| 1. |
- Khan, Wali Ullah, et al.
(författare)
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Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks
- 2023
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276. ; , s. 1-
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Tidskriftsartikel (refereegranskat)abstract
- Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.
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| 2. |
- Qian, Liangxin, et al.
(författare)
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Distributed Learning for Wireless Communications : Methods, Applications and Challenges
- 2022
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Ingår i: IEEE Journal on Selected Topics in Signal Processing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1932-4553 .- 1941-0484. ; 16:3, s. 326-342
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Tidskriftsartikel (refereegranskat)abstract
- With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, research on distributed learning for wireless communications is still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.
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| 3. |
- Xiao, Yue, et al.
(författare)
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Fully Decentralized Federated Learning-Based On-Board Mission for UAV Swarm System
- 2021
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Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1089-7798 .- 1558-2558. ; 25:10, s. 3296-3300
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Tidskriftsartikel (refereegranskat)abstract
- To handle the data explosion in the era of Internet-of-things, it is of interest to investigate the decentralized network, with the aim at relaxing the burden at the central server along with preserving data privacy. In this work, we develop a fully decentralized federated learning (FL) framework with an inexact stochastic parallel random walk alternating direction method of multipliers (ISPW-ADMM). Performing more efficient communication and enhanced privacy preservation compared with the current state-of-the-art, the proposed ISPW-ADMM can be partially immune to the effect of time-varying dynamic network and stochastic data collection, while still in fast convergence. Benefiting from the stochastic gradients and biased first-order moment estimation, the proposed framework can be applied to any decentralized FL tasks over time-varying graphs. Thus, to demonstrate the practicability of such a framework in providing fast convergence, high communication efficiency, noise robustness for a specific on-board mission to some extent, we study the extreme learning machine-based FL model beamforming design in unmanned aerial vehicle communications, as verified by the numerical simulations.
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| 4. |
- Yang, Ping, et al.
(författare)
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Editorial : Introduction to the Issue on Distributed Machine Learning for Wireless Communication
- 2022
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Ingår i: IEEE Journal on Selected Topics in Signal Processing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1932-4553 .- 1941-0484. ; 16:3, s. 320-325
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The papers in this special section focus on the use of distributed machine learning for wireless communications. With the emergence of new application scenarios (e.g., real-time and interactive services and Internet of Things) and the fast development of smart terminals, wireless data traffic has increased drastically, and the existing wireless networks cannot completely meet the technical requirements of the next generation mobile communication networks, e.g., 6G. In recent years, machine learning-based methods have been considered as potential technologies for 6G, because in wireless communication systems, key issues behind synchronization, channel estimation, signal detection, and iterative decoding can be solved by well-designed machine learning algorithms. Currently, most wireless network machine learning solutions require the training data and learning process to be centralized in one or more data centers. However, these centralized machine learning methods expose disadvantages, e.g., privacy security, significant signaling overhead, increased implementation complexity, and high latency, which limit their practicality. The wireless networks of the future must make quicker and more reliable decisions at the network edge.
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