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11 pages, 540 KiB  
Article
Research on Waveform Adaptability Based on Lunar Channels
by Min Jia, Jonghui Li, Zijie Wang, Chao Zhao, Daifu Yan, Hui Wang, Dongmei Li and Weiran Sun
Electronics 2024, 13(24), 5047; https://doi.org/10.3390/electronics13245047 - 22 Dec 2024
Viewed by 401
Abstract
In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it [...] Read more.
In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments. Full article
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<p>Propagation path of lunar electromagnetic waves.</p>
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<p>BER performance versus the length of CP. (<b>a</b>) Uplink BER performance versus the length of CP in 20 MHz bandwidth. (<b>b</b>) Downlink BER performance versus the length of CP in 20 MHz bandwidth. (<b>c</b>) Uplink BER performance versus the length of CP in 10 MHz bandwidth. (<b>d</b>) Downlink BER performance versus the length of CP in 10 MHz bandwidth.</p>
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<p>Performance comparison of different modulation methods. (<b>a</b>) Performance comparison of different modulation methods for 20 M bandwidth. (<b>b</b>) Performance comparison of different modulation methods for 10 M bandwidth.</p>
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<p>Comparison of PAPR for different modulation schemes and orders. (<b>a</b>) Comparison of PAPR for different modulation schemes under 20 MHz bandwidth. (<b>b</b>) Comparison of PAPR for different modulation orders of QAM under 20 MHz bandwidth. (<b>c</b>) Comparison of PAPR for different modulation orders of APSK under 20 MHz bandwidth. (<b>d</b>) Comparison of PAPR for different modulation orders of PSK under 20 MHz bandwidth.</p>
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<p>Comparison of PAPR for different modulation schemes and orders. (<b>a</b>) Comparison of PAPR for different modulation schemes under 10MHz bandwidth. (<b>b</b>) Comparison of PAPR for different modulation orders of QAM under 10MHz bandwidth. (<b>c</b>) Comparison of PAPR for different modulation orders of APSK under 10MHz bandwidth. (<b>d</b>) Comparison of PAPR for different modulation orders of PSK under 10MHz bandwidth.</p>
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<p>Comparison of PAPR for different modulation schemes and orders. (<b>a</b>) Comparison of PAPR for different modulation schemes under 10MHz bandwidth. (<b>b</b>) Comparison of PAPR for different modulation orders of QAM under 10MHz bandwidth. (<b>c</b>) Comparison of PAPR for different modulation orders of APSK under 10MHz bandwidth. (<b>d</b>) Comparison of PAPR for different modulation orders of PSK under 10MHz bandwidth.</p>
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<p>Comparison of BER performance for different modulation schemes. (<b>a</b>) Comparison of QAM and PSK modulation BER performance in 20 MHz bandwidth. (<b>b</b>) Comparison of BER performance for different modulation schemes in 20 MHz bandwidth. (<b>c</b>) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth. (<b>d</b>) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth.</p>
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<p>Comparison of BER performance for different modulation schemes. (<b>a</b>) Comparison of QAM and PSK modulation BER performance in 20 MHz bandwidth. (<b>b</b>) Comparison of BER performance for different modulation schemes in 20 MHz bandwidth. (<b>c</b>) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth. (<b>d</b>) Comparison of QAM and PSK modulation BER performance in 10 MHz bandwidth.</p>
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17 pages, 1866 KiB  
Article
Adaptive Channel Division and Subchannel Allocation for Orthogonal Frequency Division Multiple Access-Based Airborne Power Line Communication Networks
by Ruowen Yan, Qiao Li and Huagang Xiong
Sensors 2024, 24(23), 7644; https://doi.org/10.3390/s24237644 - 29 Nov 2024
Viewed by 416
Abstract
This paper addresses the critical needs of the aviation industry in advancing towards More Electric Aircraft (MEA) by leveraging power line communication (PLC) technology, which merges data and power transmission to offer substantial reductions in aircraft system weight and cost. We introduce pioneering [...] Read more.
This paper addresses the critical needs of the aviation industry in advancing towards More Electric Aircraft (MEA) by leveraging power line communication (PLC) technology, which merges data and power transmission to offer substantial reductions in aircraft system weight and cost. We introduce pioneering algorithms for channel division and subchannel allocation within Orthogonal Frequency Division Multiple Access (OFDMA)-based airborne PLC networks, aimed at optimizing network performance in key areas such as throughput, average delay, and fairness. The proposed channel division algorithm dynamically adjusts the count of subchannels to maximize Channel Division Gain (CDG), responding adeptly to fluctuations in network conditions and node density. Concurrently, the subchannel allocation algorithm employs a novel metric, the Subchannel Preference Score (SPS), which factors in both the signal quality and the current occupancy levels of each subchannel to determine their optimal allocation among nodes. This method ensures efficient resource utilization and maintains consistent network performance. Extensive simulations, conducted using the OMNeT++ simulator, have demonstrated that our adaptive algorithms significantly outperform existing methods, providing higher throughput, reduced delays, and improved fairness across the network. These advancements represent a significant leap in MAC protocol design for airborne PLC systems. The outcomes suggest that our algorithms offer a robust and adaptable solution, aligning with the rigorous demands of modern avionics and paving the way for the future integration of MEA technologies. Full article
(This article belongs to the Section Communications)
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<p>Beacon Period structure.</p>
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<p>HomePlug AV2 MAC frame transmission timeline in the CSMA/CA mode.</p>
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<p>Network architecture cell schematic.</p>
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<p>Throughput comparison of different schemes with varying node count. R-PMAC [<a href="#B29-sensors-24-07644" class="html-bibr">29</a>], Static Subchannel Random Access [<a href="#B34-sensors-24-07644" class="html-bibr">34</a>].</p>
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<p>Average delay comparison of different schemes with varying node counts. R-PMAC [<a href="#B29-sensors-24-07644" class="html-bibr">29</a>], Static Subchannel Random Access [<a href="#B34-sensors-24-07644" class="html-bibr">34</a>].</p>
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<p>Fairness index comparison of different schemes with varying node counts. R-PMAC [<a href="#B29-sensors-24-07644" class="html-bibr">29</a>], Static Subchannel Random Access [<a href="#B34-sensors-24-07644" class="html-bibr">34</a>].</p>
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25 pages, 2011 KiB  
Article
Optimized Architecture for Efficient OFDMA Network Design
by Sonia Ben Brahim, Amira Zrelli, Samia Dardouri and Ridha Bouallegue
Telecom 2024, 5(4), 1051-1075; https://doi.org/10.3390/telecom5040054 - 1 Nov 2024
Viewed by 753
Abstract
This study presents a novel approach to enhancing the design and performance of OFDMA (Orthogonal Frequency Division Multiple Access) networks, with a particular focus on WiMAX (Worldwide Interoperability for Microwave Access) for Best Effort (BE) services. The proposed method integrates a robust Markovian [...] Read more.
This study presents a novel approach to enhancing the design and performance of OFDMA (Orthogonal Frequency Division Multiple Access) networks, with a particular focus on WiMAX (Worldwide Interoperability for Microwave Access) for Best Effort (BE) services. The proposed method integrates a robust Markovian analytical model with four advanced scheduling algorithms: throughput fairness, resource fairness, opportunistic scheduling, and throttling. A sophisticated simulator was developed, incorporating an ON/OFF traffic generator, user-specific wireless channels, and a dynamic central scheduler to validate the model’s accuracy and evaluate its robustness by dynamically allocating radio resources per frame. The validation study showed that the proposed model reduced simulation time by over 90%, completing analytical calculations in just 15 min, compared to nearly 2 days for simulations using conventional scheduling algorithms. Performance metrics such as the average number of active users and resource utilization closely matched those from the validation study, confirming the model’s accuracy. In the robustness study, the model consistently performed well across diverse traffic distributions (exponential and Pareto) and channel conditions. The proposed architecture increased network throughput by up to 25% and reduced latency under dynamic conditions, demonstrating its scalability, adaptability, and efficiency as a crucial solution for next-generation wireless communication systems. Full article
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<p>The Multidimensional Continuous-Time Markov Chain.</p>
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<p>The system studied for the dimensioning study.</p>
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<p>Simulator diagram.</p>
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<p>The average instantaneous throughput for conventional scheduling algorithms (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits and t<sub>off</sub> = 3 s</span>).</p>
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<p>The average number of active users for conventional scheduling algorithms (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, and t<sub>off</sub> = 3 s</span>).</p>
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<p>The average resource utilization for conventional scheduling algorithms (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, and t<sub>off</sub> = 3 s</span>).</p>
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<p>The stationary state probability for conventional scheduling algorithms (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, and t<sub>off</sub> = 3 s</span>).</p>
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<p>The average number of active users for the Throttling regime (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, t<sub>off</sub> = 3 s, and MSTR = 512 Kbps</span>).</p>
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<p>The average resource utilization for the Throttling regime (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, t<sub>off</sub> = 3 s and MSTR = 512 Kbps</span>).</p>
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<p>The stationary state probability for the Throttling regime (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, t<sub>off</sub> = 3 s, and MSTR = 512 Kbps</span>).</p>
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<p>The average instantaneous throughput for the throughput fairness algorithm and the different traffic distributions (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, and t<sub>off</sub> = 3 s</span>).</p>
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<p>The average instantaneous throughput for the throughput fairness algorithm and the different channel models (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, and t<sub>off</sub> = 3 s</span>).</p>
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<p>The average instantaneous throughput of active users during an ON period for the different traffic distributions in the Throttling regime (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, t<sub>off</sub> = 3 s, and MSTR = 2048 Kbps</span>).</p>
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<p>The average instantaneous throughput of active users during an ON period for the different channel models in the Throttling regime (<span class="html-italic">N = 10, x<sub>on</sub> = 3 Mbits, t<sub>off</sub> = 3 s, and MSTR = 2048 Kbps</span>).</p>
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28 pages, 1456 KiB  
Article
Optimizing the Timeliness of Hybrid OFDMA-NOMA Sensor Networks with Stability Constraints
by Wei Wang, Yunquan Dong and Chengsheng Pan
Electronics 2024, 13(9), 1768; https://doi.org/10.3390/electronics13091768 - 3 May 2024
Viewed by 923
Abstract
In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system [...] Read more.
In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system where the users are partitioned into several groups. While users in each group share the same resource block using non-orthogonal multiple access (NOMA), different groups access the fading channel using orthogonal frequency division multiple access (OFDMA). For this system, we consider three decoding schemes at the service terminals: interfering decoding, which treats signals from other users as interference; serial interference cancellation, which removes signals from other users once they have been decoded; and the enhanced SIC strategy, where the receiver attempts to decode for another user if decoding for a previous user fails. We present the average AoI for each of the three decoding schemes in closed form. Under the constraint of the stable region, we find the minimum AoI of each decoding scheme efficiently. The numerical results show that by optionally choosing the decoding scheme and transmission rate, the hybrid OFDMA-NOMA outperforms conventional OFDMA in terms of both system timeliness and stability. Full article
(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
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<p>System model.</p>
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<p>Stabilization regions corresponding to the three decoding strategies.</p>
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<p>Discrete-time age of information sample paths.</p>
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<p>Stable regions of three decoding strategies at high decoding thresholds.</p>
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<p>Stable regions of three decoding strategies at low decoding thresholds.</p>
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<p>Age of information of the dominant system in the uplink. (<b>a</b>) Interference decoding: age of information for the first dominant system. (<b>b</b>) Interference decoding: age of information for the second dominant system. (<b>c</b>) Serial interference cancellation: age of information for the first dominant system. (<b>d</b>) Serial interference cancellation: age of information for the second dominant system. (<b>e</b>) Enhanced SIC strategy with error handling: age of information for the first dominant system. (<b>f</b>) Enhanced SIC strategy with error handling: age of information for the second dominant system.</p>
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<p>Minimum age of information for systems with different policies.</p>
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<p>OFDMA-NOMA optimal policy vs. the OFDMA age of information.</p>
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<p>The relationship between the average service rate of node <span class="html-italic">i</span> and SNR. (<b>a</b>) The relationship between the average service rate of node 1 and SNR. (<b>b</b>) The relationship between the average service rate of node 2 and SNR.</p>
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18 pages, 541 KiB  
Article
Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
by Samaneh Bidabadi, Messaoud Ahmed Ouameur, Miloud Bagaa and Daniel Massicotte
Electronics 2024, 13(6), 1040; https://doi.org/10.3390/electronics13061040 - 11 Mar 2024
Cited by 1 | Viewed by 1217
Abstract
The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the [...] Read more.
The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and the RIS phase shift matrix. To address this complex optimization problem, we propose a novel practical and low-complexity approach that is based on optimizing a computationally efficient and numerically tractable lower bound on energy efficiency. The numerical results highlight the effectiveness of our approach, demonstrating a substantial increase in energy efficiency compared to scenarios without RIS, with random RIS integration, and with the scheme using the Genetic Algorithm (GA). Full article
(This article belongs to the Special Issue Advances in Future Wireless Networks)
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<p>The considered RIS-based multi-user SISO-OFDMA system.</p>
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<p>Average EE versus <math display="inline"><semantics> <msub> <mi>P</mi> <mi>max</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics></math> bps/Hz, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>RIS</mi> </mrow> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>K</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics></math>.</p>
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<p>Average SE versus <math display="inline"><semantics> <msub> <mi>P</mi> <mi>max</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics></math> bps/Hz, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>RIS</mi> </mrow> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>K</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics></math>.</p>
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<p>Average EE versus number of RIS elements, <span class="html-italic">M</span> for <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics></math> bps/Hz, <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>RIS</mi> </mrow> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>max</mi> </msub> <mo>=</mo> <mn>20</mn> <mspace width="0.166667em"/> <mi>dBm</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics></math>.</p>
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18 pages, 1117 KiB  
Article
Performance Analysis of Power Allocation and User-Pairing Techniques for MIMO-NOMA in VLC Systems
by Hesham S. Ibrahim, Mohamed Abaza, Ali Mansour and Ayman Alfalou
Photonics 2024, 11(3), 206; https://doi.org/10.3390/photonics11030206 - 25 Feb 2024
Cited by 2 | Viewed by 1518
Abstract
In this paper, we evaluate the performance of multiple-input multiple-output (MIMO) communication systems applied with a non-orthogonal multiple access (NOMA)-based indoor visible light communication (VLC). We present two efficient user-pairing algorithms for NOMA in VLC, aiming to enhance achievable data rates effectively. Our [...] Read more.
In this paper, we evaluate the performance of multiple-input multiple-output (MIMO) communication systems applied with a non-orthogonal multiple access (NOMA)-based indoor visible light communication (VLC). We present two efficient user-pairing algorithms for NOMA in VLC, aiming to enhance achievable data rates effectively. Our investigation involves the application of three low-complexity power allocation techniques. Comparative analysis reveals performance enhancements when employing the proposed schemes, especially when contrasted with NOMA without user pairing and orthogonal frequency division multiple access (OFDMA). Additionally, we explore the performance of both algorithms in scenarios with both even and odd numbers of users. Simulation results demonstrate the superiority of NOMA in comparison to OFDMA. Full article
(This article belongs to the Special Issue New Advances in Optical Wireless Communication)
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<p>The downlink 2 × 2 NOMA-MIMO-VLC system serving <span class="html-italic">N</span> users [<a href="#B4-photonics-11-00206" class="html-bibr">4</a>].</p>
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<p>VLC channel model.</p>
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<p>Schematic of a 2 × 2 NOMA-MIMO-VLC system with <span class="html-italic">N</span> users [<a href="#B4-photonics-11-00206" class="html-bibr">4</a>].</p>
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<p>Illustration of user grouping and pairing for even and odd number of users using NLUPA and UCGD.</p>
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<p>Achievable rate vs. normalized offset-based NOMA and OFDMA with two users (<span class="html-italic">N</span> = 2) (<b>a</b>) LED 1 (<b>b</b>) LED 2.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with five users (<span class="html-italic">N</span> = 5) for LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and for LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with five users (<span class="html-italic">N</span> = 5) with Strategy 1 NLUPA/UCGD user pairing of LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and of LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with five users (<span class="html-italic">N</span> = 5) with Strategy 2 NLUPA user pairing of LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and of LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with five users (<span class="html-italic">N</span> = 5) with Strategy 2 UCGD user pairing of LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and of LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with six users (<span class="html-italic">N</span> = 6) for LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and for LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with six users (<span class="html-italic">N</span> = 6) with NLUPA user-pairing of LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and of LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Achievable rate vs. normalized offset-based NOMA with six users (<span class="html-italic">N</span> = 6) with UCGD user-pairing of LED 1 using (<b>a</b>) fixed power allocation (FPA), (<b>b</b>) gain ratio power allocation (GRPA), (<b>c</b>) normalized gain difference power allocation (NGDPA), and of LED 2 using (<b>d</b>) FPA, (<b>e</b>) GRPA, (<b>f</b>) NGDPA.</p>
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<p>Sum rate vs. normalized offset-based OFDMA and NOMA with five users (<span class="html-italic">N</span> = 5) (<b>a</b>) without grouping, (<b>b</b>) using NLUPA/UCGD Strategy 1, (<b>c</b>) NLUPA Strategy 2, (<b>d</b>) UCGD Strategy 2, and six users (<span class="html-italic">N</span> = 6) (<b>e</b>) without grouping, (<b>f</b>) using NLUPA and (<b>g</b>) UCGD.</p>
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28 pages, 814 KiB  
Review
The Age of Information in Wireless Cellular Systems: Gaps, Open Problems, and Research Challenges
by Elena Zhbankova, Abdukodir Khakimov, Ekaterina Markova and Yuliya Gaidamaka
Sensors 2023, 23(19), 8238; https://doi.org/10.3390/s23198238 - 3 Oct 2023
Cited by 3 | Viewed by 1982
Abstract
One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services [...] Read more.
One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services that need to be supported by 5G systems. The recently introduced the age of information (AoI) metric representing the timeliness of the reception of the update at the receiver is nowadays commonly utilized to quantify the performance of such services. However, the metric itself is closely related to the queueing theory, which conventionally requires strict assumptions for analytical tractability. This review paper aims to: (i) identify the gaps between technical wireless systems and queueing models utilized for analysis of the AoI metric; (ii) provide a detailed review of studies that have addressed the AoI metric; and (iii) establish future research challenges in this area. Our major outcome is that the models proposed to date for the AoI performance evaluation and optimization deviate drastically from the technical specifics of modern and future wireless cellular systems, including those proposed for URLLC and mMTC services. Specifically, we identify that the majority of the models considered to date: (i) do not account for service processes of wireless channel that utilize orthogonal frequency division multiple access (OFDMA) technology and are able to serve more than a single packet in a time slot; (ii) neglect the specifics of the multiple access schemes utilized for mMTC communications, specifically, multi-channel random access followed by data transmission; (iii) do not consider special and temporal correlation properties in the set of end systems that may arise naturally in state monitoring applications; and finally, (iv) only few studies have assessed those practical use cases where queuing may happen at more than a single node along the route. Each of these areas requires further advances for performance optimization and integration of modern and future wireless provisioning technologies with mMTC and URLLC services. Full article
(This article belongs to the Special Issue 5G/6G Networks for Wireless Communication and IoT)
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<p>Illustration of the considered system model.</p>
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<p>Detailed time diagram for transferring updates to the CC.</p>
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<p>The information stored in the CC database.</p>
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<p>The AoI and the PAoI dynamics for a single ED.</p>
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<p>The AoI <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and the PAoI <math display="inline"><semantics> <msub> <mi>A</mi> <mi>n</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> for a single ED.</p>
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<p>The mean PAoI in the considered <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>/</mo> <mi>M</mi> <mo>/</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>/</mo> <mi>D</mi> <mo>/</mo> <mn>1</mn> </mrow> </semantics></math> systems.</p>
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<p>The mean full delay in the considered <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>/</mo> <mi>M</mi> <mo>/</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>/</mo> <mi>D</mi> <mo>/</mo> <mn>1</mn> </mrow> </semantics></math> systems.</p>
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<p>The overall framework for remote-control operation.</p>
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<p>The possible events in 6G mMTC system: I—Idle; S—Success; C—Collision; E—Error.</p>
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<p>The polling service process over a wireless channel.</p>
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16 pages, 4295 KiB  
Article
High-Capacity Free Space Optics-Based Passive Optical Network for 5G Front-Haul Deployment
by Rahat Ullah, Sibghat Ullah, Waqas A. Imtiaz, Jahangir Khan, Peer Meher Ali Shah, Muhammad Kamran, Jianxin Ren and Shuaidong Chen
Photonics 2023, 10(10), 1073; https://doi.org/10.3390/photonics10101073 - 24 Sep 2023
Cited by 13 | Viewed by 1710
Abstract
With the expansion of Information and Communication Technology, it is important to develop a communication network that can provide high-capacity ubiquitous connectivity. This work proposes an energy-efficient passive optical network (PON) using orthogonal frequency division multiple access (OFDMA) and wavelength division multiplexing (WDM) [...] Read more.
With the expansion of Information and Communication Technology, it is important to develop a communication network that can provide high-capacity ubiquitous connectivity. This work proposes an energy-efficient passive optical network (PON) using orthogonal frequency division multiple access (OFDMA) and wavelength division multiplexing (WDM) to facilitate the dense deployment of radio units (RUs) in a beyond 5G (B5G) communication network. High-speed connectivity is ensured by employing a hybrid PON architecture that includes a combination of free space optics (FSO) links and optical fiber (OF) media to carry OFDM and WDM multiplexed traffic. Furthermore, an optical frequency comb generator (OFCG) is utilized at the transmitter module to generate and leverage the spectrum for transmitting information from baseband units (BBUs) to the RUs situated near the end users. The proposed system is analyzed through (i) simulation analysis using Optisystem for transmission capacity computations and (ii) mathematical analysis to determine the total savings in energy. The simulation analysis shows that the given architecture can carry data across 3 km of FSO medium using 512 subcarriers per BBU transmitting at 10 Gbps of data with QPSK-modulated bit sequence. Additionally, energy efficiency shows that the use of an OFCG cuts the total energy usage by 22% at the transmitter module without negatively impacting the system’s high cardinality and transmission capacity. Full article
(This article belongs to the Special Issue Novel Advances in Optical Communications)
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<p>CRAN architecture.</p>
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<p>Proposed architecture of OFDM-WDM-PON for BBU to RU connectivity.</p>
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<p>OFDM modulator operation blocks.</p>
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<p>Optisystem simulation model of the proposed architecture.</p>
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<p>OFCG output with different wavelengths at 25 GHz spacing.</p>
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<p>Achieved BER against various channel lengths and with different attenuations.</p>
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<p>BER versus received power at different turbulence regimes.</p>
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<p>BER versus FSO link length at different weather conditions; constellation diagrams for 1, 2, and 3 km FSO links in hazy weather conditions.</p>
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<p>Energy consumption for OLT components in the BBU pool.</p>
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<p>Energy consumption for OLT and ONU modules.</p>
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<p>Total energy consumption per user for the conventional and proposed architectures.</p>
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14 pages, 393 KiB  
Article
Joint Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Networks and Beyond
by Qusay Alghazali, Husam Al-Amaireh and Tibor Cinkler
Sensors 2023, 23(19), 8040; https://doi.org/10.3390/s23198040 - 23 Sep 2023
Cited by 2 | Viewed by 1427
Abstract
Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research [...] Read more.
Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research community because of its capacity to enhance spectral efficiency. NOMA allows multiple users to share the same frequency band and time slot by assigning different power levels and modulation schemes to different users. Furthermore, channel assignment is a critical challenge in OFDMA-NOMA systems that must be addressed to achieve optimal performance. In this context, we propose a solution for both channel and power assignment based on channel condition by splitting the problem into two parts: first, we introduce a novel algorithm to solve the channel user allocation problem, which we refer to as Channel User Sorting and Filling (CUSF). Then, we solve the power allocation problem in two steps: we apply the water filling algorithm at the power assignment and then we implement the Fractional Transmit Power Control (FTPC) algorithm in the NOMA power assignment. Full article
(This article belongs to the Special Issue 5G/6G Networks for Wireless Communication and IoT)
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<p>Capacity of the system versus different numbers of users.</p>
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<p>System capacity distributed over channels.</p>
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<p>Capacity per user for per sub-channel.</p>
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<p>System capacity for different alpha values.</p>
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16 pages, 3450 KiB  
Article
Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem
by Viacheslav Kovtun, Torki Altameem, Mohammed Al-Maitah and Wojciech Kempa
Electronics 2023, 12(18), 3847; https://doi.org/10.3390/electronics12183847 - 11 Sep 2023
Cited by 2 | Viewed by 1197
Abstract
In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even [...] Read more.
In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even in the case of using Wi-Fi 6/6E-compatible equipment. An effective solution to this problem is the implementation of a centralized management mechanism for the relevant parameters of the target network ecosystem. The growing attention to ecology and rational use of electricity makes the problem of maximizing energy consumption for useful information traffic in a dense Wi-Fi 6/6E ecosystem an urgent task. Only the addressed information traffic between the transmitter and the target subscriber, which are subjects of the OFDMA technology and the MU-MIMO multiple access system (with an emphasis on the latter), is considered useful. To solve the problem, the authors formalized the Wi-Fi 6/6E ecosystem’s energy consumption model, which takes into account the specifics of OFDMA and MU-MIMO, the influence of the communication channel characteristics on the speed of target information transfer, and detailed energy consumption for maintaining the network infrastructure in a functional state. Based on the created model, the research problem is represented by the difference between two monotonic functions, relative to which the problem of optimization with restrictions is set. The process of solving this problem is presented in the form of information technology with a branch-and-bound hierarchy and a nested unconditional optimization problem. The results of simulated modelling in the MATLAB-NS3 environment showed a significant advantage of the authors’ approach. The energy power consumption by the Wi-Fi 6/6E ecosystem, the parameters of which were adjusted with the help of the authors’ information technology, decreased by more than four times. Full article
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<p>Empirical dependence <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi>c</mi> </msub> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <msub> <mi>ρ</mi> <mi>c</mi> </msub> </mrow> </mfenced> </mrow> </semantics></math> for an instance of the channel <math display="inline"><semantics> <mi>c</mi> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>R</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>1</mn> <mo>,</mo> <mn>30</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>: *—for the above trigger values <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>O</mi> <mi>F</mi> <mi>B</mi> </mrow> </msub> <mo>≈</mo> <msub> <mi>f</mi> <mrow> <mi>O</mi> <mi>F</mi> <mi>B</mi> <mo>+</mo> </mrow> </msub> </mrow> </semantics></math>, **—for the above trigger values <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>A</mi> <mi>I</mi> <mi>T</mi> </mrow> </msub> <mo>≈</mo> <msub> <mi>f</mi> <mrow> <mi>A</mi> <mi>I</mi> <mi>T</mi> <mo>+</mo> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>R</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>1</mn> <mo>,</mo> <mn>30</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>: *—for the above trigger values <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>A</mi> <mi>I</mi> <mi>T</mi> </mrow> </msub> <mo>≈</mo> <msub> <mi>f</mi> <mrow> <mi>A</mi> <mi>I</mi> <mi>T</mi> <mo>+</mo> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Π</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>R</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>1</mn> <mo>,</mo> <mn>30</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>R</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>1</mn> <mo>,</mo> <mn>30</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>U</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>10</mn> <mo>,</mo> <mn>140</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>U</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>10</mn> <mo>,</mo> <mn>140</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The dependence of <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mi mathvariant="normal">H</mi> </mrow> <mo>=</mo> <mi>f</mi> <mfenced> <mrow> <mi>U</mi> <mo>=</mo> <mover accent="true"> <mrow> <mn>10</mn> <mo>,</mo> <mn>140</mn> </mrow> <mo stretchy="true">¯</mo> </mover> <mo>;</mo> <mfenced close="}" open="{"> <mrow> <mi>O</mi> <mi>F</mi> <msubsup> <mi>B</mi> <mo>−</mo> <mo>+</mo> </msubsup> <mo>,</mo> <mi>A</mi> <mi>I</mi> <msubsup> <mi>T</mi> <mo>−</mo> <mo>+</mo> </msubsup> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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20 pages, 2709 KiB  
Article
Research on Resource Allocation Strategy of Indoor Visible Light Communication and Radio Frequency Systems Integrating Orthogonal Frequency-Division Multiple Access Technology
by Xizheng Ke, Yaxin Xu, Huanhuan Qin and Jingyuan Liang
Photonics 2023, 10(9), 1016; https://doi.org/10.3390/photonics10091016 - 5 Sep 2023
Cited by 3 | Viewed by 1104
Abstract
Aiming at the problem of resource allocation strategies limiting the system transmission rate in indoor visible light communication and radio frequency (VLC/RF) systems, a new resource allocation method is proposed, and orthogonal frequency-division multiple access (OFDMA) technology is introduced. The capacity of the [...] Read more.
Aiming at the problem of resource allocation strategies limiting the system transmission rate in indoor visible light communication and radio frequency (VLC/RF) systems, a new resource allocation method is proposed, and orthogonal frequency-division multiple access (OFDMA) technology is introduced. The capacity of the communication system is effectively increased. In the VLC/RF system model based on OFDMA, the Lyapunov optimization method is used to transform the time averaging problem into a series of single-slot online problems to reduce its computational complexity, the optimization problem is decomposed into three independent subproblems, and the Lagrange relaxation method and convex optimization theory are used to solve the subproblems to maximize the average transmission rate of the system, and the Lyapunov drift is used to ensure the stability of the system. Simulation verifies that the Lyapunov optimization algorithm does not require iteration, which improves the optimization speed to a high extent. The simulation results show that the proposed resource allocation strategy effectively balances the system queue length and transmission rate, improves the average transmission rate of the system to the greatest extent under the premise of ensuring the stability of the system, and compares with other algorithms from the aspects of system stability and transmission rate, which proves the effectiveness of the Lyapunov optimization algorithm. Full article
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<p>Indoor VLC/RF heterogeneous system model.</p>
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<p>Indoor VLC downlink LOS link model.</p>
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<p>OFDMA system resource allocation model.</p>
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<p>(<b>a</b>) Relationship between average queue length Q and control parameter V; (<b>b</b>) relationship between average transmission rate R and control parameter V.</p>
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<p>The instantaneous transmission rate and queue length of each user change under different V values. (<b>a</b>) V = 50; (<b>b</b>) V = 150; (<b>c</b>) V = 300.</p>
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<p>The instantaneous transmission rate and queue length of each user change under different V values. (<b>a</b>) V = 50; (<b>b</b>) V = 150; (<b>c</b>) V = 300.</p>
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<p>(<b>a</b>) The relationship between the average queue length Q and the number of users under the three algorithms; (<b>b</b>) the relationship between the average transmission rate R and the number of users under the three algorithms.</p>
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<p>(<b>a</b>) The relationship between the average queue length Q of the outage probability and the number of users is considered under the three algorithms; (<b>b</b>) the relationship between the average transmission rate R of the outage probability and the number of users is considered under the three algorithms.</p>
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<p>(<b>a</b>) The relationship between the average queue length Q and the penalty parameter V under different numbers of users in the uplink RF link; (<b>b</b>) the relationship between the average transmission rate R and the penalty parameter V of the uplink RF link under different number of users.</p>
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<p>The performance of the OFDMA-VLC/RF system is compared with the VLC/RF system: (<b>a</b>) comparison of the average queue length Q of the system; (<b>b</b>) comparison of the average transmission rate R of the system.</p>
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18 pages, 2522 KiB  
Article
Performance Analysis of QoS-Oriented OFDMA Protocol Based on IEEE 802.11ax for Cognitive Radio Network
by Suoping Li, Hailing Yang, Ruiman Gao, Tongtong Jia and Hongli Li
Appl. Sci. 2023, 13(12), 7163; https://doi.org/10.3390/app13127163 - 15 Jun 2023
Cited by 1 | Viewed by 1239
Abstract
To improve the quality of service (QoS) on the internet of medical things, a cognitive radio (CR) protocol based on orthogonal frequency division multiple access (OFDMA) is proposed, named CR-OFDMA. In this protocol, we divide a complete channel into multiple orthogonal subchannels and [...] Read more.
To improve the quality of service (QoS) on the internet of medical things, a cognitive radio (CR) protocol based on orthogonal frequency division multiple access (OFDMA) is proposed, named CR-OFDMA. In this protocol, we divide a complete channel into multiple orthogonal subchannels and enhance the subchannel assignment scheme, which achieves QoS improvement under consideration of priority and fairness. Furthermore, we improve spectrum resource utilization by fully utilizing the remaining subchannels, feedback slots, and backoff slots. Then, a two-dimensional Markov model is established to describe the dynamic characteristics of the protocol operation, where the backoff stage and the backoff counter value constitute the system state. By establishing the traffic conservation equations for the system operation, the transmission probability and collision probability are calculated, and expressions of system throughput, channel utilization, and fairness index are derived. Finally, numerical results validate the advantages of CR-OFDMA. Full article
(This article belongs to the Special Issue 5G/6G Mechanisms, Services, and Applications)
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<p>CR-OFDMA systems with <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> </mrow> </semantics></math> critical patients and <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math> mild patients.</p>
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<p>The main idea of the CR-OFDMA.</p>
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<p>Channel access and data transmission process of the CR-OFDMA.</p>
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<p>Utilization of spectrum resources by SU (<math display="inline"><semantics> <mrow> <mi>a</mi> <mo>≤</mo> <mn>1</mn> </mrow> </semantics></math>).</p>
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<p>Utilization of the spectrum resources by SU <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>a</mi> <mo>&gt;</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>Contrast CR-OFDMA with traditional OFDMA and DRA-OFDMA.</p>
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<p>The discrete-time two-dimensional Markov chain for high-priority STAs.</p>
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<p>(<b>a</b>) The system throughput versus the number of low-priority patients <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math> for the cases <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>; (<b>b</b>) The system throughput versus the number of low-priority patients <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math> for the cases <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The throughput of high-priority patients versus the number of low-priority patients <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math> for the cases <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>; (<b>b</b>) The throughput of low-priority patients versus the number of low-priority patients <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math> for the cases <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mo> </mo> <mi>r</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>.</p>
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<p>Channel utilization versus the number of low-priority patients.</p>
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<p>Fairness index vs. collision probability <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo> </mo> </mrow> </semantics></math> with different numbers of low-priority patients <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
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19 pages, 2458 KiB  
Article
Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems
by Mulugeta Kassaw Tefera, Shengbing Zhang and Zengwang Jin
Entropy 2023, 25(3), 413; https://doi.org/10.3390/e25030413 - 24 Feb 2023
Cited by 8 | Viewed by 3466
Abstract
This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system [...] Read more.
This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive. Recently, model-free reinforcement learning (RL) techniques have become alternative approaches in wireless networks to solve non-convex and NP-hard optimization problems. In this paper, we study a deep Q-learning (DQL)-based approach to address the optimization of transmit power control for users in multi-cell interference networks. In particular, we have applied a DQL algorithm for resource allocation to maximize the overall system throughput subject to the maximum power and SINR constraints in a flat frequency channel. We first formulate the optimization problem as a non-cooperative game model, where the multiple BSs compete for spectral efficiencies by improving their achievable utility functions while ensuring the quality of service (QoS) requirements to the corresponding receivers. Then, we develop a DRL-based resource allocation model to maximize the system throughput while satisfying the power and spectral efficiency requirements. In this setting, we define the state-action spaces and the reward function to explore the possible actions and learning outcomes. The numerical simulations demonstrate that the proposed DQL-based scheme outperforms the traditional model-based solution. Full article
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<p>An illustration of downlink resource allocation for multi-cell and multiple user systems.</p>
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<p>Reinforcement learning for multi-cell OFDMA systems.</p>
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<p>Sum-rate vs. transmit power budget for different schemes.</p>
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<p>Sum rate vs. power budget ignoring QoS.</p>
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<p>The average sum rate vs. the number of user pairs.</p>
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10 pages, 2034 KiB  
Communication
Efficient Space–Time Signal Processing Scheme of Frequency Synchronization and Positioning for Sensor Networks
by Yung-Yi Wang and Jian-Rung Huang
Sensors 2023, 23(4), 2115; https://doi.org/10.3390/s23042115 - 13 Feb 2023
Cited by 2 | Viewed by 1237
Abstract
The orthogonal frequency division multiple access (OFDMA) technique has been widely employed in sensor networks as the data modulation scheme. This study presents a one-dimensional (1D) space–time signal processing scheme for the joint estimation of direction of arrival (DOA) and carrier frequency offsets [...] Read more.
The orthogonal frequency division multiple access (OFDMA) technique has been widely employed in sensor networks as the data modulation scheme. This study presents a one-dimensional (1D) space–time signal processing scheme for the joint estimation of direction of arrival (DOA) and carrier frequency offsets (CFOs) in OFDMA uplink systems. The proposed approach, initiated by a one-dimensional ESPRIT algorithm, involves estimating the DOAs of the received signal to identify subscriber positions. Spatial beamformers are then used to suppress multiple access interference and separate each subscriber’s signal from the received signal. The outputs of the spatial beamformer are decimated to estimate the CFO of each subscriber. Compared with conventional two-dimensional parameter estimation algorithms, the proposed one-dimensional algorithm has a higher estimation accuracy and significantly lower computational complexity. Full article
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<p>Comparisons of the RMSEs of the proposed algorithm: (<b>a</b>) the RMSEs of the DOA estimates; (<b>b</b>) the RMSEs of the CFO estimates.</p>
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<p>Comparisons of the SERs.</p>
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<p>Comparisons of the computational complexities.</p>
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13 pages, 2989 KiB  
Article
Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
by Omer Mohammed Khodayer Al-Dulaimi, Aymen Mohammed Khodayer Al-Dulaimi, Maiduc Osiceanu Alexandra and Mohammed Khodayer Hassan Al-Dulaimi
Sensors 2023, 23(3), 1705; https://doi.org/10.3390/s23031705 - 3 Feb 2023
Cited by 15 | Viewed by 4491
Abstract
The purpose of this paper is to provide a high-level overview of the most important non-orthogonal multiple access (NOMA) protocols in 5G and 6G networks that incorporate code division within the context of 3GPP standardization. The article’s objective is also to look into [...] Read more.
The purpose of this paper is to provide a high-level overview of the most important non-orthogonal multiple access (NOMA) protocols in 5G and 6G networks that incorporate code division within the context of 3GPP standardization. The article’s objective is also to look into and compare the various strategies that have been proposed as a solution to the issue of resource distribution to achieve high performance. Many different NOMA plans for 5G and 6G systems have been suggested by a multitude of businesses. NOMA is currently developing in two primary directions: one of them is with power division, and the other is with code division. During the process of standardization carried out by the 3GPP, the attention of the developers was concentrated in the second direction for the application of NOMA schemes in 5G and 6G systems. Hardware communication, also known as D2D communication, performs a significant role in the process of communication between devices. This will increase the efficiency with which network resources are utilized. Devices are now able to interact directly with one another, avoiding the need for transmission nodes. It also serves as one of the approaches to the problem of limited network coverage, which can be improved by utilizing D2D, and as a result fees and energy can be reduced. Increasing the size of the network is one way to achieve this goal, the explained of NOMA technology as well as its primary benefits in wireless technology. The most common variants of code division NOMA and the characteristics of those variants are discussed, as well as the opportunities and challenges associated with implementing those variants. NOMA protocols allow continuous expansion of wireless communication networks, i.e., 5G and 6G, which leads to enhanced performance of the networks. Full article
(This article belongs to the Special Issue mmWave and 5G Beyond for Vehicular Wireless Communications)
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<p>NOMA system transmitting side structure based on 5G standard.</p>
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<p>Illustration of an LDS–CDMA system’s basic architecture.</p>
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<p>A schematic of the sending side of a NOMA system that shows processing at the bit level.</p>
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<p>A schematic of a 5G-modulated NOMA transmission system.</p>
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<p>Aspect of the WSMA scheme concerned with structural transmission.</p>
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<p>Sparse pattern and modified 5G modulation NOMA system structure.</p>
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<p>Design of a NOMA system’s character-level hybrid processing.</p>
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<p>NOMA system structure with interleaving and zero elements.</p>
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