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On the Performance of Cellular Networks with Adaptive Modulation and Energy Harvesting : A Stochastic Geometry Approach

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The performance of ultra-dense cellular networks considering both adaptive discrete modulation (ADM) and energy harvesting (EH) is investigated. Particularly, mobile users (MUs) are charged its battery from all ambient radio frequency (RF) signals. Based on the amount of harvested energy as well as the channel conditions, MU will actively choose an appropriate modulation scheme that not only maximizes the rate but also satisfies the quality-of-service (QoS). Moreover, we consider the spatial-temporal correlation at the signal-to-interference-plus-noise ratios (SINRs) of base stations (BSs) which are totally different from work in the literature. Several important metrics are investigated such as, occurrence probabilities of different modulation schemes (Poc), coverage probability (Pcov), and achievable spectral efficiency (ASE). Finally, the results highlight the superiority of the proposed scheme compared to the conventional fixed modulation
Rocznik
Tom
Strony
115--120
Opis fizyczny
Bibliogr. 37 poz., wykr.
Twórcy
autor
  • Post and Telecommunications Institute of Technology Ho Chi Minh City, Vietnam
  • Post and Telecommunications Institute of Technology Ho Chi Minh City, Vietnam
  • Ho Chi Minh City University of Transport Ho Chi Minh City, Vietnam
autor
  • Ton Duc Thang University Ho Chi Minh City, Vietnam
Bibliografia
  • [1] Ericsson Mobility Report, Ericsson, 2017.
  • [2] N. Q. Sang et. al., “Cognitive multihop cluster-based transmission under interference constraint,” 2014 IEEE ISCE, 2014, pp. 1-3.
  • [3] L. -T. Tu et. al., “Performance Evaluation of Incremental Relaying in Underlay Cognitive Radio Networks with Imperfect CSI,” 2020 IEEE ICCE, 2021, pp. 472-477.
  • [4] T. N. Nguyen et. al., “Outage Performance of Satellite Terrestrial Full-Duplex Relaying Networks with Co-Channel Interference,” IEEE Wireless Commun. Lett., vol. 11, no. 7, pp. 1478-1482.
  • [5] J. Song et. al., “On the feasibility of interference alignment in ultra-dense millimeter-wave cellular networks,” IEEE Asilomar 2016, 2016, pp. 1176-1180.
  • [6] M. S. Alouini et. al., “Adaptive modulation over nakagami fading channels,” Wireless Pers. Commun., vol. 13, pp. 119–143, May. 2000.
  • [7] J. Huang, C.-C. Xing, and C. Wang, ”Simultaneous wireless information and power transfer: Technologies, applications, and research challenges,” IEEE Commun. Magazine, vol. 55, pp. 26–32, Nov. 2017.
  • [8] P. N. Son et. al., “Short packet communications for cooperative UAV-NOMA-based IoT systems with SIC imperfections,” Compt. Netw., 2022.
  • [9] L.-T. Tu et. al., “A New Closed-Form Expression of the Coverage Probability for Different QoS in LoRa Networks,” IEE ICC 2020.
  • [10] J. G. Andrews et. al., “A Tractable Approach to Coverage and Rate in Cellular Networks,” IEEE Trans. Commun., vol. 59, no. 11, 2011.
  • [11] T. L. Thanh et. al., “Capacity analysis of multi-hop decode-and-forward over Rician fading channels,” IEEE ComManTel 2014, 2014, pp. 134-139.
  • [12] T. T. Duy et. al., “Performance Enhancement for Multihop Cognitive DF and AF Relaying Protocols under Joint Impact of Interference and Hardware Noises: NOMA for Primary Network and Best-Path Selection for Secondary Network,” Wireless Commun. Mobile Comput., vol. 2021.
  • [13] L. -T. Tu et. al., “Broadcasting in Cognitive Radio Networks: A Fountain Codes Approach,” IEEE Trans. Veh. Techno., Early Access, 2022.
  • [14] T. L. Thanh et. al., “10-Gb/s wireless signal transmission over a seamless IM/DD fiber-MMW system at 92.5 GHz,” IEEE ICC 2015, 2015, pp. 1364-1369.
  • [15] C. H. Duc et. al., “Performance Evaluation of UAV-Based NOMA Networks with Hardware Impairment,” Electronics, vol. 11, no. 1, p. 94, Dec. 2021.
  • [16] T. T. Lam et. al., “On the Spectral Efficiency of LoRa Networks: Performance Analysis, Trends and Optimal Points of Operation,” IEEE Trans. Commun., vol. 70, no. 4, pp. 2788-2804, April 2022
  • [17] P. N. Son et. al., “Performance analysis of underlay cooperative cognitive full-duplex networks with energy-harvesting relay”, Compt. Netw., vol. 122, p. 9-19, Jun. 2018.
  • [18] L.-T. Tu et. al., “System-level analysis of swipt mimo cellular networks,” IEEE Commun. Lett., vol. 20, pp. 2011–2014, Oct. 2016.
  • [19] L.-T. Tu et. al., “System-level analysis of receiver diversity in swipt-enabled cellular networks,” IEEE/KICS J. Commun. Netw., vol. 18, pp.926–937, Dec. 2016.
  • [20] T.-T. Nguyen et. al., “Evaluation of Full-Duplex SWIPT Cooperative NOMA-Based IoT Relay Networks over Nakagami-m Fading Channels,” Sensors, vol. 22, no. 5, p. 1974, Mar. 2022.
  • [21] T. N. Nguyen et. al., “Partial and Full Relay Selection Algorithms for AF Multi-Relay Full-Duplex Networks With Self-Energy Recycling in Non-Identically Distributed Fading Channels,” IEEE Trans. Veh. Techno., vol. 71, no. 6, pp. 6173-6188, Jun. 2022.
  • [22] T. T. Lam et. al., “Analysis of millimeter wave cellular networks with simultaneous wireless information and power transfer,” IEEE SigTelCom 2017, 2017, pp. 39-43.
  • [23] M. Li et. al., “Performance analysis of adaptive multicast streaming services in wireless cellular networks,” IEEE Trans. Mobile Comput., vol. 18, pp. 2616–2630, Nov. 2019.
  • [24] X. Qiu et. al., “On the performance of adaptive modulation in cellular systems,” IEEE Trans. Commun., vol. 47, pp. 884–895, Jun. 1999.
  • [25] T. T. Lam et. al., “On the Energy Efficiency of Heterogeneous Cellular Networks With Renewable Energy Sources - A Stochastic Geometry Framework,” IEEE Trans. Wireless Commun., 2020.
  • [26] F. Baccelli and B. Blaszczyszyn, Stochastic Geometry and Wireless Networks, Part I: Theory, Now Publishers, Sep. 2009.
  • [27] M. Di Renzo et. al., “System-Level Modeling and Optimization of the Energy Efficiency in Cellular Networks – A Stochastic Geometry Framework”, IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2539-2556, Apr. 2018.
  • [28] J. Song et. al., “Bounded Path-Loss Model for UAV-to-UAV Communications,” ICWMC 2021, pp. 20-21.
  • [29] H. H. Yang et. al., “Spatio-Temporal Analysis for SINR Coverage in Small Cell Networks,” IEEE Trans. Commun., vol. 67, no. 8, pp. 5520-5531, Aug. 2019.
  • [30] P. T. Tin et. al., “Rateless Codes-Based Secure Communication Employing Transmit Antenna Selection and Harvest-To-Jam under Joint Effect of Interference and Hardware Impairments,” Entropy, vol. 21, no. 7, p.700, Jul. 2019.
  • [31] T. -T. T. Dao et. al., “Performance Evaluation of Downlink Multiple Users NOMA-Enable UAV-Aided Communication Systems Over Nakagami-m Fading Environments,” IEEE Access, vol. 9, pp. 151641-151653, 2021.
  • [32] T. V. Chien et. al., “Coverage Probability and Ergodic Capacity of Intelligent Reflecting Surface-Enhanced Communication Systems,” IEEE Commun. Lett., vol. 25, no. 1, pp. 69-73, Jan. 2021.
  • [33] T. V. Chien et. al., “Outage Probability Analysis of IRS-Assisted Systems Under Spatially Correlated Channels,” IEEE Wireless Commun. Lett., vol. 10, no. 8, pp. 1815-1819, Aug. 2021.
  • [34] L. -T. Tu et. al., “Energy Efficiency Analysis of LoRa Networks,” IEEE Wireless Commun. Lett., vol. 10, no. 9, pp. 1881-1885, Sept. 2021.
  • [35] L. -T. Tu et. al., “Coverage Probability and Spectral Efficiency Analysis of Multi-Gateway Downlink LoRa Networks,” IEEE ICC 2022 2022, pp. 1-6.
  • [36] L. -T. Tu et. al., “Energy Efficiency Optimization in LoRa Networks - A Deep Learning Approach,” IEEE Trans. Intel. Transport. Syst., Early Access, 2022.
  • [37] A. Zappone et. al., “Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks for wireless system optimization,” IEEE Veh. Technol. Mag., vol. 14, pp. 60–69, Sep. 2019.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1248cc52-4ae3-4674-acc0-81d01eaa26c5
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