Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on $ l_{1} $ -norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we propose a sigmoid-based approximation of the $ l_{0} $ -norm AC indicator, enabling a more efficient and tractable reformulation of the problem. Additionally, we introduce a penalty dual decomposition (PDD) algorithm to jointly optimize beamforming and admission control, ensuring convergence to a stationary solution. This approach reduces computational complexity and supports distributed implementation. Moreover, it outperforms existing methods by achieving lower power consumption, accommodating more users, and reducing computational time.
{"title":"Joint Admission Control and Power Minimization in IRS-Assisted Networks","authors":"Weijie Xiong;Jingran Lin;Zhiling Xiao;Qiang Li;Yuhan Zhang","doi":"10.1109/LCOMM.2025.3528041","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3528041","url":null,"abstract":"Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on <inline-formula> <tex-math>$ l_{1} $ </tex-math></inline-formula>-norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we propose a sigmoid-based approximation of the <inline-formula> <tex-math>$ l_{0} $ </tex-math></inline-formula>-norm AC indicator, enabling a more efficient and tractable reformulation of the problem. Additionally, we introduce a penalty dual decomposition (PDD) algorithm to jointly optimize beamforming and admission control, ensuring convergence to a stationary solution. This approach reduces computational complexity and supports distributed implementation. Moreover, it outperforms existing methods by achieving lower power consumption, accommodating more users, and reducing computational time.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"512-516"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-01-09DOI: 10.1109/LCOMM.2025.3527693
Zhicheng Liu;Liuquan Yao;Shuai Yuan;Guiying Yan;Zhiming Ma;Yuting Liu
In this letter, we analyze the delay probability of the first error position in perturbation-enhanced Successive cancellation (SC) decoding for polar codes. Our findings reveal that, asymptotically, an SC decoder’s performance does not degrade after one perturbation, and it improves with a probability of $frac {1}{2}$ . This analysis explains the sustained performance gains of perturbation-enhanced SC decoding as code length increases.
{"title":"Performance Analysis of Perturbation-Enhanced SC Decoders","authors":"Zhicheng Liu;Liuquan Yao;Shuai Yuan;Guiying Yan;Zhiming Ma;Yuting Liu","doi":"10.1109/LCOMM.2025.3527693","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3527693","url":null,"abstract":"In this letter, we analyze the delay probability of the first error position in perturbation-enhanced Successive cancellation (SC) decoding for polar codes. Our findings reveal that, asymptotically, an SC decoder’s performance does not degrade after one perturbation, and it improves with a probability of <inline-formula> <tex-math>$frac {1}{2}$ </tex-math></inline-formula>. This analysis explains the sustained performance gains of perturbation-enhanced SC decoding as code length increases.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"507-511"},"PeriodicalIF":3.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-01-09DOI: 10.1109/LCOMM.2025.3527636
Mengyao Yang;Peng Chen;Tao Luo;Mengjiang Sun
The existing wideband adaptive beamforming algorithms suffer severe performance degradation due to overmuch constraints and high computational complexity. Besides, the desired signal is contained in the received signals, which leads to the phenomenon of self-canceling during the beamforming-based interference suppression. In this letter, a covariance matrix reconstruction-based wideband adaptive beamforming algorithm is proposed to maintain excellent interference suppression performance with low computational complexity. Different from the prior methods, a frequency-angle conversion for wideband beamforming is proposed to convert the wideband signal into several narrowband signals. Thus, an interference-plus-noise covariance matrix (IPNCM) for wideband beamforming can be reconstructed by strategies from narrowband beamforming. Meanwhile, a Gauss-Legendre quadrature (GLQ) is introduced to approximate the integral operation, which provides high accuracy and low computational complexity compared to the polynomial summation. Furthermore, a spatial response variation (SRV) constraint is introduced to reduce the number of constraints and obtain more degrees of freedom to promote interference suppression ability. Simulation results demonstrate the effectiveness of the proposed beamformer with low computational complexity.
{"title":"Low-Complexity Covariance Matrix Reconstruction Method for Wideband Adaptive Beamforming","authors":"Mengyao Yang;Peng Chen;Tao Luo;Mengjiang Sun","doi":"10.1109/LCOMM.2025.3527636","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3527636","url":null,"abstract":"The existing wideband adaptive beamforming algorithms suffer severe performance degradation due to overmuch constraints and high computational complexity. Besides, the desired signal is contained in the received signals, which leads to the phenomenon of self-canceling during the beamforming-based interference suppression. In this letter, a covariance matrix reconstruction-based wideband adaptive beamforming algorithm is proposed to maintain excellent interference suppression performance with low computational complexity. Different from the prior methods, a frequency-angle conversion for wideband beamforming is proposed to convert the wideband signal into several narrowband signals. Thus, an interference-plus-noise covariance matrix (IPNCM) for wideband beamforming can be reconstructed by strategies from narrowband beamforming. Meanwhile, a Gauss-Legendre quadrature (GLQ) is introduced to approximate the integral operation, which provides high accuracy and low computational complexity compared to the polynomial summation. Furthermore, a spatial response variation (SRV) constraint is introduced to reduce the number of constraints and obtain more degrees of freedom to promote interference suppression ability. Simulation results demonstrate the effectiveness of the proposed beamformer with low computational complexity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"502-506"},"PeriodicalIF":3.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-01-08DOI: 10.1109/LCOMM.2025.3527463
Jixiang Chen;Quansheng Guan;Yue Rong;Chuanlin Liu
Ambient backscatter communication (AmBC) is a new advanced technology that utilizes ambient radio frequency signals to enable the communications of battery-free devices and has attracted much attention recently. Existing works develop the high-order modulation in signal domains including the time domain, the frequency domain and the space domain. However, the work in the code domain for AmBC remains missing. In this letter, we extend the high-order modulation to the code domain, i.e., high-order code shift keying (CSK). However, the detection that correlates the received signals with the candidate code cannot work due to the unknown source signals. To detect the active code index, the correlation energy detection (CED) is proposed in which the received signal energy instead of the amplitude is utilized. In this process, CED does not need the channel state information and source power knowledge. Simulation results show that the proposed CED can handle the complex source signal and achieve a desirable bit error rate (BER). The second-order CSK without the training sequences outperforms the on-off keying modulation with training sequences in terms of BER.
{"title":"High-Order Code Shift Keying for Ambient Backscatter Communications","authors":"Jixiang Chen;Quansheng Guan;Yue Rong;Chuanlin Liu","doi":"10.1109/LCOMM.2025.3527463","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3527463","url":null,"abstract":"Ambient backscatter communication (AmBC) is a new advanced technology that utilizes ambient radio frequency signals to enable the communications of battery-free devices and has attracted much attention recently. Existing works develop the high-order modulation in signal domains including the time domain, the frequency domain and the space domain. However, the work in the code domain for AmBC remains missing. In this letter, we extend the high-order modulation to the code domain, i.e., high-order code shift keying (CSK). However, the detection that correlates the received signals with the candidate code cannot work due to the unknown source signals. To detect the active code index, the correlation energy detection (CED) is proposed in which the received signal energy instead of the amplitude is utilized. In this process, CED does not need the channel state information and source power knowledge. Simulation results show that the proposed CED can handle the complex source signal and achieve a desirable bit error rate (BER). The second-order CSK without the training sequences outperforms the on-off keying modulation with training sequences in terms of BER.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"497-501"},"PeriodicalIF":3.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-01-08DOI: 10.1109/LCOMM.2025.3526865
Jianpeng Zou;Zheng Shi;Binggui Zhou;Yaru Fu;Hong Wang;Weiqiang Tan
In this letter, hybrid automatic retransmission request with incremental redundancy (HARQ-IR) is applied to assist integrated sensing and communication (ISAC). The long term average throughput (LTAT) of HARQ-IR-assisted ISAC is maximized via power allocation while ensuring both communication and sensing reliability as well as total average power budget. Since the LTAT maximization is a non-convex problem, the asymptotic outage approximation is leveraged for problem relaxation. Subsequently, successive convex approximation (SCA) is exploited to convert it into a successive geometric programming (GP) problem. However, the GP-based solution underestimates the LTAT due to the large approximation error of the asymptotic outage probability at a low signal-to-noise ratio (SNR). To address this issue, the original problem is transformed to a Markov decision process, which can be solved with deep reinforcement learning (DRL), e.g., deep deterministic policy gradient. Numerical results show that the DRL-based method delivers comparable performance to the GP-based one at high SNR while performing much better than the GP-based method at low SNR, albeit at the cost of higher complexity.
{"title":"Throughput Maximization of HARQ-IR for ISAC","authors":"Jianpeng Zou;Zheng Shi;Binggui Zhou;Yaru Fu;Hong Wang;Weiqiang Tan","doi":"10.1109/LCOMM.2025.3526865","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3526865","url":null,"abstract":"In this letter, hybrid automatic retransmission request with incremental redundancy (HARQ-IR) is applied to assist integrated sensing and communication (ISAC). The long term average throughput (LTAT) of HARQ-IR-assisted ISAC is maximized via power allocation while ensuring both communication and sensing reliability as well as total average power budget. Since the LTAT maximization is a non-convex problem, the asymptotic outage approximation is leveraged for problem relaxation. Subsequently, successive convex approximation (SCA) is exploited to convert it into a successive geometric programming (GP) problem. However, the GP-based solution underestimates the LTAT due to the large approximation error of the asymptotic outage probability at a low signal-to-noise ratio (SNR). To address this issue, the original problem is transformed to a Markov decision process, which can be solved with deep reinforcement learning (DRL), e.g., deep deterministic policy gradient. Numerical results show that the DRL-based method delivers comparable performance to the GP-based one at high SNR while performing much better than the GP-based method at low SNR, albeit at the cost of higher complexity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"492-496"},"PeriodicalIF":3.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 burgeoning technology of deep learning-based semantic communications has significantly enhanced the efficiency and reliability of wireless communication systems by facilitating the transmission of semantic features. However, security threats, notably the interception of sensitive data, remain a significant challenge for secure communications. To safeguard the confidentiality of transmitted semantics and effectively counteract eavesdropping threats, this letter proposes a secure deep learning-based semantic communication system, SecureDSC. It comprises semantic encoder/decoder, channel encoder/decoder, and encryption/decryption modules with a key processing network. By incorporating a symmetric encryption module and an attacker-oriented adversarial network, SecureDSC guarantees the secure transmission between legitimate users in the semantic communications. Besides, experiments are conducted to evaluate the effectiveness and feasibility of the proposed scheme.
{"title":"Secure Transmission in Wireless Semantic Communications With Adversarial Training","authors":"Jiting Shi;Qianyun Zhang;Weihao Zeng;Shufeng Li;Zhijin Qin","doi":"10.1109/LCOMM.2025.3526601","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3526601","url":null,"abstract":"The burgeoning technology of deep learning-based semantic communications has significantly enhanced the efficiency and reliability of wireless communication systems by facilitating the transmission of semantic features. However, security threats, notably the interception of sensitive data, remain a significant challenge for secure communications. To safeguard the confidentiality of transmitted semantics and effectively counteract eavesdropping threats, this letter proposes a secure deep learning-based semantic communication system, SecureDSC. It comprises semantic encoder/decoder, channel encoder/decoder, and encryption/decryption modules with a key processing network. By incorporating a symmetric encryption module and an attacker-oriented adversarial network, SecureDSC guarantees the secure transmission between legitimate users in the semantic communications. Besides, experiments are conducted to evaluate the effectiveness and feasibility of the proposed scheme.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"487-491"},"PeriodicalIF":3.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-01-06DOI: 10.1109/LCOMM.2025.3526155
Ningxin Zhou;Zheng Wang;Cong Ma;Yongming Huang;Qingjiang Shi
The weighted sum mean squared error minimization (WMMSE) algorithm has gained widespread adoption owing to its superior performance. In this letter, we propose a novel low-complexity distributed WMMSE (LCD-WMMSE) algorithm, which is implemented over a decentralized architecture based on ring topology. Although each distributed unit (DU) in LCD-WMMSE works only with the local channel state information (CSI), LCD-WMMSE algorithm is still able to approach the performance of traditional WMMSE. Moreover, we show that LCD-WMMSE is also scalable since its required interconnection bandwidth is independent of the number of transmitter antennas, making it promising to various scenarios of massive MIMO. Simulation results validate that the proposed LCD-WMMSE algorithm not only achieves the low complexity cost in a distributed manner but also exhibits negligible performance loss.
{"title":"Distributed Precoder Based on Weighted MMSE With Low Complexity for Massive MIMO Systems","authors":"Ningxin Zhou;Zheng Wang;Cong Ma;Yongming Huang;Qingjiang Shi","doi":"10.1109/LCOMM.2025.3526155","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3526155","url":null,"abstract":"The weighted sum mean squared error minimization (WMMSE) algorithm has gained widespread adoption owing to its superior performance. In this letter, we propose a novel low-complexity distributed WMMSE (LCD-WMMSE) algorithm, which is implemented over a decentralized architecture based on ring topology. Although each distributed unit (DU) in LCD-WMMSE works only with the local channel state information (CSI), LCD-WMMSE algorithm is still able to approach the performance of traditional WMMSE. Moreover, we show that LCD-WMMSE is also scalable since its required interconnection bandwidth is independent of the number of transmitter antennas, making it promising to various scenarios of massive MIMO. Simulation results validate that the proposed LCD-WMMSE algorithm not only achieves the low complexity cost in a distributed manner but also exhibits negligible performance loss.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"482-486"},"PeriodicalIF":3.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-12-31DOI: 10.1109/LCOMM.2024.3524637
Xiangming Cai;Weikai Xu;Lin Wang;Chau Yuen
In this letter, we propose a reconfigurable intelligent surface (RIS)-aided dual M-ary differential chaos shift keying (RIS-DM-DCSK) system for non-coherent chaotic communications. In RIS-DM-DCSK, a dual M-ary DCSK signal is designed to transmit twice the number of modulated bits compared to a standard M-ary DCSK signal while permutation index modulation is employed to convey additional index bits, thereby enhancing energy efficiency. Furthermore, we propose a permutation index detection and dual M-ary DCSK demodulation algorithm to recover the transmitted bits. The bit error probability (BEP) of RIS-DM-DCSK is analyzed over a Rayleigh fading channel. Numerical results show that the proposed RIS-DM-DCSK achieves superior bit error rate (BER) performance and energy efficiency compared to benchmark schemes.
{"title":"Toward Non-Coherent Chaotic Communications: An RIS-Aided Dual M-ary DCSK System","authors":"Xiangming Cai;Weikai Xu;Lin Wang;Chau Yuen","doi":"10.1109/LCOMM.2024.3524637","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3524637","url":null,"abstract":"In this letter, we propose a reconfigurable intelligent surface (RIS)-aided dual M-ary differential chaos shift keying (RIS-DM-DCSK) system for non-coherent chaotic communications. In RIS-DM-DCSK, a dual M-ary DCSK signal is designed to transmit twice the number of modulated bits compared to a standard M-ary DCSK signal while permutation index modulation is employed to convey additional index bits, thereby enhancing energy efficiency. Furthermore, we propose a permutation index detection and dual M-ary DCSK demodulation algorithm to recover the transmitted bits. The bit error probability (BEP) of RIS-DM-DCSK is analyzed over a Rayleigh fading channel. Numerical results show that the proposed RIS-DM-DCSK achieves superior bit error rate (BER) performance and energy efficiency compared to benchmark schemes.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"472-476"},"PeriodicalIF":3.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-12-31DOI: 10.1109/LCOMM.2024.3524888
Daewon Seo
This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to estimate a random continuous state. Considering the mean-squared error (MSE) for estimation performance metric, the Bayesian Cramér-Rao lower bound (BCRB) is widely used in literature as a proxy of the MSE; however, the BCRB is not generally tight even asymptotically except for restrictive distributions. Instead, we characterize the full tradeoff between information rate and the exact MSE using the asymptotically tight BCRB (ATBCRB) analysis, a recent variant of the BCRB. Our characterization is applicable for general channels as long as the regularity conditions are met, and the proof relies on constant composition codes and ATBCRB analysis with the codes. We also perform a numerical evaluation of the tradeoff in a variance estimation example, which commonly arises in spectrum sensing scenarios.
{"title":"Integrated Communication and Bayesian Estimation of Fixed Channel States","authors":"Daewon Seo","doi":"10.1109/LCOMM.2024.3524888","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3524888","url":null,"abstract":"This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to estimate a random continuous state. Considering the mean-squared error (MSE) for estimation performance metric, the Bayesian Cramér-Rao lower bound (BCRB) is widely used in literature as a proxy of the MSE; however, the BCRB is not generally tight even asymptotically except for restrictive distributions. Instead, we characterize the full tradeoff between information rate and the exact MSE using the asymptotically tight BCRB (ATBCRB) analysis, a recent variant of the BCRB. Our characterization is applicable for general channels as long as the regularity conditions are met, and the proof relies on constant composition codes and ATBCRB analysis with the codes. We also perform a numerical evaluation of the tradeoff in a variance estimation example, which commonly arises in spectrum sensing scenarios.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"477-481"},"PeriodicalIF":3.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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