IEEE/CIC International Conference on Communications in China

Session Opening: Opening Ceremony Session Keynote-1: Keynote 1
Session CIS-01: Data Privacy Session CIS-02: Network Security 1 Session CIS-03: Network Security 2
Session IT-01: Invited Talk 1 Session IT-02: Invited Talk 2
Session IoT-01: Space-air-ground IoT Session IoT-02: Green IoT Session IoT-03: Signal and Information Processing
Session MWN-01: Localization Session MWN-02: Edge Computing Session MWN-03: Internet of Vehicles
Session NGNI-01: Future Wireless Communications Session NGNI-02: Advanced Algorithms for Next Generation Networking Session NGNI-03: Security in Future Generation Networking
Session SAC-01: Offloading and Caching Session SAC-02: Learning-based Schemes Session SAC-03: Resource Allocation
Session SPC-01: NOMA Session SPC-02: MIMO Session SPC-03: Machine Learning
Session WCS-01: Intelligent Reflecting Surface Session WCS-04: NOMA Session WCS-02: Intelligence Communications I Session WCS-05: UAV Aided Wireless Communications Session WCS-03: Intelligence Communications II Session WCS-06: Channel Modeling
Session CT-01: Vehicular Communications

Signal Processing for Communications

Session SPC-01

NOMA

Conference
1:30 PM — 3:00 PM CST
Local
Aug 9 Sun, 10:30 PM — 12:00 AM PDT

Low Density Superposition Modulation using DCT for 5G NOMA scheme

Kun Lu and Sheng Wu (Beijing University of Posts and Telecommunications, China); Lihong Lv (Beijing Space Information Relay and Transmission Technology Research Center, China); Hongwen Yang (Beijing University of Posts and Telecommunications, China)

1
Non-orthogonal multiple access (NOMA) scheme is a promising multiple access technique for the fifth generation (5G) New Radio (NR) due to its high spectral efficiency, massive connectivity and low latency. In this paper, a Low Density Superposition Modulation (LDSM) using discrete-cosine transform (DCT) scheme with 5G-NR low-density parity-check (LDPC) channel code is proposed for the 5G scenario. Moreover, we adopt a low-complexity elementary signal estimator (ESE) detection algorithm for the multi-user detection. Simulation results show that our proposed scheme has the 3-5 dB peak-to-average power ratio (PAPR) reduction as compared with the conventional sparse code multiple access (SCMA) and pattern division multiple access (PDMA). Besides, our scheme brings about 0.6 dB-2.0 dB performance gains. Therefore, the DCT-LDSM scheme is efficient and suitable for 5G scenario.

Non-orthogonal Multiple Access in SWIPT Enabled Cooperative D2D Network

Rui Cheng, Xiaotian Zhou and Haixia Zhang (Shandong University, China); Fang Fang (The University of Manchester, United Kingdom (Great Britain)); Dongfeng Yuan (Shandong University, China)

1
In this paper, we investigate resource allocation in a downlink cooperative communication system. In the scenario where cellular users (CUs) and the base station (BS) cannot communicate directly, D2D user pairs participate in the cooperative communication to complete forwarding information required by CU and themselves through non-orthogonal multiple access (NOMA). The application of simultaneous wireless information and power transfer (SWIPT) ensures that D2D users save their own energy during the forwarding process. The ultimate optimization goal is to maximize the achievable rate of D2D user pairs under the condition of guaranteeing CU's quality of service (QoS). A stepwise iterative algorithm is proposed to obtain the suboptimal solution to the problem. By comparing with the ergodic optimal solution, numerical simulations show that the proposed algorithm can approximate the optimal solution with low computational cost in a certain error range.

Block Error Rate Analysis of Short-Packet NOMA Communications with Imperfect SIC

Ruiqiang Fu (Zhejiang University, China); Qiao Qi (ZheJiang University, China); Caijun Zhong, Xiaoming Chen and Zhaoyang Zhang (Zhejiang University, China)

0
In this paper, we study the cellular internet of things (IoT) with multi-user short-packet communications. To achieve low-latency as well as a high spectral efficiency, non-orthogonal multiple access (NOMA) technique is adopted. Considering the practical scenario with imperfect successive interference cancellation, this paper provides a detailed analysis of the average block error rate (BLER) of NOMA systems. Exact approximated expressions are derived for the BLER of an arbitrary user. In addition, to gain further insights, simplified BLER expressions of the worst and best users are obtained in the high signal to noise ratio regime. It was shown that the best user can achieve full diversity order while the worst user can only achieve unit diversity order. Extensive simulation results are provided to validate the analytical results.

Angle-Delay-Doppler Domain NOMA over Massive MIMO-OTFS Networks

Weidong Shao and Shun Zhang (Xidian University, China); Caijun Zhong (Zhejiang University, China); Xianfu Lei and Pingzhi Fan (Southwest Jiaotong University, China)

1
In this paper, we propose an uplink angle-delay-Doppler domain non-orthogonal multiple access (NOMA) scheme over massive multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) networks, which is inspired to address the situation that user connectivity is dramatically restricted if users have overlapped angle signature with limited delay-Doppler domain resources. To be specific, with aid of NOMA along the uplink transmission, we propose to schedule multiple users with the overlapped angle signature to employ the same delay-Doppler domain resources. Correspondingly, a user clustering algorithm and optimal transmission strategy with respect to power allocation for the proposed scheme are then designed. Afterwards, we propose a path following-based iteration algorithm to solve the original nonconvex optimization problem and finally obtain a suboptimal solution. Simulation results are provided to demonstrate the validity of the proposed scheme.

NDA-EVM based Co-channel Interference Analysis in Co-frequency Network

Xiaoping Zeng (Chongqing Communication Institute, China); Shiqi Li and Xin Jian (Chongqing University, China); Yang Fan (Chong Qing University & CCEE, China)

2
Transmission in co-frequency network always affected by co-channel interferences. In order to achieve higher communication performance, interference elimination schemes should be applied. Thus, analysis of co-channel interference in co-frequency network becomes the core issue, which offer threshold references to the designs of interference elimination and transmission mechanism. A novel method to quantify the co-channel interference based on nondata-aided error vector magnitude (NDA-EVM) was proposed in this paper. NDA-EVM was considered as a new metric to evaluate the change of the channels. Specifically, the NDA-EVM upper bound of multiple quadrature amplitude modulation (MQAM) signal under co-channel interferences is analytically derived. Theoretical analysis and simulation experiments indicate that, the derived upper bound closely matches with the theoretical one, especially at low SNR. Moreover, the time complexity of upper bound is linear order while that of theory is square order, which means it has a quicker react when channel estimating.
Session Chair

Shun Zhang, Feifei Gao

Session SPC-02

MIMO

Conference
3:10 PM — 4:40 PM CST
Local
Aug 10 Mon, 12:10 AM — 1:40 AM PDT

Data-enhanced Bayesian MIMO-OFDM Channel Estimation Strategy with Universal Noise Model

Jia-Cheng Jiang and Hui-Ming Wang (Xi'an Jiaotong University, China)

0
Model-based methods are dominant in current systems for their optimal designs under given models, but may suffer from inaccurate modeling assumptions. Recently, data-based deep learning methods have achieved remarkable performances by training a large amount of data but encounter some challenges such as, lack of available training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-driven methods, named model based method enhanced by data, which is training affordable, theoretically interpretable and model flexible. To show the idea more concretely, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel state information (CSI) acquisition approach. Specifically, we utilize a universal mixture of Gaussian (MoG) model to deal with the nongaussianity of the noise and interference in complex communication environments, which can adaptively adjust involved parameters to fit the true distribution by observed data. We propose a variational Bayesian framework to derive the specific form of minimum mean square error (MMSE) estimator. Simulations are performed to verify the efficiency of our proposed method and the accuracy of our analysis.

A New Real-Time Acoustic Echo Cancellation Algorithm Using Blind Source Separation and Multi-delay Filter

Xiuxiang Yang (Chongqing University of Posts and Telecommunications, China)

0
Adaptive algorithm is a traditional method for solving acoustic echo cancellation (AEC) problem, which need to perform well in different scenes, especially in the double talk (DT) scenarios. It is found that some algorithms designed for blind source separation (BSS) performs well in the DT scenario, which requires little prior information. However, in order to be used in real-time processing, the frame length of the input is required to be shorter, the performance will be worse accordingly. In this paper, we propose an algorithm framework based on BSS and multi-delay filter (MDF) for AEC, which the coefficients are updated by auxiliary independent vector analysis (AuxIVA) algorithm. The numerical studies including utterances corrupted by echo under different reverberation times that the improved algorithm outperforms the Speex AEC algorithm.

Low Complexity Activity Detection for Massive Access with Massive MIMO

Yongxin Liu (Tsinghua University, China); Shidong Zhou (Tsinghua University, Canada)

0
We propose a low complexity activity detection scheme for massive access scenarios with massive multiple-input multiple-output (MIMO) communication systems. Numerous devices with sporadic access behavior characterize these scenarios; therefore, only a subset is active. Limited by massive potential devices in the network and coherence time, which contains L signal dimensions, it is infeasible to assign a unique orthogonal pilot to each device in advance. In this case, device detection is the first critical problem to be solved. A compressed sensing-based (CS) activity detection algorithm has an excellent performance in the case of sparse active devices. However, due to the massive number of potential devices and non-orthogonal pilots, the algorithm's complexity is very high, and the base station (BS) needs much cache to store the pilot codebook for all potential devices is unacceptable in this scenario. In order to solve this problem, this paper proposes a low complexity activity detection scheme. The scheme uses a linear combination of orthogonal pilots to construct non-orthogonal pilots, which does not need to store the pilot codebook at BS. Also, we propose a device by device activity detection algorithm for this scheme. When the number of potential devices is less than L 2 - L, and the number of antennas goes to infinity, the error probability of the proposed algorithm approaches 0, and the complexity of the algorithm is 50-100 times lower than the compressed sensing-based algorithm.

Max-Min Energy-Efficient Multi-Cell Massive MIMO Transmission Exploiting Statistical CSI

Yufei Huang, Li You, Jiayuan Xiong, Wenjin Wang and Xiqi Gao (Southeast University, China)

0
With the dramatic increment of data traffic, energy efficiency has become a critical concern in the beyond 5G mobile system. In this paper, we study the max-min fairness-based energy efficient transmission strategy design for multi-cell massive multiple-input multiple-output (MIMO) downlink transmission, exploiting the statistical channel state information (CSI). We first derive the closed-form eigenvectors of the optimal downlink transmit covariance matrices, which reduces the original precoding design into a power allocation problem. Then, by exploiting the minorization-maximization procedure and Dinkelbach's transform, we propose an iterative energy-efficient power allocation algorithm with low complexity and guaranteed convergence. The performance gain of the proposed algorithm over other baselines is demonstrated in the numerical results.

Analysis on Functions and Characteristics of the Rician Phase Distribution

Zhongtao Luo and Yanmei Zhan (Chongqing University of Posts and Telecommunications, China); Edmond Jonckheere (USC, USA)

0
For a complex variable consisting of the deterministic signal and the zero-mean complex Gaussian noise, the module and phase follow the Rice distribution and the Rician phase distribution, respectively. This paper discusses the distribution functions and numerical characteristics of the Rician phase distribution. For the unwrapped noise and the wrapped noise, we present the noise distributions and then develop the functions of probability density and cumulative distribution. The formulas of the mean and the variance are derived. Besides, the variance of the unwrapped noise is approximated in closed-form. The relationship between the characteristics and the parameters are analyzed. This paper provides fundamental analysis and preparation for signal processing in the phase domain.
Session Chair

Hui-Ming Wang, Li You

Session SPC-03

Machine Learning

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 1:50 AM — 3:20 AM PDT

Deep Learning based Intelligent Recognition Method in Heterogeneous Communication Networks

Hao Gu (`Nanjing University of Posts and Telecommunications, China); Yu Wang and Sheng Hong (Nanjing University of Posts and Telecommunications, China); Yongjun Xu (Chongqing University of Posts and Telecommunications, China); Guan Gui (Nanjing University of Posts and Telecommunications, China)

0
Friendly signal coexistence problem over unlicensed bands has been received strongly attention in design next-generation wireless communication systems. Typically, it is very challenge to recognize wireless fidelity (WiFi) signal and long-term evolution (LTE) signal over the unlicensed bands (LTE-U) in heterogeneous communication networks. The main reason is that LTE-U may occupy the spectrum resources of WiFi. Hence it is necessary to solve this problem and then to lay the foundation for the friendly coexistence of LTE-U and WiFi technology. In this paper, we proposed a deep learning based intelligent recognition method for identifying LTE-U and WiFi signals in heterogeneous communication networks. First, we collect LTE-U and WiFi signal samples and introduce random phase offset and two data forms to them. Second, we use deep learning algorithms to train these samples to get the best preprocessing method and neural network algorithm parameters. Finally, experiments are conducted to show that our proposed method can efficiently recognize LTE and WiFi signals with excellent recognition accuracy and robustness.

Massive MIMO Data Detection Using 1-dimensional Convolutional Neural Network

Isayiyas Nigatu Tiba and Ben Baraka Kulimushi (Xidian University, China); Chrianus Kajuna (St. Joseph University in Tanzania, Tanzania)

0
In this work, we explore the use of an adaptive one-dimensional convolutional neural network (1d-CNN) for the massive multiple-input multiple-output (MIMO) data detection. To be able to detect under the randomly varying channel scenario, we employ a data augmentation approach along with the convolutional networks. Our method is simple, and a non-iterative which works by unfolding the potential of deep networks. We construct datasets corresponding to 100s of base station antennas serving 10s of transmit antennas to show that increasing the number of base station antennas will also increase the learning ability of the network by proving the relevant information. We will show through simulation that the proposed method can significantly improve the learning and ability to achieve competitive performances compared to the traditional detectors.

Location Aided Intelligent Deep Learning Channel Estimation for Millimeter Wave Communications

Xintong Lin, Lin Zhang and Yuan Jiang (Sun Yat-sen University, China)

0
Millimeter wave (MMW) communication provides a promising solution for high data rate services thanks to the wide MMW bandwidth. However, the channel conditions may vary more dramatically due to MMW transmissions, thus MMW receivers require the intelligent channel estimation with the low complexity to attain reliable and high data rate performances. In this paper, considering the characteristic property of the line of sight transmission over MMW bands, we propose to utilize the location information to evaluate the channel frequency response (CFR) together with the deep learning method based on the propagation model. In our design, we consider the scenario of the 60 GHz wireless local area network (WLAN) systems. At the receiver, the deep neural network (DNN) used for the channel estimation (CE) is trained offline using the pilots and location coordinates as inputs and the known CFRs as outputs. Then at the online deployment stage, with the trained neural network architecture, MMW receiver can retrieve the CFR intelligently. Simulation results demonstrate the proposed location aided DNN channel estimation can achieve lower normalized mean square error (NMSE), while providing intelligent CE with lower complexity.

Message Structure Aided Attentional Convolution Network for RF Device Fingerprinting

Lintianran Weng and Jianhua Peng (PLA Strategic Support Force Information Engineering University, China); Jinsong Li (People's Liberation Army Strategic Support Force Information Engineering University & National Digital Switching System Engineering and Technological Center, China); and Yuhang Zhu (PLA Strategic Support Force Information Engineering University, China)

0
RF device fingerprinting has become an emerging technology which identifies the device-specific fingerprint based on inherent defects in the hardware. However, existing methods pay little attention to the potential improvement of rough priori information such as message structure on the identification performance. In this paper, we propose a message structure aided attentional convolution network (MSACN) for RF device fingerprinting. Portions with different pulse waveform distribution are separated and fed into the identification network. The network extracts and merges the feature map contained in multiple data blocks, which is helpful to explore the internal relation of data. Furthermore, we design a spatial attention mechanism for low-dimensional discrete signals to pursue more efficient feature fusion. Experimental results on the dataset of real-world ADS-B transmissions show that MSACN can achieve 98.20% identification accuracy outperforming previous works.

Research on Human Activity Recognition Technology under the Condition of Through-the-wall

Ruoyu Cao, Xiaolong Yang, Zhenhua Yang, Mu Zhou and Xie Liangbo (Chongqing University of Posts and Telecommunications, China)

0
Wi-Fi-based human activity recognition is playing a critical role in wireless sensing. However, the existing through-wall human activity recognition method does not fully analyze the influence of the wall on the signal, which results in poor robustness of the Wi-Fi indoor human activity recognition system. In order to solve this problem, this paper proposes a Wi-Fi based activity recognition algorithm under through-the-wall scenarios. First, the distribution of Wi-Fi signals in the presence of wall barriers is analyzed according to the Wi-Fi signal model. Then, according to the distribution characteristics of different Wi-Fi signals, the principal component analysis (PCA) algorithm is used to reconstruct the signal to complete the de-nosing processing of the Wi-Fi signal. Finally, feature extraction and feature classification in the time-frequency domain is performed to complete the human activity recognition. It is worth mentioning that in terms of feature extraction, we innovatively use the empirical mode decomposition (EMD) algorithm to extract the difference in time series of similar actions. Experimental results show that the system achieves an average accuracy of 95.82 percent in through-the-wall scenarios.
Session Chair

Jiang Xue, Feifei Gao

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