Son Dinh-van;Hien Quoc Ngo;Simon L. Cotton;Yuen Kwan Mo;Matthew D. Higgins
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The approach to transmission power control is formulated as a non-convex optimization problem aiming to maximize the total accumulated power achieved by all IoT devices while taking into account the power weights at the APs, pilot power control at the IoT devices, and the non-linearity of practical energy harvesting circuits. An alternating optimization approach is adopted to solve it iteratively, achieving convergence within just a few iterations. Furthermore, since the number of IoT devices might be enormous in mMTC networks, we propose a pilot sharing algorithm allowing IoT devices to reuse pilot sequences effectively. Numerical results are provided to validate the effectiveness of the proposed power control algorithms and the pilot sharing scheme. It is shown that by allowing IoT devices to share the pilot sequences instead of employing the orthogonal pilots, the per-user accumulated performance is enhanced considerably, especially when the number of IoT devices is large relative to the coherence interval. The advantage of using distributed massive MIMO compared to its collocated counterpart is demonstrated in terms of the per-user accumulated power.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7160-7175"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742936","citationCount":"0","resultStr":"{\"title\":\"Distributed Massive MIMO for Wireless Power Transfer in the Industrial Internet of Things\",\"authors\":\"Son Dinh-van;Hien Quoc Ngo;Simon L. Cotton;Yuen Kwan Mo;Matthew D. Higgins\",\"doi\":\"10.1109/OJCOMS.2024.3491354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers wireless power transfer (WPT) for powering low-power devices in massive Machine Type Communication (mMTC) using a distributed massive multiple-input multiple-output (MIMO) system. Each Internet of Things (IoT) device can be served by one or more access points (APs) which is equipped with a massive antenna array. During each time slot, each IoT device transmits pilot sequences to enable APs to perform channel estimation. This process is followed by the WPT using conjugate beamforming. The approach to transmission power control is formulated as a non-convex optimization problem aiming to maximize the total accumulated power achieved by all IoT devices while taking into account the power weights at the APs, pilot power control at the IoT devices, and the non-linearity of practical energy harvesting circuits. An alternating optimization approach is adopted to solve it iteratively, achieving convergence within just a few iterations. Furthermore, since the number of IoT devices might be enormous in mMTC networks, we propose a pilot sharing algorithm allowing IoT devices to reuse pilot sequences effectively. Numerical results are provided to validate the effectiveness of the proposed power control algorithms and the pilot sharing scheme. It is shown that by allowing IoT devices to share the pilot sequences instead of employing the orthogonal pilots, the per-user accumulated performance is enhanced considerably, especially when the number of IoT devices is large relative to the coherence interval. 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Distributed Massive MIMO for Wireless Power Transfer in the Industrial Internet of Things
This paper considers wireless power transfer (WPT) for powering low-power devices in massive Machine Type Communication (mMTC) using a distributed massive multiple-input multiple-output (MIMO) system. Each Internet of Things (IoT) device can be served by one or more access points (APs) which is equipped with a massive antenna array. During each time slot, each IoT device transmits pilot sequences to enable APs to perform channel estimation. This process is followed by the WPT using conjugate beamforming. The approach to transmission power control is formulated as a non-convex optimization problem aiming to maximize the total accumulated power achieved by all IoT devices while taking into account the power weights at the APs, pilot power control at the IoT devices, and the non-linearity of practical energy harvesting circuits. An alternating optimization approach is adopted to solve it iteratively, achieving convergence within just a few iterations. Furthermore, since the number of IoT devices might be enormous in mMTC networks, we propose a pilot sharing algorithm allowing IoT devices to reuse pilot sequences effectively. Numerical results are provided to validate the effectiveness of the proposed power control algorithms and the pilot sharing scheme. It is shown that by allowing IoT devices to share the pilot sequences instead of employing the orthogonal pilots, the per-user accumulated performance is enhanced considerably, especially when the number of IoT devices is large relative to the coherence interval. The advantage of using distributed massive MIMO compared to its collocated counterpart is demonstrated in terms of the per-user accumulated power.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.