158 research outputs found
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
A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond
Today's wireless networks allocate radio resources to users based on the
orthogonal multiple access (OMA) principle. However, as the number of users
increases, OMA based approaches may not meet the stringent emerging
requirements including very high spectral efficiency, very low latency, and
massive device connectivity. Nonorthogonal multiple access (NOMA) principle
emerges as a solution to improve the spectral efficiency while allowing some
degree of multiple access interference at receivers. In this tutorial style
paper, we target providing a unified model for NOMA, including uplink and
downlink transmissions, along with the extensions tomultiple inputmultiple
output and cooperative communication scenarios. Through numerical examples, we
compare the performances of OMA and NOMA networks. Implementation aspects and
open issues are also detailed.Comment: 25 pages, 10 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
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise
In this paper, we focus on securing the confidential information of massive
multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA)
networks by exploiting artificial noise (AN). An uplink training scheme is
first proposed with minimum mean squared error estimation at the base station.
Based on the estimated channel state information, the base station precodes the
confidential information and injects the AN. Following this, the ergodic
secrecy rate is derived for downlink transmission. An asymptotic secrecy
performance analysis is also carried out for a large number of transmit
antennas and high transmit power at the base station, respectively, to
highlight the effects of key parameters on the secrecy performance of the
considered system. Based on the derived ergodic secrecy rate, we propose the
joint power allocation of the uplink training phase and downlink transmission
phase to maximize the sum secrecy rates of the system. Besides, from the
perspective of security, another optimization algorithm is proposed to maximize
the energy efficiency. The results show that the combination of massive MIMO
technique and AN greatly benefits NOMA networks in term of the secrecy
performance. In addition, the effects of the uplink training phase and
clustering process on the secrecy performance are revealed. Besides, the
proposed optimization algorithms are compared with other baseline algorithms
through simulations, and their superiority is validated. Finally, it is shown
that the proposed system outperforms the conventional massive MIMO orthogonal
multiple access in terms of the secrecy performance
Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink
Non-Orthogonal Multiple Access (NOMA) is at the heart of a paradigm shift
towards non-orthogonal communication due to its potential to scale well in
massive deployments. Nevertheless, the overhead of channel estimation remains a
key challenge in such scenarios. This paper introduces a data-driven,
meta-learning-aided NOMA uplink model that minimizes the channel estimation
overhead and does not require perfect channel knowledge. Unlike conventional
deep learning successive interference cancellation (SICNet), Meta-Learning
aided SIC (meta-SICNet) is able to share experience across different devices,
facilitating learning for new incoming devices while reducing training
overhead. Our results confirm that meta-SICNet outperforms classical SIC and
conventional SICNet as it can achieve a lower symbol error rate with fewer
pilots
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