In this paper, we study relay selection under outdated channel state information（CSI） in a decode-and-forward（DF） cooperative system. Unlike previous researches on cooperative communication under outdated CSI, we ...In this paper, we study relay selection under outdated channel state information（CSI） in a decode-and-forward（DF） cooperative system. Unlike previous researches on cooperative communication under outdated CSI, we consider that the channel varies continuously over time, i.e., the channel not only changes between relay selection and data transmission but also changes during data transmission. Thus the level of accuracy of the CSI used in relay selection degrades with data transmission. We first evaluate the packet error rate（PER） of the cooperative system under continuous time-varying fading channel, and find that the PER performance deteriorates more seriously under continuous time-varying fading channel than when the channel is assumed to be constant during data transmission. Then, we propose a repeated relay selection（RRS） strategy to improve the PER performance, in which the forwarded data is divided into multiple segments and relay is reselected before the transmission of each segment based on the updated CSI. Finally, we propose a combined relay selection（CRS） strategy which takes advantage of three different relay selection strategies to further mitigate the impact of outdated CSI.展开更多
Decision fusion rules for Wireless Sensor Networks (WSNs) under Nakagami fading channels are investigated in this paper. Considering the application limitation of Likelihood Ratio Test fusion rule based on information...Decision fusion rules for Wireless Sensor Networks (WSNs) under Nakagami fading channels are investigated in this paper. Considering the application limitation of Likelihood Ratio Test fusion rule based on information of Channel Statistics using Series expansion (LRT-CSS),and the detection performance limitation of the Censoring based Mixed Fusion rule (CMF),a new LRT fusion rule based on information of channel statistics has been presented using Laplace approximation (LRT-CSL). Theoretical analysis and simulations show that the proposed fusion rule provides better detection performance than the Censoring based Mixed Fusion (CMF) and LRT-CSS fusion rules. Furthermore,compared with LRT-CSS fusion rule,the proposed fusion rule expands the application range of likelihood ratio test fusion rule.展开更多
In this work we find a lower bound on the energy required for synchronizing moving sensor nodes in a Wireless Sensor Network (WSN) affected by large-scale fading, based on clock estimation techniques. The energy requi...In this work we find a lower bound on the energy required for synchronizing moving sensor nodes in a Wireless Sensor Network (WSN) affected by large-scale fading, based on clock estimation techniques. The energy required for synchronizing a WSN within a desired estimation error level is specified by both the transmit power and the required number of messages. In this paper we extend our previous work introducing nodes’ movement and the average message delay in the total energy, including a comprehensive analysis on how the distance between nodes impacts on the energy and synchronization quality trade-off under large-scale fading effects.展开更多
Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuo...Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.展开更多
In this work, the existing trade-off between time synchronization quality and energy is studied for both large-scale and small-scale fading wireless channels. We analyze the clock offset estimation problem using one-w...In this work, the existing trade-off between time synchronization quality and energy is studied for both large-scale and small-scale fading wireless channels. We analyze the clock offset estimation problem using one-way, two-way and N-way message exchange mechanisms affected by Gaussian and exponentially distributed impairments. Our main contribution is a general relationship between the total energy required for synchronizing a wireless sensor network and the clock offset estimation error by means of the transmit power, number of transmitted messages and average message delay, deriving the energy optimal lower bound as a function of the time synchronization quality and the number of hops in a multi-hop network.展开更多
Characterization of a mobile radio channel plays an important role in designing a reliable wireless communication system. Such channels are analyzed by two state model, namely satisfactory and outage state. This paper...Characterization of a mobile radio channel plays an important role in designing a reliable wireless communication system. Such channels are analyzed by two state model, namely satisfactory and outage state. This paper presents the analysis to estimate fading parameters of wireless channel with omission of certain outage durations which are considered as “Tolerance time”. Minimum outage duration which can be tolerated by a wireless fading channel to achieve desired packet error rate is defined as tolerance time. Normally a system with tolerable minimum outage time is analyzed based on Fade Duration Distribution (FDD) function over Rayleigh channel. In this paper Weibull function is used as FDD for varying tolerance time. The approach is simple and in general applicable from Rayleigh to Nakagami channels. The analysis is extended to study the effect of Tolerance time on channel fading statistics such as Average Fade Duration (AFD) and frequency of outage. Further the effects of various fade margin and Doppler spread on fading parameters are also investigated. The analysis can also be used in case of timeout expiration, connection resetting and congestion window control.展开更多
The effects of scatterers, fluctuation parameter and propagation clusters significantly affect the performance of κ-μ shadowed fading channel. On the other hand, opportunistic relaying is an efficient technique to i...The effects of scatterers, fluctuation parameter and propagation clusters significantly affect the performance of κ-μ shadowed fading channel. On the other hand, opportunistic relaying is an efficient technique to improve the performance of fading channels reducing the effects of aforementioned parameters. Motivated by these issues, in this paper, a secure wireless multicasting scenario through κ-μ shadowed fading channel is considered in the presence of multiple eavesdroppers with opportunistic relaying. The main purpose of this paper is to ensure the security level in wireless multicasting compensating the loss of security due to the effects of power ratio between dominant and scattered waves, fluctuation parameter, and the number of propagation clusters, multicast users and eavesdroppers, by opportunistic relaying technique. The closed-form analytical expressions are derived for the probability of non-zero secrecy multicast capacity (PNSMC) and the secure outage probability for multicasting (SOPM) to understand the insight of the effects of above parameters. The results show that the loss of security in multicasting through κ-μ shadowed fading channel can be significantly enhanced using opportunistic relaying technique by compensating the effects of scatterers, fluctuation parameter, and the number of propagation clusters, multicast users and eavesdroppers.展开更多
Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly in...Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.展开更多
The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of...The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of service(QoS) in terms of delay,reliability and security.Furthermore,the 5G network shall also incorporate high mobility requirements as an integral part,providing satisfactory service to users travelling at a speed up to 500 km/h.This paper provides a survey of potential high mobility wireless communication(HMWC) techniques for 5G network.After discussing the typical requirements and challenges of HMWC,key techniques to cope with the challenges are reviewed,including transmission techniques under the fast timevarying channels,network architecture with mobility support,and mobility management.Finally,future research directions on 5G high mobility communications are given.展开更多
为提高OFDM(Orthogonal Frequency Division Multiplexing)系统通信衰落信道估计性能,提出一种基于深度神经网络的信道估计模型DeReNet。通过串联深度密集网络和深度残差网络,抑制网络训练中出现的梯度爆炸和消失问题。将该模型与LS(Lea...为提高OFDM(Orthogonal Frequency Division Multiplexing)系统通信衰落信道估计性能,提出一种基于深度神经网络的信道估计模型DeReNet。通过串联深度密集网络和深度残差网络,抑制网络训练中出现的梯度爆炸和消失问题。将该模型与LS(Least Square)、FC-DNN(Full Connection Dense Neural Network)和SimNet(Simplified Deep Neural Networks)模型进行仿真实验对比,结果表明,在莱斯衰落环境下该模型的信道估计性能更好,能有效提高信道衰落的估计精度。展开更多
Using Markov model and the network simulator-NS, this paper studies the TCP throughput performance in wireless fading channel where the packets losses are always caused by high and burst errors. The results show that ...Using Markov model and the network simulator-NS, this paper studies the TCP throughput performance in wireless fading channel where the packets losses are always caused by high and burst errors. The results show that the burstiness in packet errors caused by slow multipath fading benefits Reno compared to i.i.d packet errors.展开更多
文摘In this paper, we study relay selection under outdated channel state information（CSI） in a decode-and-forward（DF） cooperative system. Unlike previous researches on cooperative communication under outdated CSI, we consider that the channel varies continuously over time, i.e., the channel not only changes between relay selection and data transmission but also changes during data transmission. Thus the level of accuracy of the CSI used in relay selection degrades with data transmission. We first evaluate the packet error rate（PER） of the cooperative system under continuous time-varying fading channel, and find that the PER performance deteriorates more seriously under continuous time-varying fading channel than when the channel is assumed to be constant during data transmission. Then, we propose a repeated relay selection（RRS） strategy to improve the PER performance, in which the forwarded data is divided into multiple segments and relay is reselected before the transmission of each segment based on the updated CSI. Finally, we propose a combined relay selection（CRS） strategy which takes advantage of three different relay selection strategies to further mitigate the impact of outdated CSI.
基金Supported by the National Natural Science Foundation of China (No.60772139)
文摘Decision fusion rules for Wireless Sensor Networks (WSNs) under Nakagami fading channels are investigated in this paper. Considering the application limitation of Likelihood Ratio Test fusion rule based on information of Channel Statistics using Series expansion (LRT-CSS),and the detection performance limitation of the Censoring based Mixed Fusion rule (CMF),a new LRT fusion rule based on information of channel statistics has been presented using Laplace approximation (LRT-CSL). Theoretical analysis and simulations show that the proposed fusion rule provides better detection performance than the Censoring based Mixed Fusion (CMF) and LRT-CSS fusion rules. Furthermore,compared with LRT-CSS fusion rule,the proposed fusion rule expands the application range of likelihood ratio test fusion rule.
文摘In this work we find a lower bound on the energy required for synchronizing moving sensor nodes in a Wireless Sensor Network (WSN) affected by large-scale fading, based on clock estimation techniques. The energy required for synchronizing a WSN within a desired estimation error level is specified by both the transmit power and the required number of messages. In this paper we extend our previous work introducing nodes’ movement and the average message delay in the total energy, including a comprehensive analysis on how the distance between nodes impacts on the energy and synchronization quality trade-off under large-scale fading effects.
文摘Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.
文摘In this work, the existing trade-off between time synchronization quality and energy is studied for both large-scale and small-scale fading wireless channels. We analyze the clock offset estimation problem using one-way, two-way and N-way message exchange mechanisms affected by Gaussian and exponentially distributed impairments. Our main contribution is a general relationship between the total energy required for synchronizing a wireless sensor network and the clock offset estimation error by means of the transmit power, number of transmitted messages and average message delay, deriving the energy optimal lower bound as a function of the time synchronization quality and the number of hops in a multi-hop network.
文摘Characterization of a mobile radio channel plays an important role in designing a reliable wireless communication system. Such channels are analyzed by two state model, namely satisfactory and outage state. This paper presents the analysis to estimate fading parameters of wireless channel with omission of certain outage durations which are considered as “Tolerance time”. Minimum outage duration which can be tolerated by a wireless fading channel to achieve desired packet error rate is defined as tolerance time. Normally a system with tolerable minimum outage time is analyzed based on Fade Duration Distribution (FDD) function over Rayleigh channel. In this paper Weibull function is used as FDD for varying tolerance time. The approach is simple and in general applicable from Rayleigh to Nakagami channels. The analysis is extended to study the effect of Tolerance time on channel fading statistics such as Average Fade Duration (AFD) and frequency of outage. Further the effects of various fade margin and Doppler spread on fading parameters are also investigated. The analysis can also be used in case of timeout expiration, connection resetting and congestion window control.
文摘The effects of scatterers, fluctuation parameter and propagation clusters significantly affect the performance of κ-μ shadowed fading channel. On the other hand, opportunistic relaying is an efficient technique to improve the performance of fading channels reducing the effects of aforementioned parameters. Motivated by these issues, in this paper, a secure wireless multicasting scenario through κ-μ shadowed fading channel is considered in the presence of multiple eavesdroppers with opportunistic relaying. The main purpose of this paper is to ensure the security level in wireless multicasting compensating the loss of security due to the effects of power ratio between dominant and scattered waves, fluctuation parameter, and the number of propagation clusters, multicast users and eavesdroppers, by opportunistic relaying technique. The closed-form analytical expressions are derived for the probability of non-zero secrecy multicast capacity (PNSMC) and the secure outage probability for multicasting (SOPM) to understand the insight of the effects of above parameters. The results show that the loss of security in multicasting through κ-μ shadowed fading channel can be significantly enhanced using opportunistic relaying technique by compensating the effects of scatterers, fluctuation parameter, and the number of propagation clusters, multicast users and eavesdroppers.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2022-00156299).
文摘Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.
基金supported by the National Basic Research Program of China (973 Program No.2012CB316100)
文摘The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of service(QoS) in terms of delay,reliability and security.Furthermore,the 5G network shall also incorporate high mobility requirements as an integral part,providing satisfactory service to users travelling at a speed up to 500 km/h.This paper provides a survey of potential high mobility wireless communication(HMWC) techniques for 5G network.After discussing the typical requirements and challenges of HMWC,key techniques to cope with the challenges are reviewed,including transmission techniques under the fast timevarying channels,network architecture with mobility support,and mobility management.Finally,future research directions on 5G high mobility communications are given.
文摘Using Markov model and the network simulator-NS, this paper studies the TCP throughput performance in wireless fading channel where the packets losses are always caused by high and burst errors. The results show that the burstiness in packet errors caused by slow multipath fading benefits Reno compared to i.i.d packet errors.
