Conjugate Effects on Steady Laminar Natural Convection Heat Transfer in Vertical Eccentric Annuli

Combined conduction-free convection heat transfer in vertical eccentric annuli is numerically investigated using finite-difference technique. Numerical results are presented for a fluid of Prandtl number 0.7 in an annulus of radius ratio 0.5 and dimensionless eccentricity 0.5. The conjugation effect on the induced flow rate and the total heat absorbed in the annulus is presented for the case of one wall being isothermally heated while the other wall is kept at inlet fluid temperature. The conjugate effects are controlled by solid-fluid conductivity ratio, cylinder walls thickness and dimensionless channel height (i.e. Grashof number). Solid-fluid conductivity ratio is varied over a range that covers practical cases with commonly encountered inner and outer walls thickness. Values of conductivity ratio over which conjugate effect can be neglected have been obtained

Conjugate Effects on Steady Laminar Natural Convection Heat Transfer in Vertical Eccentric Annuli

Combined conduction-free convection heat transfer in vertical eccentric annuli is numerically investigated using finite-difference technique. Numerical results are presented for a fluid of Prandtl number 0.7 in an annulus of radius ratio 0.5 and dimensionless eccentricity 0.5. The conjugation effect on the induced flow rate and the total heat absorbed in the annulus is presented for the case of one wall being isothermally heated while the other wall is kept at inlet fluid temperature. The conjugate effects are controlled by solid-fluid conductivity ratio, cylinder walls thickness and dimensionless channel height (i.e. Grashof number). Solid-fluid conductivity ratio is varied over a range that covers practical cases with commonly encountered inner and outer walls thickness. Values of conductivity ratio over which conjugate effect can be neglected have been obtained

Optimal Power Allocation and Cooperative Relaying under Fuzzy Inference System (FIS) Based Downlink PD-NOMA

Optimal power allocation (PA) is a decisive part of the power domain non-orthogonal multiple access (PD-NOMA) technique. In PD-NOMA, users are served at the same time and using the same frequency band, but at differing power levels. In this paper, the optimization problem for PA is formulated with distance (d), signal-to-noise ratio (SNR), and foliage depth (df) constraints. A fuzzy inference system (FIS) addresses the optimization problem by allocating the optimal power factors (power levels) to each user in the vicinity of a 5G base-station (gNodeB). The proposed system incorporates a cooperative relaying technique at the near-user to assist the far-user facing signal degradation and greater path losses. A realistic 5G micro-cell is analyzed for downlink PD-NOMA where superposition coding (SC) is used at the transmitter side, a successive interference cancellation (SIC) scheme at the near-user, and a maximum ratio combining (MRC) technique at the far-user’s receiver, respectively. For both simple PD-NOMA and cooperative relaying PD-NOMA, the presented technique’s bit-error-rate (BER) performance is evaluated against various SNR values, and it is concluded that cooperative PD-NOMA outperforms simple PD-NOMA. By combining the presented FIS system with cooperation relaying, the proposed FIS method guarantees user fairness in PD-NOMA systems while also significantly improving performance

Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA

The power domain non-orthogonal multiple access (PD-NOMA) scheme has gained tremendous interest with the multiple access behavior for fifth-generation (5G) wireless communication. Although the overall performance is improved through accurate power distribution among users’ signals, it depends on the user clustering strategy. Moreover, the PD-NOMA communication is not completely secured due to its broadcast nature, which is still a major problem. This paper presents a novel low-complexity short code-based technique utilized by the registered users and the 5G base station (gNodeB) for communication. By doing so, the PD-NOMA scheme is made secure from unregistered users or eavesdroppers. We proposed a three-step user clustering strategy that selects the best cluster among all the possible clusters to improve the overall performance. The proposed clustering strategy achieves a low outage probability in PD-NOMA systems. Moreover, it uses a multi-user decode and forward cooperative relaying scheme with PD-NOMA (Cop-PD-NOMA) to increase the coverage range of the gNodeB. In the multi-user Cop-PD-NOMA, the strong users (near users) are used as relay stations to aid the weak users (far users) by the decode and forward (D&F) technique. The proposed work provides a secure PD-NOMA network and the most effective user clustering approach during validation. The bit-error-rate (BER) comparisons demonstrate that multi-user cooperation outperforms single-user cooperation in Cop-PD-NOMA communication

Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA

The power domain non-orthogonal multiple access (PD-NOMA) scheme has gained tremendous interest with the multiple access behavior for fifth-generation (5G) wireless communication. Although the overall performance is improved through accurate power distribution among users’ signals, it depends on the user clustering strategy. Moreover, the PD-NOMA communication is not completely secured due to its broadcast nature, which is still a major problem. This paper presents a novel low-complexity short code-based technique utilized by the registered users and the 5G base station (gNodeB) for communication. By doing so, the PD-NOMA scheme is made secure from unregistered users or eavesdroppers. We proposed a three-step user clustering strategy that selects the best cluster among all the possible clusters to improve the overall performance. The proposed clustering strategy achieves a low outage probability in PD-NOMA systems. Moreover, it uses a multi-user decode and forward cooperative relaying scheme with PD-NOMA (Cop-PD-NOMA) to increase the coverage range of the gNodeB. In the multi-user Cop-PD-NOMA, the strong users (near users) are used as relay stations to aid the weak users (far users) by the decode and forward (D&F) technique. The proposed work provides a secure PD-NOMA network and the most effective user clustering approach during validation. The bit-error-rate (BER) comparisons demonstrate that multi-user cooperation outperforms single-user cooperation in Cop-PD-NOMA communication

Optimal Power Allocation and Cooperative Relaying under Fuzzy Inference System (FIS) Based Downlink PD-NOMA

Optimal power allocation (PA) is a decisive part of the power domain non-orthogonal multiple access (PD-NOMA) technique. In PD-NOMA, users are served at the same time and using the same frequency band, but at differing power levels. In this paper, the optimization problem for PA is formulated with distance (d), signal-to-noise ratio (SNR), and foliage depth (df) constraints. A fuzzy inference system (FIS) addresses the optimization problem by allocating the optimal power factors (power levels) to each user in the vicinity of a 5G base-station (gNodeB). The proposed system incorporates a cooperative relaying technique at the near-user to assist the far-user facing signal degradation and greater path losses. A realistic 5G micro-cell is analyzed for downlink PD-NOMA where superposition coding (SC) is used at the transmitter side, a successive interference cancellation (SIC) scheme at the near-user, and a maximum ratio combining (MRC) technique at the far-user’s receiver, respectively. For both simple PD-NOMA and cooperative relaying PD-NOMA, the presented technique’s bit-error-rate (BER) performance is evaluated against various SNR values, and it is concluded that cooperative PD-NOMA outperforms simple PD-NOMA. By combining the presented FIS system with cooperation relaying, the proposed FIS method guarantees user fairness in PD-NOMA systems while also significantly improving performance

Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature

Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature

Sagnac Loop Based Sensing System for Intrusion Localization Using Machine Learning

Among all optical sensing techniques, the distributed Sagnac loop (SI) sensor has the advantage of being simple to implement with low cost. Most of the proposed techniques for using SI exploit the frequency null method for event localization. However, such a technique suffers from the low spectrum signal power, complicating event localization under environmental noise. In this work, event localization using time-domain instead of frequency null signals is achieved using machine learning (ML), which is increasingly being exploited in many science fields, including sensing applications. First, a training dataset that includes 200 events is generated over a 50 km effective sensing fiber. These time-domain signals are considered as features for training the ML algorithm. Then, the random forest (RF) ML algorithm is used to develop a model for event location prediction. The results show the capability of ML in predicting the event’s location with 55 m mean absolute error (MAE). Further, the percentage of test realizations with prediction error > 200 m is 0.7%. The sensing signal bandwidth is investigated, showing better performance results for sensing signals of larger bandwidths. Finally, the proposed model is validated experimentally. The results showed good accuracy with MAE < 100 m