Secure Transmission With Randomized Constellation Rotation for Downlink Sparse Code Multiple Access System

Sparse code multiple access (SCMA), as a codebook-based non-orthogonal multiple access (NOMA) technique, has received research attention in recent years. The codebook design problem for SCMA has also been studied to some extent since codebook choices are highly related to the system's error rate performance. In this paper, we approach the SCMA codebook design problem by formulating an optimization problem to maximize the minimum Euclidean distance (MED) of superimposed codewords under power constraints. While SCMA codebooks with a larger MED are expected to obtain a better BER performance, no optimal SCMA codebook in terms of MED maximization, to the authors' best knowledge, has been reported in the SCMA literature yet. In this paper, a new iterative algorithm based on alternating maximization with exact penalty is proposed for the MED maximization problem. The proposed algorithm, when supplied with appropriate initial points and parameters, achieves a set of codebooks of all users whose MED is larger than any previously reported results. A Lagrange dual problem is derived which provides an upper bound of MED of any set of codebooks. Even though there is still a nonzero gap between the achieved MED and the upper bound given by the dual problem, simulation results demonstrate clear advantages in error rate performances of the proposed set of codebooks over all existing ones not only in AWGN channels but also in some downlink scenarios that fit in 5G/NR applications, making it a good codebook candidate thereof. The proposed set of SCMA codebooks, however, are not shown to outperform existing ones in uplink channels or in the case where non-consecutive OFDMA subcarriers are used. The correctness and accuracy of error curves in the simulation results are further confirmed by the coincidences with the theoretical upper bounds of error rates derived for any given set of codebooks.

To improve the spectrum efficiency (SE), sparse code multiple access (SCMA) has been proposed as an candidate for 5G wireless networks. Although SCMA has good SE performance, it suffers from high detection complexity, which may degrade its energy efficiency (EE) performance. To make up for this deficiency, we in this paper jointly consider codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment to investigate the detection complexity minimization problem for uplink SCMA networks. To tackle this hard problem effectively, we first borrow the idea of dual coordinate search to devise a cost-efficient algorithm to determine the mapping matrix and codebook assignment. Based on the mapping matrix, we exploit the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. Finally, we present some simulations to illustrate the performance gain of our proposed algorithm as compared with other schemes.

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access technology for future wireless communication systems. In the SCMA detector, deep learning (DL) technology has been adopted to improve the detection efficiency. However, most previous schemes are completely data-driven designed without using prior knowledge. In this letter, we propose a knowledge-based deep learning detection scheme (K-DLD) that incorporates prior knowledge into SCMA detection to lighten the neural network. Moreover, we propose to use a thinner but deeper network to further reduce the model complexity. Simulation results show that the proposed schemes significantly reduce the computation time without any performance loss.

The implementation of Multiple Input Multiple Output (MIMO) on the Sparse Code Multiple Access (SCMA) can enhance performance in 5G future communication. Low complexity detector for the MIMO-SCMA system to achieve near-optimum bit error rate (BER) performance is still hot topic in this research field. This paper is investigate low complexity MIMO-SCMA detector by combining a joint factor graph (JFG) and sphere decoding (SD) algorithm based on message passing algorithm (MPA) called by SD-JFG-MPA. SD-JFG-MPA is combine n-th single factor graph become one virtual factor graph to deal with MIMO detector. SD algorithm is used to reduce high complexity of the JFG-MPA by calculating possible combination codeword within search radius R SD . SD-JFG-MPA is investigated under Rayleigh channel model with overloading factor 150%. The simulation result shows that complexity and BER performance is determined by a selected radius of SD-JFG-MPA. Optimum performance with less processing time for one bit error on single SCMA when $R_{SD} = \sqrt {E_b} \times 3/2 \times r_{max}$ meanwhile for 2x2 MIMO-SCMA when $R_{SD} = \sqrt {E_b} \times N_{mimo} \times r_{max}$. 2×2 MIMO-SCMA performance is better with 5 dB gap at BER = 10−3 compared to single SCMA.

5 G networks may not be competent in fully supporting the high data rates, system capacity, and spectral efficiency expected in the upcoming 6 G networks. An enabling factor to achieve these requirements is the adoption of the Sparse Code Multiple Access (SCMA), which is one of the most promising code-domain Non-Orthogonal Multiple Access (NOMA) techniques. Nowadays, there are two major challenges for the implementation of SCMA in practical networks. First, the design of a near-optimal codebook set at the transmitter, that heavily impacts the network performance. Second, an efficient decoding algorithm at the receiver, that dominates the computational complexity of the system. The two aspects are not independent from each other; therefore the optimization of an SCMA network should address jointly both the Encoder and the Decoder side. Moreover, such solution is far from optimal when designed for general purpose use, since it does not exploit the channel statistical information known at the transmitter and the receiver side. However, most of the works available in the literature address only one aspect and also are not tailored for a given radio channel. In this paper, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art AutoEncoder (AE) architecture and a novel Wasserstein Generative Adversarial Network (WGAN). The aim is to jointly create a near-optimal codebook design and a non-computationally heavy Decoder that are robust to the given channel statistics. Finally, we trained our model to be flexible to support different channel conditions and compared its performance against the state-of-the-art AE and the best conventional Decoders. The simulation results show that our innovative design exhibits decoding performance very similar to conventional Decoders with a reduced decoding latency.

Sparse code multiple access (SCMA) is an up-and-coming non-orthogonal multiple access technology. The message passing algorithm (MPA) is utilized for multi-user signal detection in the SCMA system to achieve a performance close to the maximum a posterior algorithm. However, higher iterations will lead to the higher computational complexity of the MPA. In this paper, a dynamic threshold and stability judgment based MPA scheme (DTS-MPA) is proposed to reduce the computational complexity by simplifying the factor graph and reducing the number of iterations. Firstly, an adaptive dynamic threshold is set to minimize the number of iterations in the different channel state information. Secondly, an additional iteration is proposed to determine whether the variable nodes that meet the dynamic threshold reach stable convergence to improve the decoding reliability. Variable nodes that meet the above conditions will be decoded in advance. Numerical results show that the DTS-MPA can significantly reduce the computational complexity of the MPA and achieve a significant trade-off between bit error rate performance and computational complexity.

ABSTRACTFor the next‐generation communication systems, to improve spectral efficiency and increase the data rate, new multiple access techniques have been investigated. Orthogonal multiple access techniques are widely used in traditional communication systems while nonorthogonal multiple access (NOMA), proposed in 5G, has been a promising technology for satisfying the demand for future wireless communication networks. Sparse code multiple access (SCMA) is a code‐domain NOMA method that provides diversity gain with signal constellation coding. However, to increase the performance of SCMA, there are only limited works provided in the literature in terms of codebook design and receiver design. In this paper, a new multiple‐access model is proposed by applying various diversity techniques for downlink SCMA. The performance of the proposed model is evaluated with both computer simulations and theoretical analysis. Results show that the proposed model provides a 1.6 dB gain in terms of the bit error rate (BER) under the Rayleigh fading channel.

In this letter, we consider a sparse code multiple access (SCMA) system, in which we assign the subcarriers to users based on the available channel gains between the users and base station over all subcarriers, referred to as channel state information. The effect of this process, referred to here as codebook assignment, has not been considered before on link-level performance of an SCMA. We propose three codebook assignment methods and compare their performance with each other as well as with the conventional case, where no assignment is performed. It is shown that significant performance improvement is achieved when any of the proposed methods is deployed. Through one of the proposed methods, we show that a trade-off between user performance fairness and performance is achievable.

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

Sparse code multiple access (SCMA) has excellent application prospects due to its high spectral efficiency and accsess capacity. However, due to the nonorthogonal characteristic of SCMA in code domain, the codebook needs to be manually designed for all communication scenarios, and the receiver has high computational complexity. To address this issue, deep neural network-aided SCMA (DNN-SCMA) is proposed, but it is difficult to capture channel state information (CSI) in the dynamic and time-varying communication scenarios, which hinders the overall learning and optimization fot end-to-end communications. This paper proposes a stochastic channel model with conditional generative adversarial network (CGAN) for DNN-aided SCMA in a data-driven way. Particularly, a model-free learning method is adopted to accurately learn different types of random channel models, which realizes effective acquisition of dynamic channel information. Finally, the end-to-end training is achieved through the use of back propagation (BP), and then by an iterative training of the composed networks, the end-to-end loss can be optimized in a supervised manner. Results show the feasibility of CGAN-based channel modeling in end-to-end DNN-SCMA.

Sparse Code Multiple Access (SCMA) is a promising non-orthogonal multiple access (NOMA) technique in future wireless communications. In this letter, a novel uplink codebook optimization method for SCMA using evolution strategies is proposed. Unlike traditional multi-stage codebook designs, we use a single-stage codebook construction method and directly optimizes the entire codebook. Fitness functions suitable for different uplink channels are proposed to jointly optimize various key metrics in the corresponding channels. By comparing our codebooks with various benchmark codebooks, simulation results show that the proposed method can significantly reduce the bit error rate in various uplink channels.

Sparse code multiple access (SCMA) is one of the most concerned non-orthogonal multiple access schemes in 5G communication system. Meanwhile, device-to-device (D2D) communication is also one of the key technologies for 5G network to improve the spectral efficiency. In cellular systems with SCMA, the non-orthogonal nature renders resource blocks support for more users so that an overloaded system can be achieved. To further improve the system capacity, a D2D and uplink cellular coexisting network is proposed in this paper. The interference between D2D and cellular users are handled. Since conventional message passing algorithm (MPA) has the exponential complexity, the receiver for cellular users is based on some low complexity methods and that for D2D users is also associated with this method. Simulation results show that the proposed hybrid network has an acceptable BER performance loss compared to the general uplink SCMA system, while the proposed SCMA receiver can reduce the decoding complexity significantly compared to the conventional MPA.

In this paper, we consider the sparse code multiple access (SCMA) for low earth orbit (LEO) satellite communications to achieve high user capacity and provide technical support for satellite-based Internet of Things (IoT) applications. Note that Doppler shift in the LEO satellite link can not be ignored and the message passing algorithm (MPA) extensively utilized by SCMA is quite time-consuming due to high complexity. Thus we propose a coherent iterative receiver with low complexity for SCMA-based LEO satellite communication systems. First, we design a data-aided carrier synchronization scheme using multiple disjoint pilot blocks to estimate the Doppler shift precisely. Then, a soft-out approximate message passing (SO-AMP) algorithm with linear complexity is derived from the first-order approximation of MPA based on a novel framework of SCMA mapper. Further, a damping method is introduced into the SO-AMP detector developing a damped SO-AMP (DSO- AMP) to improve the overall performance. Numerical results show that the designed receiver can achieve excellent performance close to ideal performance with no Doppler shift while maintaining low complexity.

In 4G systems, Orthogonal Frequency-Division Multiple Access (OFDMA) has been used conventionally for multiple access purposes. This technique has low spectral efficiency since it allocates the resources orthogonally to each user. As an alternative to this technique, Non-orthogonal Multiple Access (NOMA) has been proposed for new generation systems as it allows different users to use the same resources and therefore, increases spectral efficiency. Sparse Code Multiple Access (SCMA) is a code-based NOMA technique and its performance depends on codebook design. In this study, a conventionally used codebook design in the literature has been considered and the system performance has been improved by increasing the distance between the signal constellation points. Considering two different design approaches, the conventional codebook has been modified and about 1dB gain has been achieved in the high signal-to-noiseratio (SNR) region.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.