Sparse Code Multiple Access Codewords Research Articles

SWIPT-Aided Device-to-Device Communications for Massive IoT Networks: A Novel Resource Allocation With Sparse Code Multiple Access

Spectral efficiency (SE) and energy efficiency (EE) are crucial for next-generation wireless Internet of things networks. Device-to-device (D2D) communications and sparse code multiple access (SCMA) are promising solutions to meet the high quality of service. This requires a proper interference management for an ultra-dense user system. Considering both resource and power allocation, we herein aim to optimize the SE and improve the EE. First, rate fairness is considered to assign SCMA codewords to both cellular and D2D users. Subsequently, we propose a beamforming design and power allocation algorithm for D2D communications underlaying downlink and uplink operation of cellular networks, wherein energy harvesting is involved as a simultaneous wireless information and power transfer to assist an SCMA network.

A Design of Low-Projection SCMA Codebooks for Ultra-Low Decoding Complexity in Downlink IoT Networks

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

OTFS-SCMA: A Code-Domain NOMA Approach for Orthogonal Time Frequency Space Modulation

Orthogonal time frequency space (OTFS) modulation is a two-dimensional (2-D) modulation technique that has the potential to overcome the challenges faced by orthogonal frequency division multiplexing (OFDM) in high Doppler environments. The performance of OTFS in a multi-user scenario with orthogonal multiple access (OMA) techniques has been impressive. Due to the requirement of massive connectivity in 5G and beyond, it is essential to devise and examine the OTFS system with the existing non-orthogonal multiple access (NOMA) techniques. This paper proposes a multi-user OTFS system based on a code-domain NOMA technique called sparse code multiple access (SCMA). This system is referred to as the OTFS-SCMA model. The framework for OTFS-SCMA is designed for both downlink and uplink. First, the sparse SCMA codewords are strategically placed on the delay-Doppler plane. The overall overloading factor of the OTFS-SCMA system is equal to that of the underlying basic SCMA system. The receiver in downlink performs the detection in two sequential phases: first, the conventional OTFS detection using the method of linear minimum mean square error (LMMSE) estimation, and then the SCMA detection. We propose a single-phase detector based on a message-passing algorithm (MPA) to detect multiple users’ symbols for the uplink. The expressions for the asymptotic diversity orders of the proposed OTFS-SCMA system are derived for downlink and uplink. OTFS-SCMA provides a significant diversity gain over other multiple access systems for OTFS. Based on the diversity analysis, an algorithm is proposed to devise an optimal codeword allocation scheme. The performance of the proposed OTFS-SCMA system is validated through extensive simulations both in downlink and uplink. We consider delay-Doppler planes of different parameters and various SCMA systems of overloading factor up to 200%. The performance of OTFS-SCMA is compared with those of the existing OTFS-OMA, OFDM-SCMA and OTFS-power-domain (PD)-NOMA techniques. The analysis of OTFS-SCMA with channel estimation is also presented along with the BER performance.

Improving Spectral Efficiency in the SCMA Uplink Channel

The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of users connected simultaneously. 5G uses non-orthogonal multiple access (NOMA) to increase the number of simultaneously connected users, and by encoding data it is possible to increase the spectral efficiency (SE). In this work, eight codewords are used to transmit three bits simultaneously using Sparse Code Multiple Access (SCMA), and through singular value decomposition (SVD) the Euclidean distance between constellation points is optimized. On the other hand, applications of machine intelligence and machine intelligence in 5G and beyond communication systems are still developing; in this sense, in this work we propose to use machine learning for detecting and decoding the SCMA codewords using neural networks. This paper focuses on the Use Case of enhanced mobile broadband (eMBB), where higher data rates are required, with a large number of users connected and low mobility. The simulation results show that it is possible to transmit three bits simultaneously with a low bit error rate (BER) using SVD-SCMA in the uplink channel. Our simulation results were compared against recent methods that use spatial modulation (SM) and antenna arrays in order to increase spectral efficiency. In adverse Signal-to-Noise Ratio (SNR), our proposal performs better than SM, and antenna arrays are not needed for transmission or reception.

SPARSE CODE MULTIPLE ACCESS CODEBOOK DESIGN USING SINGULAR VALUE DECOMPOSITION

Currently, sparse code multiple access (SCMA) is a commonly used multiple-access technique, and it is a strong candidate for implementation as part of the fifth generation (5G) of wireless mobile communications. Although several design methods are available for SCMA codebooks, we propose a new method that optimizes point-to-point distances within the same codeword and from codebook-to-codebook for the same carrier based on singular value decomposition (SVD). A neural network-based receiver is proposed for detecting and decoding SVD–SCMA codewords. The simulation results show an improvement in the bit error rate (BER) compared to that for methods such as low-density signatures (LDS), SCMA, and multidimensional SCMA (MD-SCMA).

Color-Domain SCMA NOMA for Visible Light Communication

Visible light communications (VLC) has emerged as a promising technology for the next-generation communication systems. For VLC, several non-orthogonal multiple access (NOMA) techniques have emerged, among which sparse-code multiple access (SCMA) is particularly viable due to promised shaping and coding gain, and typically minimal/no error propagation as compared to power-domain NOMA (PD-NOMA). In this work, we introduce using polychromatic light emitting diodes (LEDs) as transmitters, where several users are overlapped on each color using SCMA; consequently, the SCMA codewords on each color are overlapped in the color-domain before transmission. At the receiver, after color-filtering, the residual color-coupling is removed by inverting the coupling matrix. Finally, the users’ signals over each color-channel are recovered using the message passing algorithm. Further, asymptotic error-rate expressions are derived for the proposed color-domain SCMA. These expressions are validated by simulations assuming VLC channel-models for random waypoint user-mobility. The analytical results promise a potential gain in the sum-rate while supporting a larger number of users as compared to a monochromatic white-LED based SCMA system.

A novel deep neural network based approach for sparse code multiple access

Sparse code multiple access (SCMA) has been one of the non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G communication networks. Conventional SCMA approaches are confronting 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 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). 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.

Supervised learning applied to the decoding of SCMA codewords

This work puts together two technologies that are in the interest of the scientific community, on the one hand, access methods for fifth generation systems of mobile communications, in this case Sparse Code Multiple Access (SCMA), and on the other hand supervised learning based on neural networks. SCMA is one of the proposed access techniques for fifth generation mobile communication systems. Until now, the detection algorithm in the receiver is based on Message Passing Algorithm (MPA) or minimum Euclidean distance. In this work, a new approach is proposed, which is based on supervised learning using neural networks to decode SCMA codewords. The simulation results show that the receiver based on neural networks learns quickly and obtains 100% accuracy in predictions on channels with high noise. In addition to being simpler in its implementation than its predecessors (MPA and minimum Euclidean distance).

Wavelet‐based cognitive SCMA system for mmWave 5G communication networks

Fifth generation (5G) communication networks can achieve high spectral efficiency using sparse code multiple access (SCMA) scheme when large number of users are trying to transmit their data simultaneously. The sparsity of SCMA codewords offers the possibility of applying a low-complexity message passing algorithm as an alternative to maximum likelihood detector. However, the requirement of densely deployed 5G users is to opportunistically explore new frequencies via cognitive features to overcome spectrum scarcity challenges. In this study, spectrum sensing enables cognitive radio capabilities for the SCMA system applied in millimetre wave (mmWave) 5G communications. Proposed cognitive SCMA system can sense the spectrum holes and adapt the transmission in order to utilise the available subcarriers. Besides, wavelet packet transform based techniques are used instead of conventional Fourier-based spectrum sensing (FSS) and orthogonal frequency-division multiple access (OFDMA). Wavelet packet spectrum sensing offers more accurate estimation of frequency and power compared with FSS. On the other hand, wavelet packet multiple access is more flexible and robust against interference compared with OFDMA. The simulation results verify that the proposed method can significantly improve the performance of SCMA system in terms of probabilities of false alarm and detection, and symbol error rate.

Low Complexity Iterative Receiver Design for Sparse Code Multiple Access

Sparse code multiple access (SCMA) is one of the most promising methods among all the non-orthogonal multiple access techniques in the future 5G communication. Compared with some other non-orthogonal multiple access techniques such as low density signature (LDS), SCMA can achieve better performance due to the shaping gain of the SCMA codewords. However, despite of the sparsity of the codewords, the decoding complexity of the current message passing algorithm (MPA) utilized by SCMA is still prohibitively high. In this paper, by exploring the lattice structure of SCMA codewords, we propose a low complexity decoding algorithm based on list sphere decoding (LSD). The LSD avoids the exhaustive search for all possible hypotheses and only considers signal within a hypersphere. As LSD can be viewed a depth-first tree search algorithm, we further propose several methods to prune the redundancy visited nodes in order to reduce the size of the search tree. Simulation results show that the proposed algorithm can reduce the decoding complexity substantially while the performance loss compared with the existing algorithm is negligible.

Joint Sparse Graph-Detector Design for Downlink MIMO-SCMA Systems

The application of multiple-input multiple-output (MIMO) technique to sparse code multiple access (SCMA) systems is important to enhance the system performance of future fifth generation wireless networks. However, one of the challenges in design of downlink MIMO-SCMA systems is developing near-optimum bit error rate (BER) performance detectors with affordable computational costs. To achieve this goal, we construct a joint sparse graph combining the single graph of MIMO channels and SCMA codewords, and then design the corresponding virtual SCMA codebooks for the detector. To utilize them, a novel joint processing approach based on message passing algorithm is proposed. Furthermore, two strategies for message update on the joint sparse graph, i.e., parallel and serial schedules, are, respectively, presented. Simulations show that, the proposed detector can achieve the optimal BER performance with significantly reduced complexity, compared to the maximum likelihood detector.

Prototype for 5G new air interface technology SCMA and performance evaluation

Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme proposed to meet the challenging demand of the future 5G communications, especially in support of the massive connections. The coded bits from each data stream will be directly mapped as multi-dimensional SCMA codeword in complex domain and then spread onto the physical resource elements in a sparse manner. The number of codewords that can be non-orthogonally multiplexed in one SCMA block can be made much larger than the number of orthogonal resource elements therein, resulting in an overloaded system. The sparsity in the spreading pattern and the design in the multidimensional modulator jointly ensure the SCMA codewords can be robustly decoded with low complexity. In this paper, we focus on the low complexity receiver design and verified the superior of an SCMA system via simulations and real-time prototyping. Lab tests and field tests all show that SCMA is a promising candidate for 5G non-orthogonal multiple access which can provide up to 300% overloading that triples the whole system throughput while still enjoying the link performance close to orthogonal transmissions.