In this paper, we address the challenge of limited channel capacity of wireless fronthaul links in cloud radio access networks (CRANs). A novel architecture is proposed in which the base band unit (BBU) is equipped with a massive multiple-input multiple-output (MIMO) antenna array to communicate with a set of remote radio heads (RRHs) that serve multiple user equipments (UEs). Moreover, an optimal signal quantization strategy is also designed at the BBU to accommodate for the limited fronthaul capacity. To this end, a joint optimization of power allocation at the BBU, quantization noise covariance, and RRH beamforming vectors is formulated via maximizing the achievable sum-rate subject to power constraints at the BBU and RRHs. The resulting highly nonconvex problem is reformulated as a semidefinite relaxation (SDR) problem by using the difference of convex (DC) programming approach. It is also deduced analytically that the solution yielded by this SDR problem is always of rank one. Numerical results confirm the potential of the proposed system in improving the capacity of the CRAN systems with wireless fronthauling. In particular, the proposed design outperforms two proposed benchmark schemes.
{"title":"Joint Design of Fronthaul and Access Links in Massive MIMO Based CRANs","authors":"Umar Rashid, Faheem G. Awan","doi":"10.1002/dac.70012","DOIUrl":"https://doi.org/10.1002/dac.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we address the challenge of limited channel capacity of wireless fronthaul links in cloud radio access networks (CRANs). A novel architecture is proposed in which the base band unit (BBU) is equipped with a massive multiple-input multiple-output (MIMO) antenna array to communicate with a set of remote radio heads (RRHs) that serve multiple user equipments (UEs). Moreover, an optimal signal quantization strategy is also designed at the BBU to accommodate for the limited fronthaul capacity. To this end, a joint optimization of power allocation at the BBU, quantization noise covariance, and RRH beamforming vectors is formulated via maximizing the achievable sum-rate subject to power constraints at the BBU and RRHs. The resulting highly nonconvex problem is reformulated as a semidefinite relaxation (SDR) problem by using the difference of convex (DC) programming approach. It is also deduced analytically that the solution yielded by this SDR problem is always of rank one. Numerical results confirm the potential of the proposed system in improving the capacity of the CRAN systems with wireless fronthauling. In particular, the proposed design outperforms two proposed benchmark schemes.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, scarecrow-shaped antenna with a Rogers RT6002 substrate with a permittivity of 2.94 and a thickness of 1 mm is presented. It is operating from 3.5 to 12 GHz frequency band. The next generation of wireless communication networks will make extensive use of machine learning (ML). It is anticipated that the growth of various communication-based applications will improve coverage and spectrum efficiency when compared with traditional systems. A wide range of domains, including antennas, can benefit from the application of ML to generate solutions. Scarecrow-shaped antenna is optimized using machine learning algorithms decision tree, random forest, XGBoost regression, K-nearest neighbor (KNN), and light gradient boosting regression (LGBR). The antenna's return loss, gain, and directivity were predicted in this work. The KNN achieved the highest accuracy in the prediction of return loss. Hence, proposed antenna is suitable for flexible wireless communication systems, IoT, 5G, and 6G.
{"title":"Scarecrow-Shaped Antenna Optimization Using Machine Learning Algorithms","authors":"S. Bhavani, B. Raviteja, T. Shanmuganantham","doi":"10.1002/dac.70028","DOIUrl":"https://doi.org/10.1002/dac.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, scarecrow-shaped antenna with a Rogers RT6002 substrate with a permittivity of 2.94 and a thickness of 1 mm is presented. It is operating from 3.5 to 12 GHz frequency band. The next generation of wireless communication networks will make extensive use of machine learning (ML). It is anticipated that the growth of various communication-based applications will improve coverage and spectrum efficiency when compared with traditional systems. A wide range of domains, including antennas, can benefit from the application of ML to generate solutions. Scarecrow-shaped antenna is optimized using machine learning algorithms decision tree, random forest, XGBoost regression, K-nearest neighbor (KNN), and light gradient boosting regression (LGBR). The antenna's return loss, gain, and directivity were predicted in this work. The KNN achieved the highest accuracy in the prediction of return loss. Hence, proposed antenna is suitable for flexible wireless communication systems, IoT, 5G, and 6G.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimizing waveforms in radar and sonar systems is crucial for enhancing spectral efficiency and target detection capabilities, yet this process often faces challenges like computational complexity and convergence issues. This research introduces a novel method that leverages the frequency-domain exponential cross ambiguity function (FECAF) within multi-tone sinusoidal frequency modulated (MTSFM) waveforms, combined with the Conjugate Gradient Method with Efficient Line Search (CGM-ELS) for optimization. The optimization of MTSFM waveforms is achieved by combining the generalized integrated sidelobe level (GISL) and peak-to-mean power envelope ratio (PMEPR) metrics. The GISL metric controls the main lobe and sidelobe structure of the waveform's autocorrelation function (ACF), quantifying unwanted energy in sidelobes to find an optimal compromise. PMEPR ensures efficient operation of radar or sonar transmitters by minimizing energy variations in the waveform's envelope, which is crucial for peak power-limited systems. To optimize these metrics, the CGM-ELS algorithm is employed, ensuring efficient convergence through iterative adjustment of waveform parameters based on gradient information and penalty functions. The proposed method transforms the optimization problem into an unconstrained format, reducing computational complexity and improving convergence rates. Experimental results have shown significant enhancements in computational efficiency and convergence rate, demonstrating the effectiveness of the CGM-ELS algorithm in synthesizing waveforms with optimal ambiguity function characteristics for radar and sonar applications.
{"title":"Optimizing FECAF in MTSFM Waveforms Using Conjugate Gradient Descent With Efficient Line Search for Radar and Sonar Applications","authors":"G. Ravi Shankar Reddy, J. Pandu, C. H. Ashok Babu","doi":"10.1002/dac.70027","DOIUrl":"https://doi.org/10.1002/dac.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>Optimizing waveforms in radar and sonar systems is crucial for enhancing spectral efficiency and target detection capabilities, yet this process often faces challenges like computational complexity and convergence issues. This research introduces a novel method that leverages the frequency-domain exponential cross ambiguity function (FECAF) within multi-tone sinusoidal frequency modulated (MTSFM) waveforms, combined with the Conjugate Gradient Method with Efficient Line Search (CGM-ELS) for optimization. The optimization of MTSFM waveforms is achieved by combining the generalized integrated sidelobe level (GISL) and peak-to-mean power envelope ratio (PMEPR) metrics. The GISL metric controls the main lobe and sidelobe structure of the waveform's autocorrelation function (ACF), quantifying unwanted energy in sidelobes to find an optimal compromise. PMEPR ensures efficient operation of radar or sonar transmitters by minimizing energy variations in the waveform's envelope, which is crucial for peak power-limited systems. To optimize these metrics, the CGM-ELS algorithm is employed, ensuring efficient convergence through iterative adjustment of waveform parameters based on gradient information and penalty functions. The proposed method transforms the optimization problem into an unconstrained format, reducing computational complexity and improving convergence rates. Experimental results have shown significant enhancements in computational efficiency and convergence rate, demonstrating the effectiveness of the CGM-ELS algorithm in synthesizing waveforms with optimal ambiguity function characteristics for radar and sonar applications.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a quad-port multi-input multi-output antenna design specifically suited for fifth-generation wireless technology. The antenna geometry is characterized by a flower-shaped configuration, featuring five petals complemented by a circular slot, positioned in the ground plane to improve operational bandwidth. The effective realization of wide-band characteristics has been studied utilizing the analysis of characteristic modes. The design antenna demonstrates simulated performance parameters that cover the millimeter-wave frequency band from 25.6 to 32.4 GHz, delivering a maximum gain of 6.1 dBi and ensuring minimum port isolation of > 15 dB across all ports. In addition, a circular stub is purposefully positioned over the slot to facilitate circular polarization radiation behavior at 26.3 GHz, exhibiting an axial ratio bandwidth of 0.5 GHz (26–26.5 GHz). Further, the proposed antenna design is subjected to validation encompassing diversity metrics. The proposed antenna structure is successfully fabricated, and its performance is experimentally validated. A comprehensive comparative analysis is then conducted to evaluate its alignment with the simulated results.
{"title":"Circularly Polarized MIMO Antenna System for Millimeter Wave 5G Applications Using Characteristic Mode Theory","authors":"Sumon Modak, Umar Farooq, Anajaneyulu Lokam","doi":"10.1002/dac.70017","DOIUrl":"https://doi.org/10.1002/dac.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>This study introduces a quad-port multi-input multi-output antenna design specifically suited for fifth-generation wireless technology. The antenna geometry is characterized by a flower-shaped configuration, featuring five petals complemented by a circular slot, positioned in the ground plane to improve operational bandwidth. The effective realization of wide-band characteristics has been studied utilizing the analysis of characteristic modes. The design antenna demonstrates simulated performance parameters that cover the millimeter-wave frequency band from 25.6 to 32.4 GHz, delivering a maximum gain of 6.1 dBi and ensuring minimum port isolation of > 15 dB across all ports. In addition, a circular stub is purposefully positioned over the slot to facilitate circular polarization radiation behavior at 26.3 GHz, exhibiting an axial ratio bandwidth of 0.5 GHz (26–26.5 GHz). Further, the proposed antenna design is subjected to validation encompassing diversity metrics. The proposed antenna structure is successfully fabricated, and its performance is experimentally validated. A comprehensive comparative analysis is then conducted to evaluate its alignment with the simulated results.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile edge computing (MEC) is extensively utilized for supporting diverse mobile applications and the Internet of Things (IoT). One of MEC's prime operations is utilizing unmanned aerial vehicles (UAVs) included with the MEC servers for providing computational aids for offloaded tasks by mobile users in temporal hotspot regions or a few emerging situations like sports areas or environmental disaster regions. However, despite the various merits of UAVs executed with MEC servers, it is constrained by their insufficient sensible energy consumption and computational resources. Furthermore, owing to the complication of UAV-aided MEC systems, energy consumption optimizations and computation resource optimizations cannot be obtained better in conventional optimization schemes. In this research, the kookaburra jellyfish algorithm (KJA) is presented for task offloading in a UAV-enabled MEC network. The main objective is to enhance the efficiency of task offloading in UAV-enabled MEC networks by optimizing energy consumption, computational resources, and communication time using the KJA. Initially, the UAV-enabled MEC network model is simulated. Next, task computation is performed, and thereafter, task uploading is carried out. Then, task offloading is executed using KJA with consideration of multiobjective models, namely, energy consumption, communication time, and cost. Moreover, KJA is devised by integrating kookaburra optimization algorithm (KOA) with jellyfish search optimizer (JSO). Afterward, the task offloading process and data transmission are conducted. In addition, KJA obtained minimum energy, load, and time of 0.448 J, 0.122, and 1.036 s.
{"title":"KJA: Kookaburra Jellyfish Algorithm Based Task Offloading in UAV-Enabled Mobile Edge Computing Network","authors":"Anand R. Umarji, Dharamendra Chouhan","doi":"10.1002/dac.70007","DOIUrl":"https://doi.org/10.1002/dac.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile edge computing (MEC) is extensively utilized for supporting diverse mobile applications and the Internet of Things (IoT). One of MEC's prime operations is utilizing unmanned aerial vehicles (UAVs) included with the MEC servers for providing computational aids for offloaded tasks by mobile users in temporal hotspot regions or a few emerging situations like sports areas or environmental disaster regions. However, despite the various merits of UAVs executed with MEC servers, it is constrained by their insufficient sensible energy consumption and computational resources. Furthermore, owing to the complication of UAV-aided MEC systems, energy consumption optimizations and computation resource optimizations cannot be obtained better in conventional optimization schemes. In this research, the kookaburra jellyfish algorithm (KJA) is presented for task offloading in a UAV-enabled MEC network. The main objective is to enhance the efficiency of task offloading in UAV-enabled MEC networks by optimizing energy consumption, computational resources, and communication time using the KJA. Initially, the UAV-enabled MEC network model is simulated. Next, task computation is performed, and thereafter, task uploading is carried out. Then, task offloading is executed using KJA with consideration of multiobjective models, namely, energy consumption, communication time, and cost. Moreover, KJA is devised by integrating kookaburra optimization algorithm (KOA) with jellyfish search optimizer (JSO). Afterward, the task offloading process and data transmission are conducted. In addition, KJA obtained minimum energy, load, and time of 0.448 J, 0.122, and 1.036 s.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital filter multiple access passive optical network (DFMA-PON) is adopted in recent years as it offers the high bandwidth, elastic network slicing, low latency, fixed data rate, and massive end-user comparability. To overcome limited system capacity, low transmission rate and fiber fault in existing DFMA-PONs, a wheel architecture based full-duplex 8 × 200 Gbps DFMA-PON integrated system using fiber and free space optics (FSO) link is presented in this paper. Results depict that maximum FSO channel range can be attained up to 2600 m together with 50 km fiber range under weak-to-strong turbulent, haze, rain, fog, and snow conditions. Besides, faithful fiber reach of 200 km can be obtained with fixed 100 m FSO distance at receiver sensitivity of −17.2 dBm and 1.5 dB power penalty. System can support 230 numbers end units at high optical signal to noise ratio of 29–88 dB with 50–200 Gbps throughput, together with high split ratio of 256 to −3.2 dB high gain as well as 2 dB noise figure. In addition, the comparative analysis with the previous work reveals that the proposed architecture offers optimum results than existing PONs. This system helps to enhance the disaster resilience with high network security, reliability, and survivability of 5G based networks.
{"title":"8 × 200 Gbps Hybrid PON and Digital Filter Multiple Access Incorporating Fiber-FSO Link Impairments in a Wheel Architecture","authors":"Meet Kumari, Vivek Arya","doi":"10.1002/dac.70018","DOIUrl":"https://doi.org/10.1002/dac.70018","url":null,"abstract":"<div>\u0000 \u0000 <p>Digital filter multiple access passive optical network (DFMA-PON) is adopted in recent years as it offers the high bandwidth, elastic network slicing, low latency, fixed data rate, and massive end-user comparability. To overcome limited system capacity, low transmission rate and fiber fault in existing DFMA-PONs, a wheel architecture based full-duplex 8 × 200 Gbps DFMA-PON integrated system using fiber and free space optics (FSO) link is presented in this paper. Results depict that maximum FSO channel range can be attained up to 2600 m together with 50 km fiber range under weak-to-strong turbulent, haze, rain, fog, and snow conditions. Besides, faithful fiber reach of 200 km can be obtained with fixed 100 m FSO distance at receiver sensitivity of −17.2 dBm and 1.5 dB power penalty. System can support 230 numbers end units at high optical signal to noise ratio of 29–88 dB with 50–200 Gbps throughput, together with high split ratio of 256 to −3.2 dB high gain as well as 2 dB noise figure. In addition, the comparative analysis with the previous work reveals that the proposed architecture offers optimum results than existing PONs. This system helps to enhance the disaster resilience with high network security, reliability, and survivability of 5G based networks.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) has transformed vehicular ad hoc networks (VANETs), leading to the Internet of Vehicles (IOV). VANETs are wireless networks without fixed infrastructure, designed to improve traffic safety in real time, supporting intelligent transportation systems (ITS). Due to their unpredictable nature, VANETs face major challenges like frequent link failures, scalability, reliability, network layout issues, quality of service (QoS), and security, all of which are complex and difficult to solve (NP-hard problems). Traditional protocols are unsuitable for VANETs due to their unique properties. To accomplish the optimal number of clusters and achieve stability in VANETs within a dynamic environment, we propose a swarm-based metaheuristic algorithm called the rat swarm optimization (RSO) algorithm. The RSO algorithm employs a clustering technique to optimize the network performance and ensure efficient communication in VANETs. The RSO algorithm optimizes load based on node transmission range (Tx range) through effective resource utilization and coordination. RSO organizes the unstructured network into cluster structures and generates near-optimal clusters and CHs to reduce network randomness and maintain stability with lower communication costs. By keeping the number of clusters at an optimal level, the RSO algorithm enhances cluster lifetime and overall network performance. To assess the effectiveness and efficiency of the RSO algorithm, numerous experiments are performed by using various grid sizes, Tx ranges, and nodes in the network. The generated results demonstrate that the RSO algorithm stimulates 50.96%, 33.15%, 88.73%, and 96.70% optimal number of clusters when contrasted with the clustering algorithm–based on ant colony optimization (CACONET), moth flame clustering algorithm for IoV (MFCA-IoV), the whale optimization algorithm for clustering in vehicular ad hoc networks (WOACNET), and grasshoppers' optimization-based node clustering technique for VANETs (GOA) when the Tx range and nodes are taken into consideration. But, when the grid size is considered, the RSO generates 32.31%, 15.23%, 47.04%, and 58.33% optimal number of clusters when compared to cutting-edge algorithms. Hence, the quantitative results and the statistical representation show the proposed RSO algorithm's effectiveness over cutting-edge algorithms under the unpredictable nature of VANETs.
{"title":"A Swarm Intelligent–Based Cluster Optimization in Vehicular Ad Hoc Networks for ITS","authors":"Sandeep. Y, Venugopal. P","doi":"10.1002/dac.70016","DOIUrl":"https://doi.org/10.1002/dac.70016","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) has transformed vehicular ad hoc networks (VANETs), leading to the Internet of Vehicles (IOV). VANETs are wireless networks without fixed infrastructure, designed to improve traffic safety in real time, supporting intelligent transportation systems (ITS). Due to their unpredictable nature, VANETs face major challenges like frequent link failures, scalability, reliability, network layout issues, quality of service (QoS), and security, all of which are complex and difficult to solve (NP-hard problems). Traditional protocols are unsuitable for VANETs due to their unique properties. To accomplish the optimal number of clusters and achieve stability in VANETs within a dynamic environment, we propose a swarm-based metaheuristic algorithm called the rat swarm optimization (RSO) algorithm. The RSO algorithm employs a clustering technique to optimize the network performance and ensure efficient communication in VANETs. The RSO algorithm optimizes load based on node transmission range (Tx range) through effective resource utilization and coordination. RSO organizes the unstructured network into cluster structures and generates near-optimal clusters and CHs to reduce network randomness and maintain stability with lower communication costs. By keeping the number of clusters at an optimal level, the RSO algorithm enhances cluster lifetime and overall network performance. To assess the effectiveness and efficiency of the RSO algorithm, numerous experiments are performed by using various grid sizes, Tx ranges, and nodes in the network. The generated results demonstrate that the RSO algorithm stimulates 50.96%, 33.15%, 88.73%, and 96.70% optimal number of clusters when contrasted with the clustering algorithm–based on ant colony optimization (CACONET), moth flame clustering algorithm for IoV (MFCA-IoV), the whale optimization algorithm for clustering in vehicular ad hoc networks (WOACNET), and grasshoppers' optimization-based node clustering technique for VANETs (GOA) when the Tx range and nodes are taken into consideration. But, when the grid size is considered, the RSO generates 32.31%, 15.23%, 47.04%, and 58.33% optimal number of clusters when compared to cutting-edge algorithms. Hence, the quantitative results and the statistical representation show the proposed RSO algorithm's effectiveness over cutting-edge algorithms under the unpredictable nature of VANETs.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Guo, Ruiheng Zhang, Tingting Song, Xiaojuan Ban
3D object detection leverages sensors like LiDAR and cameras to capture scene information, enabling precise determination of objects' spatial positions and orientations. This technology finds extensive applications in autonomous driving, smart homes, industrial automation, and intelligent security systems. However, high-precision 3D object detection algorithms often require substantial computational resources, posing limitations for deployment on resource-constrained devices. In this paper, we devise an efficient computation offloading and data transmission framework specifically tailored for edge computing networks to address this challenge. Our framework takes into account both the computing and communication capabilities of terminal devices and network conditions, offloading suitable computation tasks to the edge for processing. This approach mitigates the algorithm's performance requirements on terminal devices. Furthermore, we propose a data transmission scheme that incorporates attention mechanisms and hardware-accelerated coding. This scheme effectively reduces detection time and enhances overall system performance. Experimental results demonstrate that our proposed framework significantly enhances the efficiency of 3D object detection on resource-constrained devices within edge computing networks, while maintaining high detection accuracy.
{"title":"Efficient Computation Offloading and Data Transmission Strategy for 3D Object Detection in Edge Computing Networks","authors":"Yu Guo, Ruiheng Zhang, Tingting Song, Xiaojuan Ban","doi":"10.1002/dac.70023","DOIUrl":"https://doi.org/10.1002/dac.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>3D object detection leverages sensors like LiDAR and cameras to capture scene information, enabling precise determination of objects' spatial positions and orientations. This technology finds extensive applications in autonomous driving, smart homes, industrial automation, and intelligent security systems. However, high-precision 3D object detection algorithms often require substantial computational resources, posing limitations for deployment on resource-constrained devices. In this paper, we devise an efficient computation offloading and data transmission framework specifically tailored for edge computing networks to address this challenge. Our framework takes into account both the computing and communication capabilities of terminal devices and network conditions, offloading suitable computation tasks to the edge for processing. This approach mitigates the algorithm's performance requirements on terminal devices. Furthermore, we propose a data transmission scheme that incorporates attention mechanisms and hardware-accelerated coding. This scheme effectively reduces detection time and enhances overall system performance. Experimental results demonstrate that our proposed framework significantly enhances the efficiency of 3D object detection on resource-constrained devices within edge computing networks, while maintaining high detection accuracy.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fifth generation and beyond (5GB) technology requires low latency, high capacity, and constant connectivity for safety and reliable service. Multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies can help meet these needs. However, MIMO can cause extra overhead due to massive channel feedback, and mmWave signals weaken over short distances, leading to limited coverage. Intelligent reflecting surfaces (IRSs) and highly directive active beamforming are recommended to address coverage and overhead issues. Most IRS research focuses on optimizing phase shifts in two dimensions. This paper introduces a three-dimensional model to jointly evaluate user location and IRS phase shift optimization. Additionally, phase constants are derived from optimal phase shifts to limit training overhead. A random forest learning algorithm is proposed, using optimal phase constants and codebook indices to train for each estimated location. Data transmission utilizes the Doppler effect to predict the possible locations of a user. In this way, the trained model can perform high-resolution joint beamforming for the current and future locations of the user. Simulation results show that the model accurately predicts phase shifts without needing channel state information while keeping complexity and training overhead low.
{"title":"A 3D High-Resolution Joint Location and Beamforming Prediction Model for IRS-Aided Wireless Networks","authors":"Gyana Ranjan Mati, Susmita Das","doi":"10.1002/dac.70024","DOIUrl":"https://doi.org/10.1002/dac.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>Fifth generation and beyond (5GB) technology requires low latency, high capacity, and constant connectivity for safety and reliable service. Multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies can help meet these needs. However, MIMO can cause extra overhead due to massive channel feedback, and mmWave signals weaken over short distances, leading to limited coverage. Intelligent reflecting surfaces (IRSs) and highly directive active beamforming are recommended to address coverage and overhead issues. Most IRS research focuses on optimizing phase shifts in two dimensions. This paper introduces a three-dimensional model to jointly evaluate user location and IRS phase shift optimization. Additionally, phase constants are derived from optimal phase shifts to limit training overhead. A random forest learning algorithm is proposed, using optimal phase constants and codebook indices to train for each estimated location. Data transmission utilizes the Doppler effect to predict the possible locations of a user. In this way, the trained model can perform high-resolution joint beamforming for the current and future locations of the user. Simulation results show that the model accurately predicts phase shifts without needing channel state information while keeping complexity and training overhead low.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents a single-layer wideband textile multiple-input–multiple-output (MIMO) antenna for wearable devices. The antenna design is made up of two rectangular-shaped monopole antennas that are mirror imaged and connected to achieve an equal voltage level in the ground surface. The antenna elements are excited by 50-Ω microstrip feed lines. By using a triangular stub decoupling element on the ground plane, greater than 22 dB of isolation is attained among antenna elements. The suggested two-element MIMO antenna has a bandwidth of 2.3–8.0 GHz and a dimension of 30 mm × 58 mm × 1 mm. In addition, four- and eight-element MIMO geometries have been designed and analyzed for massive MIMO applications. Also, an eight-element MIMO belt antenna for wearable straps is investigated. The effects of antenna bending on the human body are also investigated.
{"title":"Design and Implementation of High Isolation Textile MIMO Antenna for Wearable Applications","authors":"Sanjeev Kumar, Kunal Srivastava, Sachin Kumar, Deepti Sharma, Rakesh N. Tiwari, Abhishek Kandwal, Mahesh Kumar Singh, Bhawna Goyal","doi":"10.1002/dac.70010","DOIUrl":"https://doi.org/10.1002/dac.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>This work presents a single-layer wideband textile multiple-input–multiple-output (MIMO) antenna for wearable devices. The antenna design is made up of two rectangular-shaped monopole antennas that are mirror imaged and connected to achieve an equal voltage level in the ground surface. The antenna elements are excited by 50-Ω microstrip feed lines. By using a triangular stub decoupling element on the ground plane, greater than 22 dB of isolation is attained among antenna elements. The suggested two-element MIMO antenna has a bandwidth of 2.3–8.0 GHz and a dimension of 30 mm × 58 mm × 1 mm. In addition, four- and eight-element MIMO geometries have been designed and analyzed for massive MIMO applications. Also, an eight-element MIMO belt antenna for wearable straps is investigated. The effects of antenna bending on the human body are also investigated.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}