifically, the proposed intelligent signal detection method is designed by C-DNN and C-CNN. The proposed signal detection methods for OFDM-IM use pilots to achieve semi-blind channel estimation, and to reconstruct the transmitted symbols based on channel state information (CSI). Simulation results are given to confirm the performance of the proposed signal detection method in terms of bit error rate and convergence speed.
José Calpa Juajinoy (Pontifical Catholic University of Rio de Janeiro, Brazil); Raimundo Sampaio-Neto (Cetuc-Puc-Rio, Brazil); João Cal-Braz (National Institute of Metrology, Quality and Technology (Inmetro) & PUC-Rio, Brazil)
This article presents an Index modulated MIMO-GFDM system. In addition to the diversity/multiplexing gains and appealing spectral characteristics typical of MIMO-GFDM systems, the use of index modulation (IM) applied to the subcarriers within a GFDM block results in attractive characteristics to the system, in terms of the spectral efficiency, detection performance and computational complexity. Two detection strategies for IM-MIMO-GFDM are presented, offering different detection performance and computation complexity balances. Ultimately, the computational results obtained offer a significant insight into the choice of the values of the index modulation block parameters in terms of achieving a favorable spectral efficiency/BER/computational complexity balance.
Victor Monzon Baeza and Ana Garcia Armada (Universidad Carlos III de Madrid, Spain)
The lack of orthogonal resources to increase the capacity of the communication networks has led to research on new ways to non-orthogonally multiplex the signals of several users. In this paper, we study the viability of multiplexing users’ signals by non-orthogonal constellation-based schemes against orthogonal classical multiple access techniques, such as time- or frequency-division multiple access, in non-coherent massive MIMO (NC mMIMO) systems, where the channel state information (CSI) is not required. We use the block error rate (BLER) and the throughput to analyze the behavior of the proposed multiuser NC mMIMO system based on DPSK. We assess the performance of multiplexing the users in the constellation against using other physical resources such as time or frequency. Based on this analysis, we propose a combination of both orthogonal and non-orthogonal schemes to reduce the complexity and increase the system throughput and capacity.
Guillaume Larue (Institut Polytechnique de Paris & Orange S.A., France); Louis-Adrien Dufrene and Quentin Lampin (Orange Labs, France); Paul Chollet, Hadi Ghauch and Ghaya Rekaya (Institut Polytechnique de Paris, France)
Neural belief propagation decoders were recently introduced by Nachmani et al. as a way to improve the decoding performance of belief propagation iterative algorithm for short to medium length linear block codes. The main idea behind these decoders is to represent belief propagation as a neural network, enabling adaptive weighting of the decoding process. In the present paper an efficient recurrent neural network architecture, based on gating and weights sharing mechanisms, is proposed to perform blind neural belief propagation decoding without prior knowledge of the coding scheme used by the encoder. The proposed architecture is able to learn to decode BCH (15,11) and BCH (15,7) codes and significantly improves the decoding performance over a standard belief propagation algorithm. A particular emphasis is given to the interpretability and complexity of the proposed model to ensure scalability to larger codes.
Lukasz Lopacinski (IHP, Germany); Nebojsa Maletic (IHP – Leibniz-Institut für Innovative Mikroelektronik, Germany); Alireza Hasani (Brandenburg University of Technology Cottbus-Senftenberg & IHP GmbH – Innovations for High Performance Microelectronics, Germany); Karthik KrishneGowda and Jesús Gutiérrez (IHP – Leibniz-Institut für Innovative Mikroelektronik, Germany); Rolf Kraemer (IHP Microelectronics, Frankfurt/Oder & BTU-Cottbus, Germany); Eckhard Grass (IHP & Humboldt-University Berlin, Germany)
In this paper, we investigate the use of bipolar Barker sequences for high-speed wireless communication based on Parallel Sequence Spread Spectrum (PSSS) modulation. To improve bit error rate (BER) performance for PSSS, we propose median detection at the receiver or DC-offset correction at the transmitter. Both techniques cancel the influence of cyclic-autocorrelation sidelobes that occur in the correlator in the receiver. The simulation results reveal that PSSS based on Barker codes of length 13 outperforms PSSS with m-sequences by 2.5 dB, if one of the abovementioned techniques is applied. Although different algorithms can cancel the effects of autocorrelation sidelobes in PSSS, our DC-correction method’s advantage is straightforward hardware realization and very low complexity. All modifications are performed in the PSSS transmitter, which is significantly less complex than the PSSS receiver. Thus, this algorithm improves PSSS BER and keeps the balance between the transmitter and receiver complexity.