Throughput Performance Research Articles

VoI-based Situation-Aware Routing Protocol for Non-linear Underwater Communication Networks

One of the main challenges for underwater applications, such as environmental monitoring and disaster management, is achieving efficient data transmission in environments where conditions change rapidly, and resources need for data transport are scarce. The capability of evaluating the Value of information (VoI) enables us to assess these problems by proposing a Value of Information-based Situation-Aware Non-Linear Routing (VoI SANLR/VoI SANL) method. It aims to deal with critical event scenarios using BDI (Belief-Desire-Intention) logic criteria and prioritizing the timely uploading of data-driven information towards the destination. SANLR of VoI is developed to reduce energy consumption, end-to-end latency, jitter, and improve Packet Delivery Ratio (PDR) in underwater communication networks. VoI SANLR introduces principles of priority-based methods and intends to address challenges in terms of underwater environment such as varying channel conditions, lack energy resources, and real-time decision requirements by using SANLR. Energy optimization analysis reveals consistent outperformance, achieving a remarkable 95% reduction in energy consumption compared to other techniques. Low latency is maintained, ranging from 2.5 to 0.5 seconds, showcasing enhanced efficiency and scalability. VoI SANLR demonstrates exceptional performance in both throughput and jitter. It achieves the highest data transfer rates, ranging from 100 kbps to 110 kbps, indicating outstanding efficiency. Additionally, the jitter remains consistently low, between 1.8 ms and 2 ms, ensuring minimal delay variability and improved communication stability. PDR consistently surpasses other techniques, reaching a maximum of 99%. Additionally, network lifetime analysis demonstrates VoI SANLR's superiority, exhibiting the highest network lifetime at each node and a significant 31.25% improvement at Node 100 compared to other methods.

Secure Vehicle-to-Vehicle Communication using Routing Protocol based on Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Bayesian Learning based Rate Adaptation in IEEE 802.11ax WLANs with a Target PER

The optimal modulation and coding scheme (MCS) selection in wireless transmission depends on the dynamically evolving channel state. Hence, Rate adaptation in a wireless channel relies on periodically reported channel quality indicator (CQI) values to select the optimal MCS. The latest 802.11ax, with a HE-sounding protocol, supports an explicit feedback mechanism where the client sends back a transformed estimate of the channel state information (CSI) in the HE CQI Report field. When generated more frequently, these reports can be expensive as they introduce unnecessary computational and protocol overhead. Also, the CSI feedback information is quantized, delayed, and noisy. To reduce the frequent CSI feedback (receiver to the transmitter) overhead, in our work, we obtain CSI statistically at the transmitter through Bayesian Learning (BL). Further, we propose a Bayesian Learning based Rate Adaptation (BLbRA) scheme at the transmitter. BLbRA throughput performance is consistent even with reduced feedback overhead. BLbRA can be implemented without any change in the standard frame format, and therefore, it is suitable for practical deployment.

Comparative Analysis of Nextcloud and Owncloud Performance as Infrastructure as a Service (IaaS) Based Cloud Storage

Physical data storage using flash drives or local computer storage continues to be used for document archiving in database laboratories. Archived documents include laboratory inventory modules, practical course modules, practitioners' attendance cards, and students' task and response files. Utilizing cloud storage has emerged as a solution to these issues, enabling data to be easily accessed from anywhere via an internet connection. Numerous cloud storage applications now feature advanced functionalities; however, each cloud storage company varies regarding access speed, storage capacity, security, and costs. This study aims to analyze the performance of two cloud storage applications, Nextcloud and Owncloud, using throughput as a parameter. Test results demonstrated that Nextcloud's performance in file upload throughput was higher at 1065 bps compared to Owncloud's 850 bps. Similarly, in file download throughput, Nextcloud exceeded Owncloud's throughput of 2960 bps against 2564 bps. These results indicate that Nextcloud's performance surpasses Owncloud's in both file upload and download operations

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Meta surface Assisted Open radio access networks

The paradigm of the open radio access networks (O-RAN) seeks to carry intelligence and openness (multi-vendors) to the conventional proprietary and closed radio access networks (RAN) schemes and deliver performance enhancement, cost-efficiency, and flexibility, in both the network's operation and deployment. On the other hand, Reconfigurable Intelligent Surfaces (RIS) are suggested for future networks due to their effectiveness in terms of cost and energy consumption. However, due to the fast time-varying feature of the dense wireless systems, it becomes difficult to allow optimal user association and beamforming in terms of signalling overheads and processing in RIS assisted RANs with the limited capacity of the fronthaul. Therefore, the objective of this study is to attain a trade-off between the costs (signalling overhead/complexity) and the throughput performance. In other words, the study sets the challenges of the RIS aided O-RAN technology regarding the joint selection of user’s equipment (UEs)/open radio unit (O-RU)-RIS pairs and the designing of beamforming at the O-RU/RIS and open distributed unit (O-DU). From this point, to addressing the designing challenges, this work suggests a simple and potential beamforming strategy in RIS aided O-RAN architecture taking into account the specification of the interfaces between different O-RAN units to split opportunities between the radio and distributing units. In specific, a channel-gain-based selection of UEs/O-RU-RIS pairs joint with duality theory (DT) and transform of quadratic function (TQF) algorithm (namely DT_TQF) is proposed. Firstly, the non-convex optimization problem is relaxed via duality and transform of quadratic functions, and then an iterative approach is carried out for the active and passive beamformers via a simple alternating optimization approach. This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. Numerical simulation results justify the effectiveness of the algorithm for different systems' parameter settings and validate the important of installing RIS. For example, for a certain environment, the performance gain is about 52.9 % in comparison to the classic null-steering/random phase shifter scheme.

ZeROf-Offload: forward-gradient scheme for efficient full parameter fine-tuning of billion-scale language models

Abstract In large language models (LLMs), full-parameter fine-tuning is crucial for task-specific adaptation. Traditionally, this relies on deep learning training frameworks utilizing the back-propagation scheme. However, this scheme presents inherent issues, e.g. activation memory bottlenecks and backward locking, which limit the efficient computational resource usage. In this work, we propose the design and analysis of ZeROf-Offload, an innovative fine-tuning framework that adapts the forward-gradient scheme. This framework adopts a unique forward-gradient-oriented CPU offload strategy, enabling fine-tuning of billion-scale LLMs solely in the forward phase and enhancing computational efficiency. Empirical evaluations reveal the advantage of eliminating the backward phase in fine-tuning. ZeROf-Offload achieves134 TFlops/GPU for models with over 130 billion parameters on a single DGX-A100 node, outperforming DeepSpeed’s ZeRO-Offload, which achieves 102 TFlops/GPU for models with up to 53.7 billion parameters, the largest size manageable within GPU memory limitations. Furthermore, we have expanded ZeROf-Offload for multi-DGX-A100 environments with integrated 3D parallelism, achieving near-linear speedup across up to 128 GPUs and the token throughput by 1.4x and 1.5x, respectively. The experimental results demonstrate that the proposed ZeROf-Offload has achieved the highest throughput performance compared to all examined state-of-the-art frameworks.

A novel sharding algorithm based on TOPSIS method for blockchain systems

PurposePerformance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.Design/methodology/approachThis paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.FindingsThe effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.Originality/valueThis study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

The application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.

Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Hardware Implementation of a 2D Chaotic Map-Based Audio Encryption System Using S-Box

This paper presents a hardware-based audio encryption system using a 2D chaotic map and dynamic S-box design implemented on an Artix-7 FPGA platform. Three distinct chaotic maps—logistic–fraction (2D-LF), logistic–sine (2D-LS), and fraction–sine (2D-FS)—were investigated and implemented on an FPGA. The 2D-LF map was employed in the encryption system for its throughput and power efficiency performance. The proposed encryption system benefits from the randomness of chaotic sequences for block permutation and S-box substitution to enhance the diffusion and confusion properties of the encrypted speech signal. The system’s encryption strength is validated through performance evaluations, using the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficients, and NIST randomness tests, which confirm the unpredictability of the encrypted speech signal. The hardware implementation results show a throughput of 2880 Mbps and power consumption of 0.13 W.

Scheduling of e-commerce packaging machines: blocking machines and their impact on the performance–waste tradeoff

AbstractTo streamline their fulfillment processes, many e-commerce retailers today use automated packaging machines for their outbound parcels. An important performance–waste tradeoff is associated with these machines: To reduce packaging waste when handling different sized goods, packaging machines should be able to handle different carton sizes. However, more carton sizes lead to a more involved scheduling process, so that the throughput performance deteriorates (and vice versa). To investigate this tradeoff, this paper develops scheduling procedures for a specific type of packaging machine, called blocking machines. These packaging machines provide multiple back-to-back packaging devices, each continuously processing a dedicated carton size, but blocking each other whenever incoming goods are not properly ordered according to carton sizes on the infeed conveyor. To reduce the resulting throughput loss, we derive various scheduling problems for optimizing the inflow of goods, provide a thorough analysis of the computational complexity, and derive an exact dynamic programming approach that is polynomial in the number of orders to be packed. This allows us to solve even large real-world instances to proven optimality with which we can analyze the performance–waste tradeoff of blocking machines.

Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

Enhancing TCP Airtime Fairness through Precise Computation for Upload and Download Flows in WiFi Networks

Airtime fairness has emerged as a key approach to enhancing wireless throughput performance. However, existing research often overlooks the precise calculation of airtime, particularly in relation to TCP acknowledgments. This paper introduces a novel method, implemented on the access point side, for accurately calculating the airtime of TCP and UDP flows. Building on this, we propose a QoS-based scheduling algorithm designed to improve fairness between upload and download traffic. The effectiveness of the algorithm is validated through experiments that accurately measure both throughput and airtime for upload and download traffic.

Implementation of Load Balancing Using the PCC (Per Connection Classifier) Method on Computer Networks to Improve Responsiveness

Dependence on a single ISP can lead to the risk of internet disconnection, necessitating a solution to enhance network performance and responsiveness. This research explores the implementation of load balancing using the Per Connection Classifier (PCC) method to improve the responsiveness of computer networks utilizing three ISPs (Internet Service Providers). The test results indicate that the PCC method across the three ISPs can increase throughput by 9.32 Mbps, reduce delay by 43.54 ms, and decrease jitter by 74.17 ms while packet loss remains stable at 0.0%. Additionally, the recursive gateway failover technique in PCC load balancing demonstrates a significant increase in throughput up to 83.3 Mbps, a reduction in delay by 0.27 ms, and a reduction in jitter by 1.15 ms. Packet loss remains stable at 0.0% under varying ISP conditions. Moreover, a comparison between the PCC and PBR methods with the least connection algorithm shows that both methods effectively enhance network responsiveness, with the PCC method exhibiting superior performance in throughput.

Enhanced seagull optimization for enhanced accuracy in CUDA-accelerated Levenberg–Marquardt backpropagation neural networks for earthquake forecasting

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg–Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model’s computational speed. The experimental results demonstrate that the ISOA-LM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network’s hyperparameters, leading to more precise earthquake predictions. Additionally, the model’s computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems.

Impact assessment of blockchain-based improved PBFT consensus mechanism for improving supply chain efficiency and security

In modern supply chain management, it is crucial to be able to track the flow of goods in real-time and maintain data integrity. Research focuses on the transparency, security, and efficiency improvements brought about by blockchain technology, and how these improvements can help address information asymmetry issues in supply chain management and improve overall operational efficiency. At the same time, the study explored the working principle of the practical Byzantine fault-tolerant consensus mechanism, proposed relevant improvement measures, and analyzed how these improvements can be effectively applied in the scenario of supply chain information sharing to improve the stability and efficiency of the overall system. The research results show that the overall latency of improved PBFT is maintained at the millisecond level, while the latency of traditional PBFT can reach several seconds or exceed 10 seconds. In terms of CPU resource consumption, the demand for improving PBFT only shows linear growth, while the demand for traditional PBFT increases sharply. In addition, improving PBFT also has significant advantages in communication costs. In an 8-node network, the communication volume of improved PBFT is only 11.27 KB, while traditional PBFT is 43.67 KB. In terms of throughput and latency performance, the improved PBFT can handle over 16000 requests per second in a 16 node network, significantly better than the traditional PBFT’s approximately 8000 requests. Meanwhile, at 64 nodes, the improved version has a latency of 301.7 milliseconds, much lower than the traditional version’s 510.2 milliseconds. These improvements demonstrate the advantages of improving PBFT in enhancing supply chain transparency, security, and efficiency, providing effective technical support for the digitization and intelligence of supply chain management. We hope that research can promote the digitization and intelligence of supply chain management, and facilitate the efficient operation and optimization of the supply chain.

Analytical model and swapping policy assessment of a vertical lift module – buffer integrated storage system

To meet the rising demands of global trade and e-commerce, efficient warehousing relies on integrated and cooperative material handling systems. This paper investigates the extension of Vertical Lift Module (VLM) storage capability with a Buffer System and assesses the impact of this integration on performance. We developed an analytical model to calculate the expected dual command cycles, forming the basis for evaluating the VLM – Buffer integrated storage system’s performance. Our research emphasises minimising unnecessary swaps between the VLM and the Buffer System to enhance throughput performance. We introduce the Look Ahead Strategy (LAS) to minimise inter-system swaps and develop a Binary Integer Program (BIP) to benchmark its performance. The results indicate that LAS performs on par with BIP, due to its ability to consider product popularity during the final selection of the outbound swapping tote. Through a comprehensive analysis of the analytical model with an empirical correction, utilising Pareto-based order sequences, the results show deviations of less than 1% on average, affirming the analytical model’s accuracy. Our research provides insights on using the VLM-Buffer integrated storage system, emphasising efficient tote swapping policies like LAS for enhanced warehouse operations, and allows managers to assess system performance through scenario-based analyses.

Performance Evaluation of Non-Lambertian SLIPT for 6G Visible Light Communication Systems

Visible light communication (VLC) has emerged as one promising candidate technique to improve the throughput performance in future sixth-generation (6G) mobile communication networks. Due to the limited battery capacity of VLC systems, light energy harvesting has been proposed and incorporated for achieving the simultaneous lightwave information and power transfer (SLIPT) function and for improving the overall energy efficiency. Nevertheless, almost all reported works are limited to SLIPT scenarios adopting a basic and well-discussed Lambertian optical transmitter, which definitely cannot characterize the potential and essential scenarios employing distinctive non-Lambertian optical transmitters with various spatial beam characteristics. For addressing this issue, in this work, SLIPT based on a distinct non-Lambertian optical beam configuration is investigated, and for further enhancing the harvested energy and the achievable data rate, the relevant flexible optical beam configuration method is presented as well. The numerical results show that, for a typical receiver position, compared with about 1.14 mJ harvested energy and a 31.2 Mbps achievable data rate of the baseline Lambertian configuration, a harvested energy gain of up to 1.55 mJ and an achievable data rate gain of 21.1 Mbps can be achieved by the non-Lambertian SLIPT scheme explored here.

A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks.

Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network.