Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

SDM transmission of orbital angular momentum mode channels over a multi-ring-core fibre

Spatial division multiplexed optical transmission over a multi-ring-core orbital angular momentum (OAM) fibre is reported for the first time. The seven cores in the fibre each supports OAM modes belonging to mode groups (MGs) of topological charge |l| = 0–4. The MGs of |l| = 1–4 each contains four near-degenerate OAM modes that carry the combinations of opposite orbital and spin angular momenta. The weak coupling between these higher-order MGs as well as between the cores enables the simultaneous transmission of 56 OAM mode channels (two MGs per core of the topological charges |l| = 2 and 3) over the 60-km span, while only requiring modular 4 × 4 multi-input multi-output (MIMO) signal processing to equalize the mixing among the four mode channels in each MG that are strongly coupled – a feature that also minimizes the number of filter taps. The mode channels are launched using seven-core single-mode fibre fan-in devices, with the light in all seven cores converted into OAM modes via specially designed plates that carry seven off-axis-compensated phase masks matching the hexagonal configuration of the multi-core fibres. Each mode channel carries 10 WDM wavelengths, equivalently aggregating to a capacity of 31.4 Tbit/s (net 25.1 Tb/s) and a spectral efficiency (SE) of 62.7 bit/s/Hz (net 50.2 bit/s/Hz) with 28-GBaud QPSK modulation per data channel.

Satellite Navigation and Communication Integration Based on Correlation Domain Indefinite Pulse Position Modulation Signal

Ubiquitous signal coverage is a basic demand of Internet of Things (IoT) communications, which meets the feature of satellite communications. Infinite user number is a basic demand of IoT location-based services, which meets the feature of Global Navigation Satellite System (GNSS). Both of these demands make Satellite Navigation and Communication Integration (SNCI) an important supporting technology for IoT. Inherited from the satellite communications system, GNSS itself has a certain data transmission capacity. Thus, enhancing the communication function of the GNSS is a promising means of achieving SNCI. Considering that a unified signal system cannot currently realize high-precision positioning and high-speed data transmission simultaneously in SNCI, this project proposes a Correlation Domain Indefinite Pulse Position Modulation (CDIPPM). A pilot channel and a data channel are introduced in this technology, which are distinguished by Code Division Multiplexing (CDMA). The synchronization function is provided by the pilot channel, thereby freeing the data channel of this function. The phase of the pseudorandom code can then be used as the carrier of information. In order to transmit more information, the transmitter of the proposed technology superimposes on the data channel multiple sets of spread spectrum sequence, which are generated from one set of spread spectrum sequence by different cyclic shifting operations. The receiver will identify the number and location of the correlation function peaks by a detection algorithm and recover the message. It can be seen by theoretical analysis and simulation verification. The technology can significantly improve satellite data transmission rates and maintain the original positioning function while minimizing change in the original GNSS signal. Therefore, the SNCI system based on this technology has the following advantages: a unified signal system, high positioning accuracy, high data transmission rate, and a backward navigation function, and it is easy to promote.

Hardware Sharing for Channel Interleavers in 5G NR Standard

Interleaver module is an important part of modern mobile communication system. It plays an important role in reducing bit error rate and improving transmission efficiency over fading channels. In 5G NR (5th Generation New Radio) standards, LDPC (low-density parity-check) and polar channel codes are employed for data channels and control channels, respectively. If multiple interleavers are implemented separately for them, the cost increases significantly. To address this issue, a hardware multiplexing scheme for channel interleavers based on LDPC and polar codes is proposed in this paper. Firstly, the formulas for the processes of the control channel interleaving and data channel interleaving are derived with respect to 5G NR standard. Then, the hardware implementation structures of the two interleavers are given. Subsequently, hardware reuse is proposed by sharing the similar or identical parts between the two hardware structures. Simulation results verify the correctness of our proposed scheme and demonstrate that it can realize the hardware sharing of the two kinds of channel interleavers to reduce the cost of silicon.

Random numerical linear precoding and channel estimation in massive MIMO systems

The information growth we have experienced in the immediate past and which continues to increase has consequently brought about the big data era and when pooled with the vast increase in subscriber numbers has led to an ever-escalating demand for more efficient and high-capacity communication systems. The affinity for higher capacity and efficient networks has necessitated the initiation of wireless fifth generation (5G) networks. Among the key technologies underlying the wireless 5G network are massive Multiple-Input Multiple-Output (MIMO) and Cloud Radio Access Network (C-RAN) which enhances spectral efficiency, energy efficiency, security and robustness but suffers from pilot contamination and fronthaul finite capacity. There have been several attempts to minimize pilot contamination in massive MIMO system through linear precoding. But for those precoding schemes with good performance, they suffer from intricate problem of matrix inversion owing to large antenna numbers inherent in massive MIMO system, yet they do not render themselves readily to hardware parallelization. Also, channel state information estimation remains a challenge within massive MIMO networks. While the finite fronthaul capacity remains a bottleneck in C-RAN network systems. This study presents the formulation of iterative linear precoder that is efficiently parallelizable with efficient channel estimators for massive MIMO and massive MIMO partially centralised CRAN networks. The channel precoder was formulated and adapted using the iterative linear Rapid Numerical Algorithm (RNA). This model was then extended to include coordination among multicell massive MIMO system with receive combining computational complexity and efficiency evaluation. RNA model is again used to formulate improved linear and semi-blind channel estimators for massive MIMO systems in combination with the Fast Data Projection Method (FDPM). The semi-blind channel estimator is combined with compressed data channel estimator then extended based on Givens transformations and Data Projection Method (DPM) for massive MIMO partially centralised C-RAN networks. And finally, the estimation of the signal-to-interference-to-noise ratio, bit error rates, spectral efficiency, energy efficiency and normalised mean square error for the respective modelled components was realized. The models above were simulated using MATLAB for the analysis and validation. The TDD downlink massive MIMO system was considered with varying immediate channel state information qualities for the single cell and multicell systems. For single cell system, there was optimal performance with regard to the signal-to-interference-to-noise ratio and the bit error rate when rapid numerical algorithm was used to implement the matrix inversion process in comparison to existing methods. It also rendered the precoding process highly parallelizable further reducing the complexity. For instance, for base transceiver station with 128 antennas serving 32 user terminals at signal-to-interference-to-noise ratio = 20 the average per user terminal rate was: RNA = 5 bit/sec/Hz, Regularized Zero Forcing (RZF) = 5 bit/sec/Hz and Truncated polynomial Expansion (TPE at J = 2) = 2.9 bit/sec/Hz. For the case of the Bit Error Rate (BER), for base transceiver station with 128 antennas serving 32 user terminals at signalto-interference-to-noise ratio = 10 the BER was: RNA = 1, Regularized Zero Forcing (RZF) = 1 and TPE (J = 2) = 5. For the multicell massive MIMO, it was found that the performance of rapid numerical algorithm implementation gave a good spectral efficiency and energy efficiency performance in comparison to existing methods while lowering the complexity further through parallelization. The compressed data channel estimator gave comparable performance for the spectral efficiency and normalized mean square error when compared to the improved linear channel estimators. The semi-blind channel estimators for both massive MIMO and massive MIMO partially centralised C-RAN outperformed the linear channel estimators as well as the compressed data channel estimator. These results demonstrate that rapid numerical algorithm can effectively eliminate the intricate matrix inversion associated with linear precoding while rendering itself to efficient parallelization. It also shows that the compressed data channel estimator optimally estimates the channel covariance matrix while reducing the amount of channel state information transmitted in estimation process. The semi-blind channel estimators have the optimal performance with regard to the normalised mean square error. It was also illustrated that the Givens transformation based semi-blind estimator outperforms the FDPM based semi-blind channel estimator.

PERSPECTIVES OF APPLYING WEBRTC FOR REMOTE-CONTROLLED MINING EQUIPMENT

Currently a lot of mining companies, such as Caterpillar, Sandvik, Atlas Copco and Komatsu are developing solutions for machines remote control and mining process automation. The purpose of these technologies is to increase labor efficiency and safety. Solutions for remote control should establish secure connection and transfer data with low latency – this could be implemented with WebRTC technology. Several problems were revealed during open data analysis of Cisco, Sandvik, Moxa and Acksys remote control solutions – using of expensive IP-cameras, sophisticated network and security design. WebRTC could solve these and several other problems. WebRTC operation principles reviewed further: initial information exchange via signaling server, use of ICE for discovering shortest path between peers and establishing peer-topeer connection. This could simplify network design and allow to use more cheap USB cameras instead of IP-cameras. For security reasons WebRTC encrypts transmitted data with DTLS and SRTP algorithms. Encryption key fingerprints are exchanged over signaling server; after connection establishment, peers are exchanging keys itself over discovered route. But WebRTC specification does not define peer to signaling server communication, which may lead to breach in unsecure data channel, especially man-in-the-middle attack. To prevent this, software engineer should ensure that connection with signaling server is secure. Mining machine model was developed to test data transmission latency. In this model, Raspberry Pi single-board computer is responsible for wireless connection, video encoding and commands processing. Received commands are passed to Arduino controller, which operates electric engines controller. Three remote control scenarios were tested – model is near the operator and in direct line of sight; model is near operator, but not in direct line of sight; model and operator are far away from each other (over 1600 km), model controlled over Internet. Test results shows that transmission latency does not exceed 300 ms, which is suitable for real-time remote driving.