Application of NOMA in 6G Networks: Future Vision and Research Opportunities for Next Generation Multiple Access

As a prominent member of the next generation multiple access (NGMA) family, non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access candidate for the sixth-generation (6G) networks. This article focuses on applying NOMA in 6G networks, with an emphasis on proposing the so-called "One Basic Principle plus Four New" concept. Starting with the basic NOMA principle, the importance of successive interference cancellation (SIC) becomes evident. In particular, the advantages and drawbacks of both the channel state information based SIC and quality-of-service based SIC are discussed. Then, the application of NOMA to meet the new 6G performance requirements, especially for massive connectivity, is explored. Furthermore, the integration of NOMA with new physical layer techniques is considered, followed by introducing new application scenarios for NOMA towards 6G. Finally, the application of machine learning in NOMA networks is investigated, ushering in the machine learning empowered NGMA era.Comment: 14 pages, 5 figures, 1 tabl

非直交多元接続のための高信頼空間変調

　マルチユーザ空間変調（SM: Spatial Modulation）では，SM信号の疎性を用いた圧縮センシングによるマルチユーザ検出が研究されているものの，受信機においてチャネル情報が完全に既知であるという条件の下で議論されている．実際には受信機側でチャネル情報を推定し，推定したチャネル情報を用いて復調処理を行う．推定したチャネル情報の精度は復調の精度に影響を及ぼすため，チャネル推定は重要なものであり考慮しなければならない．　そこで本研究ではチャネル推定を，ブロックスパース性を有する信号の再構成問題として扱い，ブロックスパース性を考慮した複素数近似メッセージ伝播法（BS-CAMP: Block-Sparse Complex Approximate Message Passing）によって信号の再構成を行う方法を提案する．BS-CAMPは受信機が送信信号に含まれる非零要素の個数を事前に知る必要がない再構成アルゴリズムとなっており，ランダムアクセス方式にも適用可能である．計算機シミュレーションより，BS-CAMPによるチャネル推定の精度やスループット特性への影響を示す．　さらに，高信頼な通信を実現するにはチャネル推定だけでなく誤り訂正符号が重要となる．そこで併せて本研究ではSMに誤り訂正符号化を組み合わせたものの一つである，ターボトレリス符号化空間変調（SM-TTC: SM with Turbo Trellis-Coding）における符号の最適化及び性能解析を行った．具体的には，シンボルベースEXIT（Extrinsic Information Transfer）チャートを用いた低演算符号探索法によって，演算量を低減しながら最良の特性を示す符号を探索する．計算機シミュレーションより，探索した符号を用いたSM-TTCが従来のものよりも優れていること，および提案手法が従来の符号探索法よりも低計算量で符号探索が可能であることを示す．電気通信大学201

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

Two-Layered Superposition of Broadcast/Multicast and Unicast Signals in Multiuser OFDMA Systems

We study optimal delivery strategies of one common and $K$ independent messages from a source to multiple users in wireless environments. In particular, two-layered superposition of broadcast/multicast and unicast signals is considered in a downlink multiuser OFDMA system. In the literature and industry, the two-layer superposition is often considered as a pragmatic approach to make a compromise between the simple but suboptimal orthogonal multiplexing (OM) and the optimal but complex fully-layered non-orthogonal multiplexing. In this work, we show that only two-layers are necessary to achieve the maximum sum-rate when the common message has higher priority than the $K$ individual unicast messages, and OM cannot be sum-rate optimal in general. We develop an algorithm that finds the optimal power allocation over the two-layers and across the OFDMA radio resources in static channels and a class of fading channels. Two main use-cases are considered: i) Multicast and unicast multiplexing when $K$ users with uplink capabilities request both common and independent messages, and ii) broadcast and unicast multiplexing when the common message targets receive-only devices and $K$ users with uplink capabilities additionally request independent messages. Finally, we develop a transceiver design for broadcast/multicast and unicast superposition transmission based on LTE-A-Pro physical layer and show with numerical evaluations in mobile environments with multipath propagation that the capacity improvements can be translated into significant practical performance gains compared to the orthogonal schemes in the 3GPP specifications. We also analyze the impact of real channel estimation and show that significant gains in terms of spectral efficiency or coverage area are still available even with estimation errors and imperfect interference cancellation for the two-layered superposition system

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&