Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: CellFree Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio
目标：本文分析了无小区大规模多输入多输出（CFMM）的两个瓶颈，即先导污染（PC）和信道估计误差（CEE）。方法：CFMM 网络受 PC 的影响很大，PC 是瓶颈之一，会影响服务质量和信道估计的准确性。因此，为了解决这一问题，我们提出了先进的先导分配算法来减轻 PC，并通过深度学习辅助信道估计来降低 CFMM 系统的 CEE，从而最大限度地提高频谱效率（SE）。我们推导出了拟议系统可实现的上行和下行 SE 表达式，并与最小均方误差和最大比组合技术进行了比较。此外，还对不同天线配置的性能进行了评估。先进的先导分配算法与贪婪先导分配法和随机先导分配法进行了比较。比较得出了蜂窝大规模多输入多输出（MIMO）的性能。使用 MATLAB 软件评估了 CFMM 系统的性能。结果：就 SE 而言，拟议系统的 UL 和 DL 性能是采用 MMSE 和 MR 组合技术的传统 CFMM 的 3.2 倍。拟议系统的平均总频谱效率随着接入点（AP）数量的增加而提高。与不同天线配置的比较表明，在 400 个接入点配备单天线的情况下，只有信道条件良好的 UE 才能提高性能，但当每个接入点配备 4 根天线时，信道条件不利的 UE 也能获得更好的性能。事实证明，高级先导分配方案优于贪婪和随机先导分配技术。对于相同的蜂窝设置，拟议的 CFMM 系统比蜂窝大规模多输入多输出系统获得更高的 SE。新颖性：由于拟议的 CFMM 系统采用了先进的先导分配算法，每次只选择一个接入点，被选中的接入点以其全部接收功率为所需的 UE 服务，从而抑制了干扰，提高了 SE 性能。服务 AP 的选择考虑了 UE 与 AP 之间的距离，而不是使用大规模衰落系数，这是先导分配算法的独特之处。所提出的深度学习辅助信道估计方法能使实际信道与通过 MMSE 估计获得的信道估计值之间的均方误差（MSE）最小化，从而减少信道估计误差。因此，使用所提出的先进先导分配算法和深度学习辅助信道估计方法可提高 CFMM 系统的 SE 性能。关键词无小区大规模多输入多输出、先导污染、信道估计误差、最小均方误差、最大比率
{"title":"Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks","authors":"Swapnaja Deshpande, Mona Aggarwal, Pooja Sabherwal, Swaran Ahuja","doi":"10.17485/ijst/v17i5.2961","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2961","url":null,"abstract":"Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: CellFree Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.17485/ijst/v17i5.2701
Shamandeep Singh, Simranjit Singh, Bikrampal Kaur
Background: Optical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In Optical Burst Switching (OBS) networks, determining an adaptive burst size is a difficult task that must be performed efficiently during burst assembling. Methods: This research proposes a hybrid burst assembly algorithm that determines the optimal burst size during the burst creation time. The proposed algorithm uses the Transition Count Number (TCN) based method to maintain the optimal burst size when the incoming traffic is unpredictable. The efficiency of the proposed approach is investigated in terms of queuing delay, burst utilization, burst size, and burst size consistency. Findings: Three types of traffic variations (H = 0.5, H = 0.6, and H = 0.7) are imposed to evaluate the performance of the proposed burst assembly approach. As compared to the E-hybrid (time/length) strategy, the research outcomes demonstrate a 13.15% reduction in average queuing latency and a 21.26% improvement in average burst utilization. Novelty: A new burst assembly approach (hybrid burst assembly) has been proposed for OBS networks. Keywords: Burst assembly, Optical Burst Switching (OBS), burstification, burst consistency
{"title":"A Hybrid Burst Assembly Algorithm Based on Transition Count Number for OBS Network","authors":"Shamandeep Singh, Simranjit Singh, Bikrampal Kaur","doi":"10.17485/ijst/v17i5.2701","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2701","url":null,"abstract":"Background: Optical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In Optical Burst Switching (OBS) networks, determining an adaptive burst size is a difficult task that must be performed efficiently during burst assembling. Methods: This research proposes a hybrid burst assembly algorithm that determines the optimal burst size during the burst creation time. The proposed algorithm uses the Transition Count Number (TCN) based method to maintain the optimal burst size when the incoming traffic is unpredictable. The efficiency of the proposed approach is investigated in terms of queuing delay, burst utilization, burst size, and burst size consistency. Findings: Three types of traffic variations (H = 0.5, H = 0.6, and H = 0.7) are imposed to evaluate the performance of the proposed burst assembly approach. As compared to the E-hybrid (time/length) strategy, the research outcomes demonstrate a 13.15% reduction in average queuing latency and a 21.26% improvement in average burst utilization. Novelty: A new burst assembly approach (hybrid burst assembly) has been proposed for OBS networks. Keywords: Burst assembly, Optical Burst Switching (OBS), burstification, burst consistency","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.17485/ijst/v17i5.2020
Amit Kumar Mittal, Kirti Mathur
Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long ShortTerm Memory, Harris hawk's optimization
{"title":"An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System","authors":"Amit Kumar Mittal, Kirti Mathur","doi":"10.17485/ijst/v17i5.2020","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2020","url":null,"abstract":"Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long ShortTerm Memory, Harris hawk's optimization","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140474462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.17485/ijst/v17i5.2719
Chetan M Bobade, Swapnil B Mohod, S. K. Singh
Background: Dual Rotor Radial Flux Induction Motor (DRRFIM) is proposed in this study to improve the operating performance in a more concise way for several applications compared to the conventional Induction motor. Mathematical analyses are obtained from the modeling and analytical study of Double Rotor Induction Motors (DRIM) which are useful to determine the dynamical behavior of the motor in saturated and unsaturated conditions. Methods: In normal DRIM, the driving state flow equation is done by the state equations in q-d axis and Park's transformation. To develop and design such models, it is advised to use better optimization techniques adapted MATLAB2020/Simulink software for validation. Further, transient, and steady state performance of the DRRFIM is analyzed in the various scenarios with and without saturation in DRRFIM. Finding: The proposed research also compares conventional IM with optimization possibilities. In addition, a DRIM is being operated at different stages of load connectivity and the level of speed conditions is being further investigated in this work. The method proposed and implemented in this paper achieved the overall efficiency is 82% by optimizing the mention performance metrics like active and reactive power of machine, speed of motor and its torque performance. Novelty and Applications: Dynamical behavior of solving and optimizing the amplitude of unsaturated and saturated magnetic flux are of utmost importance. The study also presents a machine equipped with two rotors that uplift the performance of the induction motor. This machine has the potential to be utilized in industrially important applications as well as in vehicles that are driven by electricity. Keywords: Dual Rotor RadialFlux Induction Motor (DRRFIM), Saturation, Unsaturation, Mathematical Model, Inner and Outer Rotor
{"title":"Robust Analysis of Dual Rotor Radial Flux Induction Motor Used for Industrial Application","authors":"Chetan M Bobade, Swapnil B Mohod, S. K. Singh","doi":"10.17485/ijst/v17i5.2719","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2719","url":null,"abstract":"Background: Dual Rotor Radial Flux Induction Motor (DRRFIM) is proposed in this study to improve the operating performance in a more concise way for several applications compared to the conventional Induction motor. Mathematical analyses are obtained from the modeling and analytical study of Double Rotor Induction Motors (DRIM) which are useful to determine the dynamical behavior of the motor in saturated and unsaturated conditions. Methods: In normal DRIM, the driving state flow equation is done by the state equations in q-d axis and Park's transformation. To develop and design such models, it is advised to use better optimization techniques adapted MATLAB2020/Simulink software for validation. Further, transient, and steady state performance of the DRRFIM is analyzed in the various scenarios with and without saturation in DRRFIM. Finding: The proposed research also compares conventional IM with optimization possibilities. In addition, a DRIM is being operated at different stages of load connectivity and the level of speed conditions is being further investigated in this work. The method proposed and implemented in this paper achieved the overall efficiency is 82% by optimizing the mention performance metrics like active and reactive power of machine, speed of motor and its torque performance. Novelty and Applications: Dynamical behavior of solving and optimizing the amplitude of unsaturated and saturated magnetic flux are of utmost importance. The study also presents a machine equipped with two rotors that uplift the performance of the induction motor. This machine has the potential to be utilized in industrially important applications as well as in vehicles that are driven by electricity. Keywords: Dual Rotor RadialFlux Induction Motor (DRRFIM), Saturation, Unsaturation, Mathematical Model, Inner and Outer Rotor","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140479081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.17485/ijst/v17i3.2224
A. S. K. Nayani, C. A. Sai
{"title":"LTE and MMW 5G Integrated MIMO Antenna System","authors":"A. S. K. Nayani, C. A. Sai","doi":"10.17485/ijst/v17i3.2224","DOIUrl":"https://doi.org/10.17485/ijst/v17i3.2224","url":null,"abstract":"","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140497631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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