Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed