Downlink Asynchronous Non-Orthogonal Multiple Access with Quantizer Optimization

In this letter, we study a two-user downlink asynchronous non-orthogonal multiple access (ANOMA) with limited feedback. We employ the max-min criterion for the power allocation and derive the closed-form expressions for the upper and lower bounds of the max-min rate. It is demonstrated that ANOMA can achieve the same or even higher average maxmin rate with a lower feedback rate compared with NOMA. Moreover, we propose a quantizer optimization algorithm which applies to both NOMA and ANOMA. Simulation results show that the optimized quantizer significantly improves the average max-min rate compared with the conventional uniform quantizer, especially in the scenario with a low feedback rate

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

Downlink Asynchronous Non-Orthogonal Multiple Access With Quantizer Optimization

In this letter, we study a two-user downlink asynchronous non-orthogonal multiple access (ANOMA) with limited feedback. We employ the max-min criterion for the power allocation and derive the closed-form expressions for the upper and lower bounds of the max-min rate. It is demonstrated that ANOMA can achieve the same or even higher average maxmin rate with a lower feedback rate compared with NOMA. Moreover, we propose a quantizer optimization algorithm which applies to both NOMA and ANOMA. Simulation results show that the optimized quantizer significantly improves the average max-min rate compared with the conventional uniform quantizer, especially in the scenario with a low feedback rate