Sparse code multiple access (SCMA) has been one of non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G wireless communication networks. Conventional SCMA approaches are confronting remarkable challenges in designing low complexity high accuracy decoding algorithm and constructing optimum codebooks. Fortunately, the recent spotlighted deep learning technologies are of significant potentials in solving many communication engineering problems. Inspired by this, we explore approaches to improve SCMA performances with the help of deep learning methods. We propose and train a deep neural network (DNN) called DL-SCMA to learn to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN). Putting encoding and decoding together, an autoencoder called AE-SCMA is established and trained to generate optimal SCMA codewords and reconstruct original bits. Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed. Simulations show that the DNN SCMA decoder significantly outperforms the conventional message passing algorithm (MPA) in terms of bit error rate (BER), symbol error rate (SER) and computational complexity, and AE-SCMA also demonstrates better performances via constructing better SCMA codebooks. The performance of deep learning aided DCMA is superior to the SCMA.As depicted in Fig. 1, consider J users transmitting data bits over the same K sub-carriers of OFDM, here K < J such that overloading is provided. According to SCMA encoder, each user maps every m = log 2 (M ) bits into a K-dimensional complex codeword c with only N non-zero elements standing for QAM modulation and LDS spreading combination, here N < K. The overlapping degree is d f = JN K , and overloading ratio is λ = J K . There are M codewords forming a codebook for each user and each codebook is unique. The encoding procedure can be described by c = f (b), where b ∈ B log2(M ) and c ∈ C ⊂ C K with |C| = M . Function f is actually a mapping matrix which

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Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub (https://github.com/haiping1010/DeepBindPoc) and the webserver is available at (http://cbblab.siat.ac.cn/DeepBindPoc/index.php).

As the researches on Networked Control & Cyber-Physical System (NCCPS) are growing, the requirement of reliable evaluation tools for these systems is urgent. There are several simulators, such as TureTime, Ptolemy II and so on, can be used, but they usually focus on modeling of the control dynamics, and are too simple and abstracted on the simulation of network communication. In this work, a co-simulation tool, NCCPIS is presented, which integrates the dynamic control system simulator, Ptolemy II and the network simulator, NS-2. We demonstrate the validation of the tool by presenting a case study of platoon longitudinal control in AHS (Automatic Highway System). Related WorkAs a most popular tool for validating NCS, TrueTime [7] extends Matlab/Simulink with platform related modeling concepts (i.e., network, clock and schedulers) and supports simulation of networked and embedded control systems with implementation effects [3]. However, in TureTime, the modeling of network dynamics are highly abstracted, thus it's not appropriate to evaluate the systems that require detailed low layer network communication. For different considerations, combining different control system simulators with network simulators, some similar ideas of seeking co-simulating methods exist in a few articles. In [16], an evaluation tool called NCSWT was developed, which integrated Matlab/Simulink and NS-2 using the HLA standard for coordinating data communication and time synchronization. In [1], two approaches of extending NS-2 and one of integrating Modelica and NS-2 have been proposed. In [6], for WNCS (Wireless NCS) over MANET (Mobile ad-hoc Network), the SIMULINK-OPNET co-simulation was investigated.

From Cyber Physical System (CPS) perspective, it is nature for the system to tightly couple the communications and computing aspects with its physical dynamics. In this paper, we investigate this characteristic for multi-agent formation control in Mobile Ad-hoc Network (MANET). We manage to measure the real time wireless network congestion, implement a wireless QoS mechanism to provide different network channel access priorities and come up with the emergency calculation. Based on these, we present our adaptive approach for formation control. To examine the benefit of our approach, we use a co-simulation tool that we have developed for evaluating networked control & cyber physical system to study a specific scenario, in which five follower agents plus one leader perform a specific formation within a MANET with different congestion conditions. Through simulation experiments, it is showed that our approach could adaptively adjust sample periods and network channel access priorities according to real time dynamics emergencies and the network traffics condition, so as to optimize network utilization and improve the formation control performance.