Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118721
Seungnyun Kim, Jiao Wu, B. Shim
The main purpose of this paper is to propose an effective precoding technique for the frequency-division-duplexing (FDD)-based massive MIMO systems under the common scattering effect. Key idea of the proposed path-selective precoding (PSP) scheme is to choose a small paths maximizing the data rate and then exploit only the angle information of the chosen paths for the downlink data precoding. To efficiently select the paths for each mobile, we use the notion of leakage, a metric of how much signal power leaks into other mobiles. While the interference is a joint function of precoding vectors of different mobiles, the leakage is solely a function of the precoding vector of corresponding mobile so that the signal-to-leakage-and-noise-ratio (SLNR) maximization problem can be decoupled into the sub-problems for each mobile. To find out a near-optimal solution of the decoupled SLNR maximization problem, we propose a greedy algorithm that iteratively removes the index of shared paths from the candidate index set until the SLNR does not increase. We demonstrate from the simulation results that the proposed PSP scheme achieves the significant data rate gains over the conventional angular-domain precoding schemes.
{"title":"Path-Selective Precoding for FDD-based Massive MIMO Systems","authors":"Seungnyun Kim, Jiao Wu, B. Shim","doi":"10.1109/WCNC55385.2023.10118721","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118721","url":null,"abstract":"The main purpose of this paper is to propose an effective precoding technique for the frequency-division-duplexing (FDD)-based massive MIMO systems under the common scattering effect. Key idea of the proposed path-selective precoding (PSP) scheme is to choose a small paths maximizing the data rate and then exploit only the angle information of the chosen paths for the downlink data precoding. To efficiently select the paths for each mobile, we use the notion of leakage, a metric of how much signal power leaks into other mobiles. While the interference is a joint function of precoding vectors of different mobiles, the leakage is solely a function of the precoding vector of corresponding mobile so that the signal-to-leakage-and-noise-ratio (SLNR) maximization problem can be decoupled into the sub-problems for each mobile. To find out a near-optimal solution of the decoupled SLNR maximization problem, we propose a greedy algorithm that iteratively removes the index of shared paths from the candidate index set until the SLNR does not increase. We demonstrate from the simulation results that the proposed PSP scheme achieves the significant data rate gains over the conventional angular-domain precoding schemes.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133461491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118795
Qian Xu, Jianyong Sun
In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.
{"title":"A Two-Stage Majorization-Minimization Based Beamforming for Downlink Massive MIMO","authors":"Qian Xu, Jianyong Sun","doi":"10.1109/WCNC55385.2023.10118795","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118795","url":null,"abstract":"In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10119121
Giovanni Interdonato, S. Buzzi, C. D’Andrea, L. Venturino
The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is investigated in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative coexistence strategies to deal with the inter-service interference. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect channel state information knowledge at the base stations, pilot-based estimation overhead, spatially correlated channels, and the structure of the radio frame. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.
{"title":"Non-Orthogonal Multiplexing of eMBB and URLLC in Multi-cell Massive MIMO","authors":"Giovanni Interdonato, S. Buzzi, C. D’Andrea, L. Venturino","doi":"10.1109/WCNC55385.2023.10119121","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119121","url":null,"abstract":"The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is investigated in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative coexistence strategies to deal with the inter-service interference. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect channel state information knowledge at the base stations, pilot-based estimation overhead, spatially correlated channels, and the structure of the radio frame. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122049835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118809
Xuechen Xie, Dongheng Zhang, Yadong Li, Jinbo Chen, Yang Hu, Qibin Sun, Yan Chen
The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.
{"title":"Robust Respiration Sensing with WiFi","authors":"Xuechen Xie, Dongheng Zhang, Yadong Li, Jinbo Chen, Yang Hu, Qibin Sun, Yan Chen","doi":"10.1109/WCNC55385.2023.10118809","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118809","url":null,"abstract":"The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116209894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118714
Sara Norouzi, B. Champagne
As an enabling technology for emerging and future generations of wireless networks, sparse code multiple access (SCMA) offers major improvements in terms of spectral efficiency and massive connectivity. Although the message passing algorithm (MPA) for SCMA decoding at the receiver side can achieve near optimum performance, it entails high computational complexity. In this paper, to address this issue, we propose a novel SCMA decoder based on deep residual neural network (ResNet), wherein the decoder is trained to predict the transmit codewords. In our approach, residual blocks are employed to tackle the problems of accuracy saturation and vanishing gradients with deep learning based decoder, while batch normalization is utilized to enhance the stability and robustness of the decoder. The performance of the proposed ResNet decoder for SCMA is validated by means of simulations over AWGN and Rayleigh fading channels. The results show that besides a much reduced complexity, the proposed decoder leads to improvements in term of bit error rate (BER) over competing deep neural network (DNN) based decoders.
{"title":"Deep Residual Neural Network Decoder for Sparse Code Multiple Access","authors":"Sara Norouzi, B. Champagne","doi":"10.1109/WCNC55385.2023.10118714","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118714","url":null,"abstract":"As an enabling technology for emerging and future generations of wireless networks, sparse code multiple access (SCMA) offers major improvements in terms of spectral efficiency and massive connectivity. Although the message passing algorithm (MPA) for SCMA decoding at the receiver side can achieve near optimum performance, it entails high computational complexity. In this paper, to address this issue, we propose a novel SCMA decoder based on deep residual neural network (ResNet), wherein the decoder is trained to predict the transmit codewords. In our approach, residual blocks are employed to tackle the problems of accuracy saturation and vanishing gradients with deep learning based decoder, while batch normalization is utilized to enhance the stability and robustness of the decoder. The performance of the proposed ResNet decoder for SCMA is validated by means of simulations over AWGN and Rayleigh fading channels. The results show that besides a much reduced complexity, the proposed decoder leads to improvements in term of bit error rate (BER) over competing deep neural network (DNN) based decoders.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118961
Sravani Kurma, Keshav Singh, P. Sharma, Chih-Peng Li
Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.
{"title":"DRL Approach for Spectral-Energy Trade-off in RIS-assisted Full-duplex Multi-user MIMO Systems","authors":"Sravani Kurma, Keshav Singh, P. Sharma, Chih-Peng Li","doi":"10.1109/WCNC55385.2023.10118961","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118961","url":null,"abstract":"Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121853117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118689
Xuanyu Li, K. Niu, Jincheng Dai, Zhi-Wei Tan, Zhiheng Guo
Guessing random additive noise decoding (GRAND) is a recently proposed decoding algorithm which can achieve the error performance of maximum likelihood (ML) decoding. However, GRAND and its variants are only suitable for some short codes with high code rates and have large average query numbers. To mitigate these problems, we propose a successive cancellation list (SCL)-GRAND decoding algorithm for the cyclic redundancy check concatenated polar (CRC-polar) codes. The proposed decoder first divides the received sequence into two subblocks. Then SCL is used to decode the upper subblock and output several candidates into the candidate list. For each candidate, GRAND is used to decode the lower subblock and finally choose the most-likely codeword as the decoded result. Since the SCL is integrated into the SCL-GRAND algorithm, this algorithm can achieve lower complexity and better flexibility than the original GRAND.
{"title":"SCL-GRAND: Lower complexity and better flexibility for CRC-Polar Codes","authors":"Xuanyu Li, K. Niu, Jincheng Dai, Zhi-Wei Tan, Zhiheng Guo","doi":"10.1109/WCNC55385.2023.10118689","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118689","url":null,"abstract":"Guessing random additive noise decoding (GRAND) is a recently proposed decoding algorithm which can achieve the error performance of maximum likelihood (ML) decoding. However, GRAND and its variants are only suitable for some short codes with high code rates and have large average query numbers. To mitigate these problems, we propose a successive cancellation list (SCL)-GRAND decoding algorithm for the cyclic redundancy check concatenated polar (CRC-polar) codes. The proposed decoder first divides the received sequence into two subblocks. Then SCL is used to decode the upper subblock and output several candidates into the candidate list. For each candidate, GRAND is used to decode the lower subblock and finally choose the most-likely codeword as the decoded result. Since the SCL is integrated into the SCL-GRAND algorithm, this algorithm can achieve lower complexity and better flexibility than the original GRAND.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121990750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118908
Marwan Dhuheir, A. Erbad, Sinan Sabeeh
Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.
{"title":"LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms","authors":"Marwan Dhuheir, A. Erbad, Sinan Sabeeh","doi":"10.1109/WCNC55385.2023.10118908","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118908","url":null,"abstract":"Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122080562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118859
Xiao Zhang, Tao Peng, Yichen Guo, Wenbo Wang
The fifth-generation (5G) wireless network is expected to support emerging applications requiring ultra high reliability and low latency, such as self-driving cars, factory automation (industry 4.0), and smart grid, known as ultra-reliable and low-latency communications (URLLC). Uplink grant-free (GF) transmission is considered as a promising technology for supporting the rigorous requirements of URLLC by saving the time of requesting/waiting for the scheduling grant and supporting the K-repetition transmission. Besides, the intercell interference (ICI) in uplink multi-cell GF transmission is another critical issue to be solved. In this paper, we propose an interference-aware based radio resource configuration framework of URLLC uplink GF transmission which means that we can configure the radio resources by utilizing the available interference information to mitigate the impact of severe ICI on the transmission performance in URLLC. Numerical results show that, the proposed scheme can greatly improve the total transmission reliability and has higher scalability and robustness compared to prior art solutions under the condition of satisfying the transmission delay requirement and resource constraint.
{"title":"Interference-Aware Based Resource Configuration Optimization for URLLC Grant-Free Transmission","authors":"Xiao Zhang, Tao Peng, Yichen Guo, Wenbo Wang","doi":"10.1109/WCNC55385.2023.10118859","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118859","url":null,"abstract":"The fifth-generation (5G) wireless network is expected to support emerging applications requiring ultra high reliability and low latency, such as self-driving cars, factory automation (industry 4.0), and smart grid, known as ultra-reliable and low-latency communications (URLLC). Uplink grant-free (GF) transmission is considered as a promising technology for supporting the rigorous requirements of URLLC by saving the time of requesting/waiting for the scheduling grant and supporting the K-repetition transmission. Besides, the intercell interference (ICI) in uplink multi-cell GF transmission is another critical issue to be solved. In this paper, we propose an interference-aware based radio resource configuration framework of URLLC uplink GF transmission which means that we can configure the radio resources by utilizing the available interference information to mitigate the impact of severe ICI on the transmission performance in URLLC. Numerical results show that, the proposed scheme can greatly improve the total transmission reliability and has higher scalability and robustness compared to prior art solutions under the condition of satisfying the transmission delay requirement and resource constraint.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118783
Wenlin Yao, Jiayi Liu, Chen Wang, Qinghai Yang
In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.
{"title":"Learning-based RSU Placement for C-V2X with Uncertain Traffic Density and Task Demand","authors":"Wenlin Yao, Jiayi Liu, Chen Wang, Qinghai Yang","doi":"10.1109/WCNC55385.2023.10118783","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118783","url":null,"abstract":"In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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