Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN’s universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

Tactile Internet of Things (IoT) requires ultra-responsive and ultra-reliable connections for massive IoT devices. As a promising enabler of Tactile IoT, grant-free non-orthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and non-orthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multi-loss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in Tactile IoT. The significant reliability gain of our scheme is validated by simulations.

In this letter, we exploit constellation rotation to enhance the performance of uplink non-orthogonal multiple access (NOMA) with a successive interference cancellation receiver. We characterize the received signal with a Gaussian mixture model (GMM), where optimal rotation angle is obtained by maximizing the entropy of GMM. Then, we efficiently solve the entropy maximization problem by deriving an optimal closed-form approximate problem via variational approximation. Performance analysis and simulation results show that the proposed scheme achieves larger capacity and lower bit error rate compared with conventional NOMA. The performance gain is more significant with fewer receiving antennas or with smaller channel gain difference between multiplexed users.

Artificial intelligence (AI) has become a promising solution for meeting the stringent performance requirements on wireless physical layer in sixth-generation (6G) communication systems, due to its strong ability to learn complex model, achieve end-to-end optimization and adapt to dynamic environments. This article provides a comprehensive review with respect to artificial intelligence for wireless physical-layer technologies (AI4PHY). Specifically, we first analyze the characteristics of the classic AI techniques and their potential applications for physical-layer technologies. Then we study the AI-enhanced designs from the point of view of the basic physical-layer modules, including coding, modulation, multiple access, multiple-input-multiple-output (MIMO), channel estimation, as well as relay transmission. The standardization progress of AI4PHY in 3GPP is also discussed. Based on the current AI4PHY researches, we propose some potential future research directions to inspire and encourage the further exploration.

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

In order to meet the tremendous requirements on the convergence between broadband and broadcast, this paper proposes to incorporate layered division multiplexing (LDM), an important variant of non-orthogonal multiple access (NOMA) technologies, into Millimeter wave (mmWave) multiple-input multiple-output (MIMO) system with device-to-device (D2D) communications to simultaneously provide broadcast and unicast services. The performance of the proposed full-duplex D2D-aided mmWave MIMO-NOMA with randomly deployed users is then analyzed under practical assumptions. Closed-form expressions of both the outage probability and the ergodic capacity are then derived by using the Laplace transform technique. Finally, detailed numerical results reveal the performance gains of the proposed scheme compared with the LDM without D2D communications and conventional time division multiplexing based scheme. The impacts of the antenna array gain and the transmission power of both the base station and the full-duplex link on system performance are also presented. Besides, we observe that it is vital to optimal the transmission power of the full-duplex link which ensures less self-interference to enhance the system capacity.