249 research outputs found
Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO
The coupling of cell-free massive MIMO (CF-mMIMO) with Mobile Edge Computing
(MEC) is investigated in this paper. A MEC-enabled CF-mMIMO architecture
implementing a distributed user-centric approach both from the radio and the
computational resource allocation perspective is proposed. An optimization
problem for the joint allocation of uplink powers and remote computational
resources is formulated, aimed at striking an optimal balance between the total
uplink power consumption and the sum SE throughout the network, under power
budget and latency constraints. In order to efficiently solve such a
challenging non-convex problem, an iterative algorithm based on sequential
convex programming is proposed, along with two approaches to priory assess the
problem feasibility. Finally, a detailed performance comparison between the
proposed MEC-enabled user-centric CF-mMIMO architecture and its network-centric
(both centralized and distributed) counterpart, is provided. Numerical results
reveal the effectiveness of the proposed joint optimization problem, under
different AP selection strategies, and the natural suitability of CF-mMIMO in
supporting computation-offloading applications with benefits over users'
transmit power and energy consumption, the offloading latency experienced, and
the total amount of allocated remote computational resources.Comment: This paper has been submitted for publication in an IEEE journal.
{\copyright} 2022 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G
Due to the explosive growth in the number of wireless devices and diverse
wireless services, such as virtual/augmented reality and
Internet-of-Everything, next generation wireless networks face unprecedented
challenges caused by heterogeneous data traffic, massive connectivity, and
ultra-high bandwidth efficiency and ultra-low latency requirements. To address
these challenges, advanced multiple access schemes are expected to be
developed, namely next generation multiple access (NGMA), which are capable of
supporting massive numbers of users in a more resource- and
complexity-efficient manner than existing multiple access schemes. As the
research on NGMA is in a very early stage, in this paper, we explore the
evolution of NGMA with a particular focus on non-orthogonal multiple access
(NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review
the fundamental capacity limits of NOMA, elaborate on the new requirements for
NGMA, and discuss several possible candidate techniques. Moreover, given the
high compatibility and flexibility of NOMA, we provide an overview of current
research efforts on multi-antenna techniques for NOMA, promising future
application scenarios of NOMA, and the interplay between NOMA and other
emerging physical layer techniques. Furthermore, we discuss advanced
mathematical tools for facilitating the design of NOMA communication systems,
including conventional optimization approaches and new machine learning
techniques. Next, we propose a unified framework for NGMA based on multiple
antennas and NOMA, where both downlink and uplink transmissions are considered,
thus setting the foundation for this emerging research area. Finally, several
practical implementation challenges for NGMA are highlighted as motivation for
future work.Comment: 34 pages, 10 figures, a survey paper accepted by the IEEE JSAC
special issue on Next Generation Multiple Acces
Energy-efficient non-orthogonal multiple access for wireless communication system
Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed
- …