Code-domain Non-orthogonal Multiple Access Research Articles

Simulation and Analysis of the Code Domain NOMA with UFMC for 5G Wireless Networks

In fifth generation (5G) wireless networks, radio access techniques and multi-carrier waveforms play a vital role in meeting the diversified demands of ultra-low latency, massive connectivity, and higher throughput. Multi-access schemes used conventionally in 4G system was Orthogonal Multiple Access (OMA) technique. The OMA techniques suffer from inefficient spectrum utilization, high latency, and supports a limited number of users. Next-generation networks, Non-Orthogonal Multiple Access (NOMA), has a great potential, in which multiple users are simultaneously served using the same time, frequency, or code resource increasing the throughput. Code domain-NOMA (CD-NOMA) is the key technique implemented in the system design where multiple users are distinguished based on unique user-specific spreading codes. The NOMA system could significantly benefit from Universal Filtered Multi-Carrier (UFMC) modulation waveform in terms of flexibility, spectral efficiency, compatibility with Multiple Input Multiple Output (MIMO) technique, and relaxed synchronization requirements. The novel integrated system proposed in the paper is CD-NOMA-UFMC with convolutional codes. The major outcome of the paper is that the combination of UFMC air interface modulation technique with CD-NOMA access method can be the most effective way to meet the growing demands of 5G.

Clustering Approach for Reliable Wireless Communication

Multiple access techniques for 5G systems are based on principles related to security, efficiency, and performance increase. Multi-user shared access (MUSA) is included in code-domain non-orthogonal multiple access (CD-NOMA) techniques with multiple benefits including multiple-access interference minimization and system capacity increase. The additional benefits of MUSA include its grant-free nature, which makes it applicable to scenarios involving random access channels, minimal latency communications, etc. Depending on the high-frequency diversity of the fading-affected channels, this technique is also used to lessen their effects. This paper illustrates the concept of code correlation used in MUSA by proposing a method to allocate MUSA codes to active users in an uplink data transmission system based on clusters. This clustering approach (grouping of 3GPP TR 38.812 spreading codewords) enables performance improvement in different system configurations by implementing various scenarios and providing reliable results. It is demonstrated that four-length spreading code selection from 3GPP family codes is a satisfactory solution, and this cluster grouping can be designed in a less overloaded multi-user system. The challenges, advantages, and limitations of the proposed method are outlined throughout the study.

NON-ORTHOGONAL MULTIPLE ACCESS SCHEMES SURVEY: AN OVERVIEW OF NON-ORTHOGONAL MULTIPLE ACCESS TECHNIQUES

Non-Orthogonal Multiple Access (NOMA) is a promising technology that can significantly improve the performance of wireless communication systems, especially in applications with a high number of users and limited spectral resources. NOMA enables simultaneous transmission and reception of multiple signals at the same time and frequency resources. NOMA can be implemented using different methods, including Power Domain NOMA (PD-NOMA) and Code Domain NOMA (CD-NOMA). The implementation of NOMA requires advanced signal processing techniques and can be challenging in some scenarios. However, with the advancement of technology, NOMA is becoming more practical and is expected to play a significant role in the future of wireless communication systems. This paper will discuss what NOMA and NOMA schemes are. These schemes will be compared in terms of their benefits and drawbacks, and it will be explained which one performs better.

SCMA-Enabled Multi-Cell Edge Computing Networks: Design and Optimization

Multi-access edge computing (MEC) is regarded as a promising approach for providing resource-constrained mobile devices with computing resources through task offloading. Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) scheme that can meet the demands of multi-cell MEC networks for high data transmission rates and massive connections. In this paper, we propose an optimization framework for SCMA-enabled multi-cell MEC networks. The joint resource allocation and computation offloading problem is formulated to minimize the system cost, which is defined as the weighted energy cost and latency. Due to the nonconvexity of the proposed optimization problem induced by the coupled optimization variables, we first propose an algorithm based on the block coordinate descent (BCD) method to iteratively optimize the transmit power and edge computing resources allocation by deriving closed-form solutions, and further develop an improved low-complexity simulated annealing (SA) algorithm to solve the computation offloading and multi-cell SCMA codebook allocation problem. To solve the problem of partial state observation and timely decision-making in long-term optimization environment, we put forward a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and distributed execution. Furthermore, we extend the framework to the partial offloading case and propose an algorithm based on alternating convex search for solving the task offloading ratio. Numerical results show that the proposed multi-cell SCMA-MEC scheme achieves lower energy consumption and system latency in comparison to the orthogonal frequency division multiple access (OFDMA) and power-domain (PD) NOMA techniques.

Drone-Aided Networking with Massive Connectivity and High Spectral Efficiency Enabled

Drone-aided networking is considered a potential candidate for internet of things (IoT) networking in 5G and beyond, where drones are deployed to serve a large number of devices simultaneously for data collection, surveillance, remote sensing, etc. However, challenges arise due to massive connectivity requests as well as limited power budgets. To this end, this paper focuses on the design of drone-aided IoT networking, where a drone access point serves a large number of devices for efficient data transmission, collection, and remote sensing. Constant envelope signaling such as minimum shift keying (MSK) family is considered to avoid potential significant power inefficiency due to nonlinear distortion. To this end, code-domain non-orthogonal multiple access (NOMA) is developed and analyzed in terms of achievable sum spectral efficiency. Further, power allocation is derived based on the aforementioned analysis and is demonstrated to offer significantly improved performance in terms of sum spectral efficiency and user load. Simulation results confirm the feasibility of the proposed design and shows that the designed system can attain the promised performance using either simple convolutional code or complex polar code. The proposed system can be used in scenarios such as deep space communications, where MSK family signaling is adopted as well.

An Enhanced Probabilistic-Shaped SCMA NOMA for Wireless Networks

The future digital evolution poses challenges that need to be spectral and energy-efficient, as well as highly reliable and resilient. The non-orthogonal multiple access (NOMA) accomplishes massive connectivity, spectral efficiency, effective bandwidth utilization, and low latency. The proposed work involves the code domain NOMA scheme called Sparse Code Multiple Access (SCMA) which provides shaping gain through multi-dimensional constellation and the best performance in terms of bit error rate (BER). It achieves overloading of users through the non-orthogonal allocation of resources which enhances the spectral efficiency and serves more users. The shaping gain can be further improved by reducing the BER and enhancing the capacity of the channel through constellation shaping. This work employs a probabilistic-shaped (PS) constellation where each symbol is transmitted with different probabilities which achieves a reduction of average symbol power and forward error correction (FEC) through channel coding using polar codes which aid in energy efficiency. The output is two-dimensionally spread over Orthogonal Frequency Code Division Multiplexing (OFCDM) subcarriers to achieve a flexible transmission rate through a variable spreading factor. Computer simulations showed better BER performance under AWGN and Rayleigh channels with remarkable gain in SNR which paves the way for future applications in Fifth Generation (5G) beyond networks.

A Residual Learning-Aided Convolutional Autoencoder for SCMA

Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) technology proposed to meet the access needs of large-scale intelligent terminal devices with high spectrum utilization. To improve the accuracy and computational complexity of SCMA to accommodate the internet of things (IoT) scenario, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs) and residual networks. A residual network with multitask learning improves the decoding accuracy, and CNN units are used for SCMA codeword mapping, with sparse connectivity and weight-sharing to reduce the number of trainable parameters. Simulations show that this scheme outperforms existing autoencoder schemes in bit error rate (BER) and computational complexity.

A Knowledge-Based Deep Learning Detection Scheme for Downlink SCMA Systems

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.

A Flexible Encoding/Decoding Procedure for 6G SCMA Wireless Networks via Adversarial Machine Learning Techniques

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.

On the Convex Hull of the Achievable Capacity Region of the Two User FDM OMA Downlink

In multiple access channel systems, such as a mobile communication network, it is important to determine how to share the available resources (for example bandwidth and power) among the users. In recent years, one of the promising scheme is Non-Orthogonal Multiple Access (NOMA), where, unlike the traditional Orthogonal Multiple Access, OMA solution, signals for the different users overlap in some domain (power-domain NOMA, code-domain NOMA, etc). In order to evaluate the performance of any NOMA scheme, we need to compare the achievable bit rates of the users (the capacity region) to an OMA case with comparable parameters (for example, same total bandwidth, same total power and same channel conditions, etc). To make this comparison, we first need to know the capacity region for the OMA cases. Many papers make such comparison without detailing the derivation of the capacity region of the OMA case they compare to [1], [2], [3], [4]. In some cases, we have only one free parameter to choose (for example in uplink frequency division multiplexing systems for two users, it is the bandwidth ratio between the users), and the achievable capacity can be directly calculated for both users depending on the single parameter (hence the boundary of the capacity region is trivial). In other cases, such as downlink frequency division multiplexing systems, even for only two users, we have to allocate optimally two resources between the users: the bandwidth and the base station’s available power. Hence, it is far from being trivial to determine which combination is better and where the boundary of the capacity region is. In this paper, we provide a derivation for that case.

Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting With D2D Communications

The cellular network coexisting with device-to-device (D2D) communications has been studied extensively. Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for the evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is considered suitable for next-generation wireless network in code-domain NOMA. In this paper, we consider the RIS-aided uplink SCMA cellular network simultaneously with D2D users. We formulate the optimization problem which aims to maximize the cellular sum-rate by jointly designing D2D users’ resource block (RB) association, the transmitted power for both cellular users and D2D users, and the phase shifts at the RIS. The power limitation and users’ communication requirements are considered. The problem is non-convex, and it is challenging to solve it directly. To handle this optimization problem, we propose an efficient iterative algorithm based on block coordinate descent (BCD) method. The original problem is decoupled into three subproblems to solve separately. Simulation results demonstrate that the proposed scheme can significantly improve the sum-rate performance over various schemes.

On Near-Optimal Codebook and Receiver Designs for MIMO-SCMA Schemes

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) technique that supports high-level connectivity. Through the combination of SCMA and multiple-input multiple-output (MIMO) techniques, MIMO-SCMA schemes are capable of enhancing the overall spectral efficiency. This paper proposes novel MIMO-SCMA designs for both uplink and downlink Rayleigh fading channels. Rather than directly combining the SCMA and spatial multiplexing techniques presented in the previous literature, the proposed designs consider not only the frequency or time diversity, but also simultaneously include space diversity. Codebook design criteria for different transmission scenarios are respectively investigated, and a low-complexity codebook design algorithm is proposed based on the cross-entropy method. In addition, since the new designs suggest a dense factor graph, conventional message-passing algorithm (MPA)-based detectors are less suitable. Consequently, low-complexity MIMO-SCMA codeword detectors based on depth-first and breadth-first tree-search algorithms are respectively investigated. It is shown that the overall designs are able to simultaneously achieve a higher diversity order, enhanced BER results, and lower levels of detection complexity for both uplink and downlink scenarios, when compared to conventional MIMO-SCMA schemes.

Exploiting hybrid non‐orthogonal multiple access in satellite–terrestrial systems with randomly deployed users

In this letter, a hybrid non-orthogonal multiple access (NOMA) scheme for satellite–terrestrial systems is proposed, which is capable of integrating both power domain NOMA (PD-NOMA) and code domain NOMA (CD-NOMA) design modes. The stochastic geometry method is considered to model the position of the terrestrial NOMA user. Specifically, the closed-form expressions of exact outage probability with imperfect interference cancellation are derived. In order to get more insights, the asymptotic outage probabilities for high SNR conditions are also discussed. Numerical results are provided to confirm the validity of theoretical analysis and reveal the effects of critical parameters on system performance.

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

This letter presents an enhanced design of multi-dimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems.

Asynchronous Multi-User Detection for Code-Domain NOMA: Expectation Propagation Over 3D Factor-Graph

Code-domain non-orthogonal multiple access (NOMA) is a promising technology to achieve ubiquitous massive connections for 5G and beyond. In application scenarios with high channel dynamics or simplified scheduling procedures, multi-user signals face the difficulty of accurate synchronization. The resulted asynchronous inter-user interference (IUI) is too complex to be modeled or alleviated by conventional multi-user detection algorithms. To effectively characterize the asynchronous IUI for code-domain NOMA, this paper constructs a three-dimensional (3D) factor-graph by introducing time delay as an additional dimension and develops the corresponding message passing mechanisms. Then, a novel 3D-expectation propagation algorithm (3D-EPA) is proposed for low-complexity asynchronous multi-user detection. The proposed 3D-EPA iterates between per-user estimation, which projects the asynchronous interference via Gaussian approximation, and inter-user message passing, which propagates the previous estimates among the users to refine the approximation. We also extend the 3D-EPA to multi-antenna scenarios and analyze its state evolution. Simulation results show that the proposed asynchronous NOMA system with 3D-EPA even outperforms its synchronous counterpart, especially under high overloadings.

On the Performance of the Unified NOMA Framework for Satellite-Terrestrial Networks with Hardware Impairments

A unified nonorthogonal multiple access (NOMA) framework for satellite-terrestrial network is investigated over Shadowed-Rician fading channels, which can be applied to both power domain NOMA (PD-NOMA) and code domain NOMA (CD-NOMA). Under the realistic assumption of hardware impairments (HIs) and imperfect successive interference cancellation (SIC), the achievable outage probability of the paired users is analyzed first, and the closed-form expressions of exact and asymptotic outage probability are derived. Next, the ergodic sum rate is investigated in different levels of HIs and residual interference. In addition, the energy efficiency in the delay-tolerant transmission mode is studied to characterize the performance of the satellite-terrestrial network. Finally, the Monte Carlo simulation results demonstrate the accuracy of theoretical derivation and showcase the effects of a wide range of satellite channel parameters and the realistic assumption on outage probability, ergodic rate, and energy efficiency.

Deep Learning-Based Codebook Design for Code-Domain Non-Orthogonal Multiple Access: Approaching Single-User Bit-Error Rate Performance

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3dB over the additive white Gaussian noise channel.

A Novel Multitask Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This letter aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.

A Wasserstein GAN Autoencoder for SCMA Networks

In the view of the exponential growth of mobile applications, the Sparse Code Multiple Access (SCMA) is one promising code-domain Non-Orthogonal Multiple Access (NOMA) technique. The challenge in the adoption of SCMA in practical networks is twofold: designing optimal codebooks at the transmitter and decoding the data at the receiver. However, most of the works available in the literature address only one aspect. In this letter, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art autoencoder architecture and a novel Wasserstein Generative Adversarial Network (WGAN) that improves the robustness to the channel noise. We compare the performance obtained by the proposed network with conventional computationally intensive solutions and a deep learning based autoencoder. The performance achieved show better performances in terms of Symbol Error Rate (SER), especially at low energy per bit to noise power spectral density ratio.

Low-Density Spreading Design Based on an Algebraic Scheme for NOMA Systems

In this letter, a code-domain nonorthogonal multiple access (NOMA) technique based on an algebraic design is studied. We propose an improved low-density spreading (LDS) sequence design based on projective geometry. In terms of its bit error rate (BER) performance, our proposed improved LDS code set outperforms the existing LDS designs over the frequency-nonselective Rayleigh fading and additive white Gaussian noise (AWGN) channels. We demonstrated that achieving the best BER depends on the minimum distance.