175 research outputs found
Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting
Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has
aroused. Specifically, UAVs can be used in cellular networks as aerial users
for delivery, surveillance, rescue search, or as an aerial base station (aBS)
for communication with ground users in remote uncovered areas or in dense
environments requiring prompt high capacity. Aiming to satisfy the high
requirements of wireless aerial networks, several multiple access techniques
have been investigated. In particular, space-division multiple access(SDMA) and
power-domain non-orthogonal multiple access (NOMA) present promising
multiplexing gains for aerial downlink and uplink. Nevertheless, these gains
are limited as they depend on the conditions of the environment. Hence, a
generalized scheme has been recently proposed, called rate-splitting multiple
access (RSMA), which is capable of achieving better spectral efficiency gains
compared to SDMA and NOMA. In this paper, we present a comprehensive survey of
key multiple access technologies adopted for aerial networks, where aBSs are
deployed to serve ground users. Since there have been only sporadic results
reported on the use of RSMA in aerial systems, we aim to extend the discussion
on this topic by modelling and analyzing the weighted sum-rate performance of a
two-user downlink network served by an RSMA-based aBS. Finally, related open
issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed
AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems
In this paper, we investigate the performance enhancement of Multiple Input, Multiple Output, and Non-Orthogonal Multiple Access (MIMO-NOMA) wireless communication systems using an Artificial Intelligence (AI) based Q-Learning reinforcement learning approach. The primary challenge addressed is the optimization of power allocation in a MIMO-NOMA system, a complex task given the non-convex nature of the problem. Our proposed Q-Learning approach adaptively adjusts power allocation strategy for proximal and distant users, optimizing the trade-off between various conflicting metrics and significantly improving the system’s performance. Compared to traditional power allocation strategies, our approach showed superior performance across three principal parameters: spectral efficiency, achievable sum rate, and energy efficiency. Specifically, our methodology achieved approximately a 140% increase in the achievable sum rate and about 93% improvement in energy efficiency at a transmitted power of 20 dB while also enhancing spectral efficiency by approximately 88.6% at 30 dB transmitted Power. These results underscore the potential of reinforcement learning techniques, particularly Q-Learning, as practical solutions for complex optimization problems in wireless communication systems. Future research may investigate the inclusion of enhanced channel simulations and network limitations into the machine learning framework to assess the feasibility and resilience of such intelligent approaches
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
Multidimensional Index Modulation for 5G and Beyond Wireless Networks
This study examines the flexible utilization of existing IM techniques in a
comprehensive manner to satisfy the challenging and diverse requirements of 5G
and beyond services. After spatial modulation (SM), which transmits information
bits through antenna indices, application of IM to orthogonal frequency
division multiplexing (OFDM) subcarriers has opened the door for the extension
of IM into different dimensions, such as radio frequency (RF) mirrors, time
slots, codes, and dispersion matrices. Recent studies have introduced the
concept of multidimensional IM by various combinations of one-dimensional IM
techniques to provide higher spectral efficiency (SE) and better bit error rate
(BER) performance at the expense of higher transmitter (Tx) and receiver (Rx)
complexity. Despite the ongoing research on the design of new IM techniques and
their implementation challenges, proper use of the available IM techniques to
address different requirements of 5G and beyond networks is an open research
area in the literature. For this reason, we first provide the dimensional-based
categorization of available IM domains and review the existing IM types
regarding this categorization. Then, we develop a framework that investigates
the efficient utilization of these techniques and establishes a link between
the IM schemes and 5G services, namely enhanced mobile broadband (eMBB),
massive machine-type communications (mMTC), and ultra-reliable low-latency
communication (URLLC). Additionally, this work defines key performance
indicators (KPIs) to quantify the advantages and disadvantages of IM techniques
in time, frequency, space, and code dimensions. Finally, future recommendations
are given regarding the design of flexible IM-based communication systems for
5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible
publicatio
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