Performance Evaluation of Spectrum Efficiency over Rayleigh Fading Channel in a Tough Channel Error Condition in Fifth Generation (5G) New Radio
Keywords:
Channel error condition, QAM-based OFDM signal model, Rayleigh model, spectral efficiency, 5G new radioAbstract
As mobile communication networks develop from generation to generation, spectral efficiency is still a crucial component that needs to be enhanced and optimized. The upcoming technologies of the fifth generation (5G) should take spectral efficiency into account. With proper resource utilization, the 5G new radio will still have the challenges of signal interference because as the data capacity increases the latency might be high. This is a trade-off which a good algorithm is meant to suppress. The major challenge in cellular systems is to improve spectral efficiency in very tough channel error conditions. This is because in a poor tough channel error condition, there are challenges in terms of connectivity, due to poor signal quality, low network capacity and efficiency, network instability, and an increase in drop calls. The proposed algorithm, Channel Quality Indicator-Maximum Likelihood Detection (CQI-MLD) shows how to evaluate interference on tough channel error conditions concerning Channel State Information (CSI) error variance. The system model was designed and implemented on MATLAB 2024a environment. The data were generated from MATLAB 2024a communication toolbox and program codes were written on MATLAB visual environment. The results were analyzed and simulated. The proposed algorithm measures the channel quality by the user equipment and matches this information with a channel quality indicator within a specified SNR interval. The channel parameters change as the proposed algorithm reacts to the physical channel condition. CQI-MLD-Spatial Filtering models were implemented and evaluated in MATLAB 2024a environment. The performance of the CQI-MLD-Spatial model was compared with three existing algorithms, namely OFDM, Link Adaptation, and High Order Quadrature Amplitude Modulation (QAM) algorithms. The result demonstrates that the CQI-MLD model can successfully sustain an improved Spectrum Efficiency (SE) even in a poor SNR condition. The simulated graph shows that CQI-MLD gives higher SE than the other three existing algorithms. For instance, at the SNR of 10 dB, a spectral efficiency (SE) of about 30 bits/s/HZ is obtained for the CQI-MLD model, whereas the SE obtained for the other existing algorithms is below 9 bits/s/Hz at the same SNR of 10 dB. This result validates the performance of the CQI-MLD model to be more spectrum efficient than the existing algorithms.
References
Y. M. Khattabi and S. A. Alkhawaldeh, “Performance Analysis of Spatial Modulation Under Rapidly Time-Varying Rayleigh Fading Channels,” in IEEE Access, vol. 7, pp. 110594-110604, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2934000
Y. Xin, P. Shi, X. Xia, and X. Fan, “Spectral efficiency analysis of multi‐cell multi‐user massive MIMO over channel aging,” IET Communications, vol. 14, no. 5, pp. 811–817, Mar. 2020, doi: https://doi.org/10.1049/iet-com.2019.1079
Muhammed Abdurrahman Hazar, Niyazi Odabasioglu, Tolga Ensari, Yusuf Kavurucu, and O. F. Sayan, “Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels,” Neural computing & applications, vol. 29, no. 9, pp. 351–360, May 2017, doi: https://doi.org/10.1007/s00521-017-3040-6
A. M. Hamed and R. K. Rao, “Bandwidth and power efficiency analysis of fading communication link,” 2016 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Montreal, QC, Canada, 2016, pp. 1-7, doi: https://doi.org/10.1109/SPECTS.2016.7570527
S. A. Alkhawaldeh and Y. M. Khattabi, “ABEP Evaluation of Spatial Modulation Communication Systems: Improved Analytical Framework,” in IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10991-11002, Nov. 2019, doi: https://doi.org/10.1109/TVT.2019.2942091
J. Wang et al., “Spectral Efficiency Improvement With 5G Technologies: Results From Field Tests,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 8, pp. 1867-1875, Aug. 2017, doi: https://doi.org/10.1109/JSAC.2017.2713498
K. -L. A. Yau, J. Qadir, C. Wu, M. A. Imran and M. H. Ling, “Cognition-Inspired 5G Cellular Networks: A Review and the Road Ahead,” in IEEE Access, vol. 6, pp. 35072-35090, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2849446
D. J. Jabbar, A. H. Al-Nakkash, and Shatha Kadhim Al_hummadi, “Performance Evaluation of 2G and 3G Networks in the City of Baghdad,” IOP Conference Series Materials Science and Engineering, 745, pp. 1–15, Feb. 2020, doi: https://doi.org/10.1088/1757-899x/745/1/012050
J. J. Popoola, D. Okhueleigbe, and I. Alimi, “Link Adaptation for Microwave Link using both MATLAB and Path-Loss Tool,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 4, no. 4, Dec. 2016, doi: https://doi.org/10.11591/ijeei.v4i4.245
N. Yoo, J.-H. Back, H.-Y. Choi, and K. Lee, “ML Symbol Detection for MIMO Systems in the Presence of Channel Estimation Errors,” KSII Transactions on Internet and Information Systems, vol. 10, no. 11, pp. 5305–5321, Nov. 2016, doi: https://doi.org/10.3837/tiis.2016.08.006
T. A. Kumar and L. Anjaneyulu, “Channel Estimation Techniques for Multicarrier OFDM 5G Wireless Communication Systems,” 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, 2020, pp. 98-101, doi: https://doi.org/10.1109/ICSET51301.2020.9265353
Saxena, H. Tullberg and J. Jaldén, “Model-Based Adaptive Modulation and Coding with Latent Thompson Sampling,” 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 2021, pp. 610-616, doi: https://doi.org/10.1109/PIMRC50174.2021.9569685
Behnam Akbarian and Saeed Ghazi-Maghrebi, “Interference cancellation for cell-edge users in massive MIMO-NOMA systems: a contemporary overview,” Telecommunication Systems, vol. 87, pp. 1033–1044, Oct. 2024, doi: https://doi.org/10.1007/s11235-024-01219-1
B. S. K. Reddy and B. Lakshmi, “Adaptive modulation and coding with channel state information in OFDM for WiMAX,” I.J. Image, Graphics Signal Process, vol. 1, pp. 61–69, Dec. 2014, doi: https://doi.org/10.5815/ijigsp.2015.01.08
V. Saxena, H. Tullberg and J. Jaldén, “Reinforcement Learning for Efficient and Tuning-Free Link Adaptation,” in IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 768-780, Feb. 2022, doi: https://doi.org/10.1109/TWC.2021.3098972
E. U. Ogbodo, D. G. Dorrell, and A. M. Abu-Mahfouz, “Radio Resource Allocation Improvements in Cognitive Radio Sensor Network for Smart Grid: Investigative Study and Solutions,” International Journal of Sensors Wireless Communications and Control, vol. 11, no. 6, pp. 666–688, July. 2021, doi: https://doi.org/10.2174/1872212115999201125123708
M. R. Palattella et al., “Internet of Things in the 5G Era: Enablers, Architecture, and Business Models,” in IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 510-527, March 2016, doi: https://doi.org/10.1109/JSAC.2016.2525418
G. Fodor, S. Fodor and M. Telek, “MU-MIMO Receiver Design and Performance Analysis in Time-Varying Rayleigh Fading,” in IEEE Transactions on Communications, vol. 70, no. 2, pp. 1214-1228, Feb. 2022, doi: https://doi.org/10.1109/TCOMM.2021.3126760
A. Zahedi, “Effective capacity and outage analysis using moment-generating function over Nakagami-m and Rayleigh fading channels in a cooperative communication system,” Annals of Telecommunications, vol. 75, no. 5–6, pp. 193–200, Nov. 2019, doi: https://doi.org/10.1007/s12243-019-00739-1
R. Tian, K. Senda, T. Ota, and H. Otsuka, “Transmission performance of OFDM-based 1024-QAM in multipath fading conditions,” IEICE Communications Express, vol. 7, no. 7, pp. 272–277, Jan. 2018, doi: https://doi.org/10.1587/comex.2018xbl0053
V. Lazzarini and J. Timoney, “Higher-Order Frequency Modulation Synthesis,” arXiv (Cornell University), Jan. 2023, doi: https://doi.org/10.48550/arxiv.2305.07909
