Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102578
Mohsen Moradi, Amir Mozammel
This paper proposes a rate-profile construction method for polarization-adjusted convolutional (PAC) codes of any code length and rate, which is capable of preserving the trade-off between the error-correction performance and decoding complexity of PAC codes. The proposed method can improve the error-correction performance of PAC codes while guaranteeing a low mean sequential decoding complexity for signal-to-noise ratio (SNR) values beyond a target SNR value.
{"title":"A Monte-Carlo based construction of polarization-adjusted convolutional (PAC) codes","authors":"Mohsen Moradi, Amir Mozammel","doi":"10.1016/j.phycom.2024.102578","DOIUrl":"10.1016/j.phycom.2024.102578","url":null,"abstract":"<div><div>This paper proposes a rate-profile construction method for polarization-adjusted convolutional (PAC) codes of any code length and rate, which is capable of preserving the trade-off between the error-correction performance and decoding complexity of PAC codes. The proposed method can improve the error-correction performance of PAC codes while guaranteeing a low mean sequential decoding complexity for signal-to-noise ratio (SNR) values beyond a target SNR value.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102578"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158142","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102589
Xuefeng Chen, Rui Ma
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks offer a powerful solution for enhancing communication efficiency in resource-constrained environments and managing compute-intensive tasks. However, the inherent limitations of UAVs, such as restricted data storage, computation capability and battery capacity, hinder the maximum communication efficiency. For the first time, this paper investigates a semantic-aware mobile edge computing (SMEC) network, where task data is semantically compressed at the users and processed at edge computing servers. This approach aims to significantly reduce the transmission and storage overhead in UAV, and improve task performance in low signal-to-noise ratio (SNR). To further enhance transmission robustness and task performance, we incorporate a UAV-carried mobile intelligent reflecting surface (IRS). The objective is to minimize system costs while maintaining task performance, which requires the joint optimization of UAV trajectories, server pairings, user assignments, and IRS reflecting elements. This problem is NP-hard, posing significant computational challenges. To address the complexity of the formulated problem, we propose a novel cognitive UAV-IRS planning strategy based on deep reinforcement learning (DRL), where the UAV can infer the task intentions of the users. Simulation results demonstrate the effectiveness of our intelligent scheme, showing rapid convergence in solving the complex optimization problem. Comparative analysis with benchmark schemes reveals a substantial reduction in system costs and more robust task performance achieved by our proposed approach.
{"title":"Cognitive UAV-IRS planning for semantic-aware mobile edge computing networks","authors":"Xuefeng Chen, Rui Ma","doi":"10.1016/j.phycom.2024.102589","DOIUrl":"10.1016/j.phycom.2024.102589","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks offer a powerful solution for enhancing communication efficiency in resource-constrained environments and managing compute-intensive tasks. However, the inherent limitations of UAVs, such as restricted data storage, computation capability and battery capacity, hinder the maximum communication efficiency. For the first time, this paper investigates a semantic-aware mobile edge computing (SMEC) network, where task data is semantically compressed at the users and processed at edge computing servers. This approach aims to significantly reduce the transmission and storage overhead in UAV, and improve task performance in low signal-to-noise ratio (SNR). To further enhance transmission robustness and task performance, we incorporate a UAV-carried mobile intelligent reflecting surface (IRS). The objective is to minimize system costs while maintaining task performance, which requires the joint optimization of UAV trajectories, server pairings, user assignments, and IRS reflecting elements. This problem is NP-hard, posing significant computational challenges. To address the complexity of the formulated problem, we propose a novel cognitive UAV-IRS planning strategy based on deep reinforcement learning (DRL), where the UAV can infer the task intentions of the users. Simulation results demonstrate the effectiveness of our intelligent scheme, showing rapid convergence in solving the complex optimization problem. Comparative analysis with benchmark schemes reveals a substantial reduction in system costs and more robust task performance achieved by our proposed approach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102589"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157049","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102586
Asma Ahmadinejad, Siamak Talebi
Beamforming design is a pivotal issue in intelligent reflecting surface (IRS) assisted wireless communication. The capacity of the classic regular IRS-based schemes with a few numbers of elements is not convincing. In order to deal with this issue and gain spatial degrees of freedom, we offer an irregular IRS architecture and investigate a weighted sum rate (WSR) maximization problem so as to enhance the system capacity. WSR maximization subject to the transmit power is a nonconvex problem and confronting with this issue is arduous. Despite some existing approaches exhibit proper results, several defects such as computational complexity, acquiring local optimal solutions and so on are still controversial. In this paper, unlike these conventional techniques, a machine learning (ML) inspired beamforming design is presented. In the offered method, the goal is to employ a deep learning (DL) model which, via utilizing only omni or quasi-omni beam patterns, learns how to predict the precoding vectors. In order to improve the support of this system, instead of hiring position information, uplink received signal are used for beamforming prediction. In addition, a joint optimization method was considered in order to iteratively handle the optimization problem. Moreover, other fruitful advantages such as negligible training overhead and no need for training before deployment are attained. Simulation results, based on accurate ray tracing, affirm that the offered method access premiere performance compared with conventional beamforming approaches.
{"title":"Beamforming design via machine learning in intelligent reflecting surface-aided wireless communication","authors":"Asma Ahmadinejad, Siamak Talebi","doi":"10.1016/j.phycom.2024.102586","DOIUrl":"10.1016/j.phycom.2024.102586","url":null,"abstract":"<div><div>Beamforming design is a pivotal issue in intelligent reflecting surface (IRS) assisted wireless communication. The capacity of the classic regular IRS-based schemes with a few numbers of elements is not convincing. In order to deal with this issue and gain spatial degrees of freedom, we offer an irregular IRS architecture and investigate a weighted sum rate (WSR) maximization problem so as to enhance the system capacity. WSR maximization subject to the transmit power is a nonconvex problem and confronting with this issue is arduous. Despite some existing approaches exhibit proper results, several defects such as computational complexity, acquiring local optimal solutions and so on are still controversial. In this paper, unlike these conventional techniques, a machine learning (ML) inspired beamforming design is presented. In the offered method, the goal is to employ a deep learning (DL) model which, via utilizing only omni or quasi-omni beam patterns, learns how to predict the precoding vectors. In order to improve the support of this system, instead of hiring position information, uplink received signal are used for beamforming prediction. In addition, a joint optimization method was considered in order to iteratively handle the optimization problem. Moreover, other fruitful advantages such as negligible training overhead and no need for training before deployment are attained. Simulation results, based on accurate ray tracing, affirm that the offered method access premiere performance compared with conventional beamforming approaches.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102586"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158138","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102579
Yong Wang , Bangwei Yu , Ying Wang , Liangang Qi , Yang Liu
In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate (BER). In this paper, a Paradigm Inner Product Orthogonal Matching Pursuit (PIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, the observation vectors of pump noise are obtained by signal cyclic prefix (CP) differencing, and the resulting observation vectors are more accurate than the traditional methods. Second, the columns of the sensing matrix that are most relevant to the observation vectors are selected as candidate support sets by computing the L2 paradigm. Then, the least squares method was used to solve for the estimated value of the pump noise at the previous moment. Finally, the pump noise is reconstructed by combining the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We simulate the denoising performance of pump noise under stable and unstable conditions and analyze the denoising performance of the PIPOMP algorithm in depth. Simulation results show that the algorithm significantly improves the interference immunity performance and reduces the system BER in the environment where pump noise interferes and the fading is more drastic.
{"title":"Noise removal techniques for underground communication systems based on matching pursuit","authors":"Yong Wang , Bangwei Yu , Ying Wang , Liangang Qi , Yang Liu","doi":"10.1016/j.phycom.2024.102579","DOIUrl":"10.1016/j.phycom.2024.102579","url":null,"abstract":"<div><div>In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate (BER). In this paper, a Paradigm Inner Product Orthogonal Matching Pursuit (PIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, the observation vectors of pump noise are obtained by signal cyclic prefix (CP) differencing, and the resulting observation vectors are more accurate than the traditional methods. Second, the columns of the sensing matrix that are most relevant to the observation vectors are selected as candidate support sets by computing the L2 paradigm. Then, the least squares method was used to solve for the estimated value of the pump noise at the previous moment. Finally, the pump noise is reconstructed by combining the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We simulate the denoising performance of pump noise under stable and unstable conditions and analyze the denoising performance of the PIPOMP algorithm in depth. Simulation results show that the algorithm significantly improves the interference immunity performance and reduces the system BER in the environment where pump noise interferes and the fading is more drastic.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102579"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157538","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102566
Haiqiang Chen , Yan Chen , Yuanbo Liu , Rui Wang , Xiangcheng Li , Youming Sun , Qingnian Li
A perturbation sphere decoding algorithm for control channels with small payload size is proposed in this paper. When a codeword fails the cyclic redundancy check (CRC), the algorithm performs a perturbation operation by adding noise to the codeword and achieves a new received sequence. Subsequently, this perturbed sequence is sent to the decoder for another decoding attempt. Moreover, a partial perturbation algorithm is proposed to further reduce the resource consumption, which only applies perturbation for those rows having relative low weights, resulting in improved performance and reduced complexity. Simulation results show that, the two proposed algorithms exhibit excellent performance over Rayleigh fading channel and the performance gain increases with the maximum perturbation number. For (64,22) polar codes, the two algorithms can achieve about 0.58 dB and 0.71 dB performance gain, respectively, with maximum perturbation number = 4 and FER = , compared to the conventional sphere decoding algorithm. Meanwhile, the complexity of partial perturbation algorithm is reduced to about 77.66% compared to the perturbation algorithm at the SNR = 5 dB.
{"title":"Polar-coded perturbation sphere decoding algorithm over Rayleigh fading channel","authors":"Haiqiang Chen , Yan Chen , Yuanbo Liu , Rui Wang , Xiangcheng Li , Youming Sun , Qingnian Li","doi":"10.1016/j.phycom.2024.102566","DOIUrl":"10.1016/j.phycom.2024.102566","url":null,"abstract":"<div><div>A perturbation sphere decoding algorithm for control channels with small payload size is proposed in this paper. When a codeword fails the cyclic redundancy check (CRC), the algorithm performs a perturbation operation by adding noise to the codeword and achieves a new received sequence. Subsequently, this perturbed sequence is sent to the decoder for another decoding attempt. Moreover, a partial perturbation algorithm is proposed to further reduce the resource consumption, which only applies perturbation for those rows having relative low weights, resulting in improved performance and reduced complexity. Simulation results show that, the two proposed algorithms exhibit excellent performance over Rayleigh fading channel and the performance gain increases with the maximum perturbation number. For (64,22) polar codes, the two algorithms can achieve about 0.58 dB and 0.71 dB performance gain, respectively, with maximum perturbation number = 4 and FER = <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, compared to the conventional sphere decoding algorithm. Meanwhile, the complexity of partial perturbation algorithm is reduced to about 77.66% compared to the perturbation algorithm at the SNR = 5 dB.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102566"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157539","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102568
Xingyu Wang, Xiangdong Zheng, Lianhong Zhang, Chao Li
Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.
{"title":"Deep joint source-channel coding empowered two-way relay networks for wireless image transmission","authors":"Xingyu Wang, Xiangdong Zheng, Lianhong Zhang, Chao Li","doi":"10.1016/j.phycom.2024.102568","DOIUrl":"10.1016/j.phycom.2024.102568","url":null,"abstract":"<div><div>Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102568"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158110","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102555
Xiangyang Xu , Chunlong He , Xingquan Li , Jiaming Xu
Semantic communication can effectively save bandwidth, and enhance communication capabilities by transmitting semantic features. However, semantic communication has certain limitations, such as limited application scenarios and inflexible deployment. To this end, we investigate an unmanned aerial vehicle (UAV)-assisted semantic communication system in this paper. An UAV serves as a mobile base station to service users in designated area. Each user has different requirements for transmission delay and performance, and the UAV has a limited maximum flight time. We need to achieve the communication goals of all users in the shortest possible time, that is, to ensure that the information received by each user meets latency and quality requirements. This is a non-convex optimization problem, which is very complicated to solve using traditional methods. In order to solve this problem, we propose a deep reinforcement learning algorithm based on Proximal Policy Optimization 2. The simulation results confirm the effectiveness of our proposed algorithm.
{"title":"Joint optimization trajectory and resource allocation for UAV-assisted semantic communications","authors":"Xiangyang Xu , Chunlong He , Xingquan Li , Jiaming Xu","doi":"10.1016/j.phycom.2024.102555","DOIUrl":"10.1016/j.phycom.2024.102555","url":null,"abstract":"<div><div>Semantic communication can effectively save bandwidth, and enhance communication capabilities by transmitting semantic features. However, semantic communication has certain limitations, such as limited application scenarios and inflexible deployment. To this end, we investigate an unmanned aerial vehicle (UAV)-assisted semantic communication system in this paper. An UAV serves as a mobile base station to service users in designated area. Each user has different requirements for transmission delay and performance, and the UAV has a limited maximum flight time. We need to achieve the communication goals of all users in the shortest possible time, that is, to ensure that the information received by each user meets latency and quality requirements. This is a non-convex optimization problem, which is very complicated to solve using traditional methods. In order to solve this problem, we propose a deep reinforcement learning algorithm based on Proximal Policy Optimization 2. The simulation results confirm the effectiveness of our proposed algorithm.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102555"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158139","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102595
Siyang Xu , Songze Wu , Ye Zheng
The integration of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) has emerged as a transformative approach to enhance the performance of next-generation wireless networks. However, managing energy efficiency (EE) and addressing the challenges posed by dynamic network environments remain critical issues. In this paper, we propose a novel joint dynamic user pairing and resource allocation framework tailored for RIS-aided multi-cluster NOMA networks. Specifically, we consider scenarios where users experience varying degrees of obstruction, categorizing them as having either direct or non-direct links to the base station (BS). To mitigate interference and enhance system performance, a dynamic user pairing algorithm is developed to cluster users with distinct channel conditions based on channel gain difference and correlation. Building on this, an EE maximization problem is formulated, involving the joint optimization of the beamforming matrix, power allocation factors, and RIS phase shifts. The inherent non-convexity and coupling of the problem are addressed using a solution framework that combines auxiliary variables, successive convex approximation (SCA), and alternating optimization (AO) to convert the problem into a tractable convex form. Simulation results demonstrate the superior performance of the proposed algorithms in improving energy efficiency and mitigating interference compared to existing benchmarks.
{"title":"Joint dynamic user pairing and resource allocation for RIS-aided multi-cluster NOMA networks","authors":"Siyang Xu , Songze Wu , Ye Zheng","doi":"10.1016/j.phycom.2024.102595","DOIUrl":"10.1016/j.phycom.2024.102595","url":null,"abstract":"<div><div>The integration of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) has emerged as a transformative approach to enhance the performance of next-generation wireless networks. However, managing energy efficiency (EE) and addressing the challenges posed by dynamic network environments remain critical issues. In this paper, we propose a novel joint dynamic user pairing and resource allocation framework tailored for RIS-aided multi-cluster NOMA networks. Specifically, we consider scenarios where users experience varying degrees of obstruction, categorizing them as having either direct or non-direct links to the base station (BS). To mitigate interference and enhance system performance, a dynamic user pairing algorithm is developed to cluster users with distinct channel conditions based on channel gain difference and correlation. Building on this, an EE maximization problem is formulated, involving the joint optimization of the beamforming matrix, power allocation factors, and RIS phase shifts. The inherent non-convexity and coupling of the problem are addressed using a solution framework that combines auxiliary variables, successive convex approximation (SCA), and alternating optimization (AO) to convert the problem into a tractable convex form. Simulation results demonstrate the superior performance of the proposed algorithms in improving energy efficiency and mitigating interference compared to existing benchmarks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102595"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157107","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}
Sixth generation (6G) wireless technology is predicted to bring revolutionary changes to network sensing capabilities and allow for extraordinarily large communication capacities. Because of its unexplored frequency bands, short wavelengths, and bandwidths that provide excellent spatial resolution, the terahertz (THz) frequency range (0.1 THz–10 THz) stands out as a potential enabler. In addition to helping to address the existing spectrum shortage, the enormous bandwidth accessible at THz frequencies can clear the path for the wireless terabit-per-second (Tbps) connectivity needed for 6G networks. This article in contrast to previous surveys concentrates on the difficulties and essential technologies in THz communication. The use of intelligent reflecting surfaces (IRS) in THz communication systems is heavily emphasized. With its capacity to dynamically control electromagnetic waves, IRS technology offers a revolutionary means of addressing issues including excessive path loss and absorption losses related to THz frequencies. In the THz domain, the notion of integrated sensing and communication (ISAC) is also discussed, emphasizing its dual role in enabling data transfer and environmental sensing at the same time. This review attempts to provide a thorough knowledge of the revolutionary influence of these new technologies on future wireless networks by looking at the fundamentals, technological developments, and real-world applications of THz-IRS systems. THz communication integration with IRS and ISAC has the potential to completely transform wireless communication and open the door to incredibly fast, dependable, and intelligent network solutions.
{"title":"A journey of terahertz communication: An IRS integration perspective","authors":"Pranali Langde , Tapan Kumar Jain , Mayur R. Parate , Sandeep Kumar Singh","doi":"10.1016/j.phycom.2024.102572","DOIUrl":"10.1016/j.phycom.2024.102572","url":null,"abstract":"<div><div>Sixth generation (6G) wireless technology is predicted to bring revolutionary changes to network sensing capabilities and allow for extraordinarily large communication capacities. Because of its unexplored frequency bands, short wavelengths, and bandwidths that provide excellent spatial resolution, the terahertz (THz) frequency range (0.1 THz–10 THz) stands out as a potential enabler. In addition to helping to address the existing spectrum shortage, the enormous bandwidth accessible at THz frequencies can clear the path for the wireless terabit-per-second (Tbps) connectivity needed for 6G networks. This article in contrast to previous surveys concentrates on the difficulties and essential technologies in THz communication. The use of intelligent reflecting surfaces (IRS) in THz communication systems is heavily emphasized. With its capacity to dynamically control electromagnetic waves, IRS technology offers a revolutionary means of addressing issues including excessive path loss and absorption losses related to THz frequencies. In the THz domain, the notion of integrated sensing and communication (ISAC) is also discussed, emphasizing its dual role in enabling data transfer and environmental sensing at the same time. This review attempts to provide a thorough knowledge of the revolutionary influence of these new technologies on future wireless networks by looking at the fundamentals, technological developments, and real-world applications of THz-IRS systems. THz communication integration with IRS and ISAC has the potential to completely transform wireless communication and open the door to incredibly fast, dependable, and intelligent network solutions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102572"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158114","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}
Pub Date : 2025-02-01DOI: 10.1016/j.phycom.2024.102577
Yanliang Jin, Pengdan Qi, Yuan Gao, Shengli Liu
Channel estimation of reconfigurable intelligent surface-aided multi-user communication (RIS-MUC) systems is one of the key tasks for expanding network coverage and improving signal transmission quality. However, such a system typically involves cascaded channels with complex statistical distributions, making channel estimation more challenging. Existing channel estimation methods face the dual challenges of high pilot overhead and limited estimation accuracy. To address the above problems, this paper proposes an efficient channel estimation framework that integrates deep learning and two-timescale channel estimation to minimize pilot overhead and improve estimation accuracy. First, this paper models the channel estimation problem as a denoising problem. Then, a denoising neural network based on the convolutional neural network (CNN) and residual structures is designed, which is named the dual-path residual attention network (DPRAN). The network leverages parallel residual structures and spatial attention mechanisms to extract spatial features from the noisy channel matrix for channel recovery. Experimental results reveal that the proposed method can achieve higher channel estimation accuracy under different channel conditions and system configurations.
{"title":"Dual-path residual attention network for efficient channel estimation in RIS-assisted communication systems","authors":"Yanliang Jin, Pengdan Qi, Yuan Gao, Shengli Liu","doi":"10.1016/j.phycom.2024.102577","DOIUrl":"10.1016/j.phycom.2024.102577","url":null,"abstract":"<div><div>Channel estimation of reconfigurable intelligent surface-aided multi-user communication (RIS-MUC) systems is one of the key tasks for expanding network coverage and improving signal transmission quality. However, such a system typically involves cascaded channels with complex statistical distributions, making channel estimation more challenging. Existing channel estimation methods face the dual challenges of high pilot overhead and limited estimation accuracy. To address the above problems, this paper proposes an efficient channel estimation framework that integrates deep learning and two-timescale channel estimation to minimize pilot overhead and improve estimation accuracy. First, this paper models the channel estimation problem as a denoising problem. Then, a denoising neural network based on the convolutional neural network (CNN) and residual structures is designed, which is named the dual-path residual attention network (DPRAN). The network leverages parallel residual structures and spatial attention mechanisms to extract spatial features from the noisy channel matrix for channel recovery. Experimental results reveal that the proposed method can achieve higher channel estimation accuracy under different channel conditions and system configurations.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102577"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158135","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}
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