DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning

The millimeter-wave (mmWave) large-scale antenna arrays (LSAAs) systems play a vital role in increasing the beamforming (BF) gain and acquiring highly directional propagation. Recently, non-orthogonal multiple access (NOMA) has been integrated into these systems to manage massive connectivity and achieve spectral-efficient communications. This paper focuses on angle-domain (AD) hybrid beamforming (BF) for mmWave LSAAs and NOMA systems, thanks to the low complexity, power consumption, and channel estimation overhead. However, with limited radio-frequency chains, the hybrid BF-based single-beam (SB)-NOMA scheme generating a single beam to serve the NOMA users fails to exploit the multi-user diversity due to narrow beams with LSAAs. To tackle this limitation, we design schemes offering additional degrees of freedom. More importantly, they require only the knowledge of angular information and are suitable for either linear or rectangular antenna arrays, unlike those proposed in the literature. The first scheme exploits the time-domain resources to schedule groups having high spatial interference within distinct time slots. To minimize the need for fast and precise synchronization when applying time division multiple access (TDMA) with mmWave NOMA, we leverage the multi-beam (MB)-NOMA framework. And we propose a joint SB- and MB-NOMA scheme to benefit from NOMA multi-user diversity, whatever the cell load and the users’ positions. Using the New York University channel simulator (NYUSIM), we further validate the performance of the proposed schemes compared to the solution proposed in the literature and others using fully digital BF. Specifically, the proposed TDMA-based scheme achieves a sum-rate gain of up to 83% over the TDMA-based one existing in the literature. Moreover, we verify the superiority of applying both SB- and MB-NOMA instead of only MB-NOMA.

Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance far from the limit. The recent breakthrough of deep learning and its positive applications to wireless communications have paved the way to tackle these challenges. This article studies the effectiveness and efficiency of deep learning in enhancing NOMA performance. Specifically, we first present the deep neural network (DNN), which is constructed via a uniform signal processing architecture, and use it as the unified multiuser receiver in both data and model-driven approaches. This enables the end-to-end optimization of NOMA transceivers due to the universal function approximation property of DNN. On the other hand, with DNN we can automatically extract the user access behaviors out of the time-series signals and optimize the transceivers to match these cross-lay-er behaviors. We further analyze the integration of non-orthogonal communication and neural computation to accomplish high-efficiency data transmission at low cost. Finally, we identify some essential future directions of deep-learning-enhanced NOMA from the perspectives of online reconfigurability and adaptability toward the ever changing environment in future mMTC.

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this article, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the explainable DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

Covert communication and physical layer security (PLS) have emerged as two essential pillars for safeguarding wireless networks against increasingly sophisticated eavesdropping and surveillance threats. This paper investigates a downlink hybrid power–frequency multiple access (HPMA) system aided by a friendly jammer, where a single source communicates with two legitimate users in the presence of a passive eavesdropper. By jointly exploiting the power and frequency domains, the proposed HPMA scheme achieves clear advantages in terms of covertness and secrecy performance compared to conventional orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. For performance analysis, we derive closed-form and approximated expressions for key metrics, including detection error probability (DEP), secrecy outage probability (SOP), effective secrecy throughput (EST), and average secrecy capacity (ASC), which reveal important trade-offs among resource allocation, jammer power, and secrecy reliability. To further reduce the computational burden of conventional analysis, a regression-based deep neural network (DNN) framework is developed to efficiently predict the secrecy performance of the considered system. Extensive Monte Carlo simulations validate the analytical results and confirm the superiority of the proposed HPMA scheme with friendly jamming. These findings provide useful insights for the design of future secure and covert wireless systems with low-complexity and intelligent physical-layer security requirements.

Non-Orthogonal Multiple Access (NOMA) is known as one of the promising techniques in the fifth generation of wireless communication. The classification of users in (NOMA) is based on power, while it is based on time, frequency and code in Orthogonal Multiple Access (OMA). In this study the downlink NOMA includes two users, one strong and one weak. The comparison of NOMA and OMA properties shows a range of Power Allocation Factors (PAF) for the two users within which NOMA properties have proved to be better than OMA. It was also shown that the achieved PAF is a function of channel gains of two users. Then, the coverage region is defined for multiple access channel; and by considering the Base Station (BS) as a fixed one in the center of the cell, the coverage region for both NOMA and OMA is calculated. The theoretical and analytical results show that the achieved coverage region of NOMA for both weak and strong users has been increased compared to OMA.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth generation (5G) wireless networks. In this paper, we develop a general performance analysis framework for mmWave-NOMA networks with spatially random users taking into account link blockage and directional beamforming. To facilitate NOMA transmission in mmWave networks, we propose an angle-based user pairing strategy. Specifically, the base station first randomly selects one user and then pairs it with the line-of-sight user that has the minimum relative angle difference. NOMA is enabled when the beamwidth of the main lobe created by directional beamforming is not smaller than the angle difference between the paired NOMA users. Tools from stochastic geometry are utilized to derive the coverage probability and the sum rate of the proposed NOMA scheme, where beam misalignment at both the base station and the users is taken into account. Simulations validate the performance analysis and show that the proposed NOMA scheme achieves a larger coverage probability and a higher sum rate than conventional NOMA with distance-based user pairing and orthogonal multiple access.

Cell-Free massive MIMO (cf-mMIMO) is an incarnation of distributed massive MIMO, which provides high spectral efficieny (SE). However, when the number of users increases, the SE of cf-mMIMO will decrease. In order to increase the SE when the number of users is large, many researchers investigated cf-mMIMO with non-orthogonal multiple access (NOMA). In the existing research, cf-mMIMO with NOMA requires the number of users in each cluster to be equal, which is inflexible. This paper investigates the multiple access technology for cf-mMIMO. A more flexible scheme which uses the orthogonal multiple access (OMA) and NOMA at the same time is proposed in this paper. This scheme does not require the number of users in each cluster to be equal and it combines the advantages of both NOMA and OMA. An achievable SE of this scheme is derived. It is proved that under certain number of users and the length of the channel’s coherence time conditions, using OMA and NOMA at the same time in the cf-mMIMO is able to obtain higher SE than using OMA or NOMA alone.KeywordsCell free massive MIMONOMAOMASuccessive interference cancellation

The non-orthogonal multiple access (NOMA) is one of the fledging paradigms which next generation radio access technologies are sprouting toward. The NOMA with superposition coding (SC) in the transmitter and successive interference cancellation (SIC) at the receiver comes with many desirable features and benefits over orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) adopted by Long-Term Evolution (LTE). In this paper, we study the recent research trends on NOMA in 5G systems. We discuss the basic concept of NOMA and explain its aspects of importance for future radio access. Then, we provide a survey of the state of the art in NOMA for 5G systems in a categorized manner. Further, we analyze the NOMA performances with numerical examples; and provide some avenues for future research on NOMA on a set of open issues and challenges.

The emerging non-orthogonal multiple access (NOMA), which enables mobile users (MUs) to share same frequency channel simultaneously, has been considered as a spectrum-efficient multiple access scheme to accommodate tremendous traffic growth in future cellular networks. In this paper, we investigate the NOMA downlink relay-transmission, in which the macro base station (BS) first uses NOMA to transmit to a group of relays, and all relays then use NOMA to transmit their respectively received data to an MU. In specific, we propose an optimal power allocation problem for the BS and relays to maximize the overall throughput delivered to the MU. Despite the non-convexity of the problem, we adopt the vertical decomposition and propose a layered-algorithm to efficiently compute the optimal power allocation solution. Numerical results show that the proposed NOMA relay-transmission can increase the throughput up to 30 percent compared with the conventional time division multiple access (TDMA) scheme, and we find that increasing the relays' power capacity can increase the throughput gain of the NOMA relay against the TDMA relay. Furthermore, to improve the throughput under weak channel power gains, we propose a hybrid NOMA (HB-NOMA) relay that adaptively exploits the benefit of NOMA relay and that of the interference-free TDMA relay. By using the throughput provided by the HB-NOMA relay for each individual MU, we study the multi-MUs scenario and investigate the multi-MUs scheduling problem over a long-term period to maximize the overall utility of all MUs. Numerical results demonstrate the performance advantage of the proposed multi-MUs scheduling that adopts the HB-NOMA relay-transmission.

This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications toward next generation multiple access (NGMA). First, the limitations of current scenario-specific multiple-antenna NOMA schemes are discussed, and the importance of AI for NGMA is highlighted. Then, to achieve the vision of NGMA, a novel cluster-free NOMA framework is proposed for providing scenario-adaptive NOMA communications, and several promising machine learning solutions are identified. To elaborate further, novel centralized and distributed machine learning paradigms are conceived for efficiently employing the proposed cluster-free NOMA framework in single-cell and multi-cell networks, where numerical results are provided to demonstrate the effectiveness. Furthermore, the interplays between the proposed cluster-free NOMA and emerging wireless techniques are presented. Finally, several open research issues of AI enabled NGMA are discussed.

Recently, non-orthogonal multiple access (NOMA) has been proposed to improve spectral efficiency greatly over conventional orthogonal multiple access (OMA). However, it is still challenging to implement NOMA into transmission of video. In this research, we investigate video broadcasting design over NOMA systems. When video transmission is considered, quality of experience (QoE) is a better metric for NOMA design than throughput. Therefore, we design a QoE-driven scalable video broadcasting (SVB) scheme over NOMA systems. To achieve scalable transmission performance, power allocation is optimized among multiple video layers, which are requested by users with different channel conditions. Specifically, we formulate the power allocation problem to maxmize the system overall QoE while guaranteeing the basic service of users. Although this problem is non-convex and discontinuous, an optimal algorithm is proposed based on the hidden monotonicity of the problem. Simulation results show that the proposed SVB scheme over NOMA systems outperforms existing OMA and NOMA based schemes in various broadcast scenarios in terms of QoE.

The fifth generation (5G) of wireless communication accompanied by the advancement of mobile internet and the Internet of Things (IoT) will create stormy data traffic. The 5G challenges like higher spectral efficiency, massive connectivity, and low latency are addressed by the Non-Orthogonal Multiple Access (NOMA) in recent years. NOMA is proven to be a promising technique from its capability to accommodate a larger number of users in contrast to the conventional orthogonal multiple access (OMA) schemes. NOMA comprises two distinct techniques such as Power-Domain NOMA and Code-Domain NOMA. The non-orthogonal resource allocation in Power-Domain NOMA is practically realized by the Superposition Coding (SC) at the Base Station (BS) and an advanced Successive Interference Cancellation at the User Equipment (UE). In this manuscript, the basic concepts of NOMA, on comparing its channel gain with OMA are discussed first. Specifically, a comprehensive survey of various SIC techniques applied under perfect and imperfect Channel State Information (CSI) uncertainties are summarized in terms of outage probability, ergodic capacity, and system performance. In addition, the sophisticated SIC techniques, when NOMA is integrated with modern wireless communication technologies such as multiple-input multiple-output (MIMO-NOMA) and Cooperative (C-NOMA) communications are investigated. Furthermore, the challenges to be solved with their corresponding opportunities and future research directions of NOMA are explored.

Present research paper proposes about the creating innovation within the way of 5G Wi-Fi networksmostly the non-orthogonal multiple access (NOMA) and cognitive radio (CR) thatgives a prominent green usage of Wi-Fi range in upcoming future. In this newsletter, we look at the mixture of NOMA with CR right that gives a comprehensive gadget, especially psychological NOMA people group, for presenting a smart range sharing. Format mind of psychological NOMA networks are completely adjusted to capacity prerequisites of 5G WI-Fi networks, related to extreme range in general execution, enormous availability, low idleness, and higher value. Three exact cognitive NOMA structures are given, along with underlay NOMA organizations, overlay NOMA organizations, and CR-enlivened NOMA organizations. To address inter and intra-network impedance which in gigantic viewpoint corrupt the general regular by and large execution of intellectual NOMA organizations, agreeable transferring systems are proposed. For each psychological NOMA structure, our proposed agreeable transferring approach shows its capacity to observably diminish blackout openings. Besides, we talk open irritating circumstances and predetermination research orders on usage of psychological NOMA organizations.

Non-orthogonal multiple access (NOMA) provides higher spectral efficiency and access to more users than orthogonal multiple access. However, the issue of resource allocation in NOMA is dynamic and produces a high computation burden when using traditional methods. In this paper, a symmetry-aware double deep Q network (DDQN) algorithm in deep reinforcement learning is employed to allocate power to users in NOMA while guaranteeing quality of service for the weakest users. The research process is divided into two parts. Firstly, users in the communication system are grouped using a method that synergistically considers gain difference and similarity, exploiting symmetrical properties within the user groups. Secondly, the DDQN algorithm is used to allocate power to multiple users in a NOMA system, which utilizes the inherent symmetry in the signal-to-interference noise ratio of each user as an objective function. By recognizing and leveraging these symmetrical patterns, the algorithm can dynamically adjust the power allocation to optimize system performance. Finally, the proposed algorithm is compared with conventional NOMA power allocation algorithms and shows significant improvements in system performance. The results of the convergence function show that the algorithm proposed in this paper can converge in approximately 1800 iterations, which effectively solves the problem of large arithmetic and complex processes existing in the traditional method.

A low-complexity ADMM-based secure beamforming for IRS-assisted uplink cognitive radio networks with NOMA