Pub Date : 2018-10-01DOI: 10.1109/WCSP.2018.8555622
Bin Wang, Jun Fang
The alternating direction method of multiplier (ADMM) is a popular method for solving composite convex minimization problems with separable linear constraints. Unfortunately, the direct extension of the ADMM for multi-block problems is not necessarily convergent. To address this issue, several variants of the ADMM were proposed, among which the parallel splitting algorithm has attracted much attention due to its efficiency and simplicity. However, a major drawback of the parallel splitting algorithm is that the weighting factor placed on the proximal term has to be greater than a certain value in order to ensure the convergence. A large weighting factor has the effect of forcing the current solution to stay close to its previous solution, thus leading to a slow convergence speed. In this paper, we propose a new hybrid type ADMM for multi-block separable convex programming. The proposed method places a much smaller weighting factor on the proximal term. Thus the proposed algorithm has the potential to achieve faster convergence rates. Numerical results are provided to illustrate the efficiency of the proposed algorithm.
{"title":"A Hybrid type ADMM for Multi-Block Separable Convex Programming","authors":"Bin Wang, Jun Fang","doi":"10.1109/WCSP.2018.8555622","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555622","url":null,"abstract":"The alternating direction method of multiplier (ADMM) is a popular method for solving composite convex minimization problems with separable linear constraints. Unfortunately, the direct extension of the ADMM for multi-block problems is not necessarily convergent. To address this issue, several variants of the ADMM were proposed, among which the parallel splitting algorithm has attracted much attention due to its efficiency and simplicity. However, a major drawback of the parallel splitting algorithm is that the weighting factor placed on the proximal term has to be greater than a certain value in order to ensure the convergence. A large weighting factor has the effect of forcing the current solution to stay close to its previous solution, thus leading to a slow convergence speed. In this paper, we propose a new hybrid type ADMM for multi-block separable convex programming. The proposed method places a much smaller weighting factor on the proximal term. Thus the proposed algorithm has the potential to achieve faster convergence rates. Numerical results are provided to illustrate the efficiency of the proposed algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117101725","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555912
Zhongsheng Sun, Jun Wang, Peng Lei, Zhaotao Qin
Classification of multiple walking people is researched based on radar micro-Doppler features in this paper. An architecture of deep convolutional neural networks without pooling layer is designed to extract the inherent features of micro-Doppler and complete the classification automatically without specific feature selection. The pooling layer is not used in the convolutional neural networks in order to preserve more subtle micro-Doppler features to improve the classification accuracy. The radar data of different types of pedestrians including one, two and three walking people are collected in the outdoor environment. Then the deep convolutional neural networks is trained with a small data set and the average accuracy of 95.55% is achieved.
{"title":"Multiple Walking People Classification with Convolutional Neural Networks Based on Micro-Doppler","authors":"Zhongsheng Sun, Jun Wang, Peng Lei, Zhaotao Qin","doi":"10.1109/WCSP.2018.8555912","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555912","url":null,"abstract":"Classification of multiple walking people is researched based on radar micro-Doppler features in this paper. An architecture of deep convolutional neural networks without pooling layer is designed to extract the inherent features of micro-Doppler and complete the classification automatically without specific feature selection. The pooling layer is not used in the convolutional neural networks in order to preserve more subtle micro-Doppler features to improve the classification accuracy. The radar data of different types of pedestrians including one, two and three walking people are collected in the outdoor environment. Then the deep convolutional neural networks is trained with a small data set and the average accuracy of 95.55% is achieved.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271922","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555918
Yinglei Teng, An Liu, V. Lau
In the ultra-dense network (UDN), terminals may be exposed to more frequent handover than ever due to smaller cell radius. Employing the handover protocol of the received signal strength with handover hysteresis (RSSH), the ping-pong effect can be significantly mitigated. In this work, we propose a stochastic geometry framework for handover analysis in UDNs and derive the theoretical expression for handover probability under such handover protocol. However, the handover probability becomes tricky to handle because the hysteresis margin makes the user association state strongly correlated, and UE does not any longer associate with the nearest BS consistently. Using the law of total probability, we derive the theoretical expression for handover probability by addressing its conditional probabilistic events of handover (HO) or non-handover $(overline{mathrm{H}mathrm{O}})$ in the former stage and obtain the simplified expression in the low mobility case. Both analytical and simulation results demonstrate the correctness and effectiveness of our analysis and show that higher hysteresis is tolerable for a denser network. Furthermore, the simplified expression for the special case of low mobility is shown to be quite accurate, and thus can be used to capture first-order insights for general cases.
{"title":"Stochastic Geometry based Handover Probability Analysis in Dense Cellular Networks","authors":"Yinglei Teng, An Liu, V. Lau","doi":"10.1109/WCSP.2018.8555918","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555918","url":null,"abstract":"In the ultra-dense network (UDN), terminals may be exposed to more frequent handover than ever due to smaller cell radius. Employing the handover protocol of the received signal strength with handover hysteresis (RSSH), the ping-pong effect can be significantly mitigated. In this work, we propose a stochastic geometry framework for handover analysis in UDNs and derive the theoretical expression for handover probability under such handover protocol. However, the handover probability becomes tricky to handle because the hysteresis margin makes the user association state strongly correlated, and UE does not any longer associate with the nearest BS consistently. Using the law of total probability, we derive the theoretical expression for handover probability by addressing its conditional probabilistic events of handover (HO) or non-handover $(overline{mathrm{H}mathrm{O}})$ in the former stage and obtain the simplified expression in the low mobility case. Both analytical and simulation results demonstrate the correctness and effectiveness of our analysis and show that higher hysteresis is tolerable for a denser network. Furthermore, the simplified expression for the special case of low mobility is shown to be quite accurate, and thus can be used to capture first-order insights for general cases.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509631","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555644
Chongchong Zhang, Fei Shen, Guowei Zhang, F. Qin, Feng Yan, P. Martins
As the big data era arrives, massive data traffic and applications generated by various terminal devices need to be processed in real time. To relieve the pressure of cloud computing on link congestion, delay, and energy consumption caused by the long distance between terminals and cloud server, the promising fog computing has been proposed. The fog network consisting of several fog clusters is considered, in which a fog controller $(Gamma mathrm{C})$ collects all the resource information of all its fog nodes (FNs). In order to better serve the terminal nodes, different FCs are willing to exchange the information of their FNs and share their services to some extent. Therefore, in this paper, we propose a novel incentive framework for collaborative sensing to motivate the fog cluster to provide service for other fog clusters. The SRs use the computation reward prices to motivate the SP to provide more computational capability to complete the tasks. The utility functions of the SRs and the SP are proposed, considering the payment for task computation, the task delay and the computation cost. The existences of the global optimums of both the utilities for the SRs the SP are proved. Numerous simulations verify our theoretical analyses and indicate the importance of our proposed incentive framework for collaborative sensing between fog clusters subscribed to different mobile providers in the fog network.
{"title":"An Incentive Framework for Collaborative Sensing in Fog Networks","authors":"Chongchong Zhang, Fei Shen, Guowei Zhang, F. Qin, Feng Yan, P. Martins","doi":"10.1109/WCSP.2018.8555644","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555644","url":null,"abstract":"As the big data era arrives, massive data traffic and applications generated by various terminal devices need to be processed in real time. To relieve the pressure of cloud computing on link congestion, delay, and energy consumption caused by the long distance between terminals and cloud server, the promising fog computing has been proposed. The fog network consisting of several fog clusters is considered, in which a fog controller $(Gamma mathrm{C})$ collects all the resource information of all its fog nodes (FNs). In order to better serve the terminal nodes, different FCs are willing to exchange the information of their FNs and share their services to some extent. Therefore, in this paper, we propose a novel incentive framework for collaborative sensing to motivate the fog cluster to provide service for other fog clusters. The SRs use the computation reward prices to motivate the SP to provide more computational capability to complete the tasks. The utility functions of the SRs and the SP are proposed, considering the payment for task computation, the task delay and the computation cost. The existences of the global optimums of both the utilities for the SRs the SP are proved. Numerous simulations verify our theoretical analyses and indicate the importance of our proposed incentive framework for collaborative sensing between fog clusters subscribed to different mobile providers in the fog network.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124083509","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555922
Yuting Wang, Peng Long, Nan Liu, Zhiwen Pan, X. You
Outage detection in wireless networks is a significant problem of self-healing in SON. In this paper, we propose a cooperative outage detection paradigm using the RBF neural network improved by a genetic artificial bee colony(IRBFG) algorithm for global optimum of neural network parameters and better classification of nonlinear user data. Spatial and temporal features are selected through an improved decision tree base learner for better performance. The simulation results demonstrate that the proposed scheme receives higher detection accuracy and reduces data transmission, especially in the dense small cell network environment.
{"title":"A Cooperative Outage Detection Approach Using an Improved RBF Neural Network with Genetic ABC Algorithm","authors":"Yuting Wang, Peng Long, Nan Liu, Zhiwen Pan, X. You","doi":"10.1109/WCSP.2018.8555922","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555922","url":null,"abstract":"Outage detection in wireless networks is a significant problem of self-healing in SON. In this paper, we propose a cooperative outage detection paradigm using the RBF neural network improved by a genetic artificial bee colony(IRBFG) algorithm for global optimum of neural network parameters and better classification of nonlinear user data. Spatial and temporal features are selected through an improved decision tree base learner for better performance. The simulation results demonstrate that the proposed scheme receives higher detection accuracy and reduces data transmission, especially in the dense small cell network environment.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129021921","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}
In vehicular networks, Mobile Edge Computing (MEC) is applied to meet the offloading demand from vehicles. However, the mobility of vehicles may increase the offloading delay and even reduce the success rate of offloading, because vehicles may access another road side unit (RSU) before finishing offloading. Therefore, an offloading algorithm with low time complexity is required to make the offloading decision quickly. In this paper, we put forward an efficient offloading algorithm based on Support Vector Machine (SVMO) to satisfy the fast offloading demand in vehicular networks. The algorithm can segment a huge task into several sub-tasks through a weight allocation method according to available resources of MEC servers. Then each sub-task is decided whether it should be offloaded or executed locally based on SVMs. As the vehicle moves through several MEC servers, sub-tasks are allocated to them by order if they are offloaded. Each server ensures the sub-task can be processed and returned in time. Our proposed algorithm generate training data through Decision Tree. The simulation results show that the SVMO algorithm has a high decision accuracy, converges faster than other algorithms and has a small response time.
{"title":"An Efficient Offloading Algorithm Based on Support Vector Machine for Mobile Edge Computing in Vehicular Networks","authors":"Siyun Wu, Weiwei Xia, Wenqing Cui, Chao Qian, Zhuorui Lan, Feng Yan, Lianfeng Shen","doi":"10.1109/WCSP.2018.8555695","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555695","url":null,"abstract":"In vehicular networks, Mobile Edge Computing (MEC) is applied to meet the offloading demand from vehicles. However, the mobility of vehicles may increase the offloading delay and even reduce the success rate of offloading, because vehicles may access another road side unit (RSU) before finishing offloading. Therefore, an offloading algorithm with low time complexity is required to make the offloading decision quickly. In this paper, we put forward an efficient offloading algorithm based on Support Vector Machine (SVMO) to satisfy the fast offloading demand in vehicular networks. The algorithm can segment a huge task into several sub-tasks through a weight allocation method according to available resources of MEC servers. Then each sub-task is decided whether it should be offloaded or executed locally based on SVMs. As the vehicle moves through several MEC servers, sub-tasks are allocated to them by order if they are offloaded. Each server ensures the sub-task can be processed and returned in time. Our proposed algorithm generate training data through Decision Tree. The simulation results show that the SVMO algorithm has a high decision accuracy, converges faster than other algorithms and has a small response time.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129369693","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555863
Fang Dong, Tianyu Wang, Shaowei Wang
Caching at base stations (BSs) is a promising scheme to alleviate the traffic burden in mobile communication systems. In this paper, we aim to minimize the average delay of all users in the cache-enabled mobile network where the BSs can exchange data with each other via X2 interface. We jointly consider cache placement and user association problems and employ graph theory to deal with the optimization task. For a given network graph, we aim to find the maximum cliques and place different files in the maximum clique so as to improve local cache hit probability. In the user association procedure, we make the BSs which store the requested files of users serve these users as many as possible. Simulation results show that our proposed algorithm yields the lowest delay among the other representative algorithms.
{"title":"Graph-Theoretic Approach for Cache Placement and Delay optimization in Cache-Enabled Mobile Networks","authors":"Fang Dong, Tianyu Wang, Shaowei Wang","doi":"10.1109/WCSP.2018.8555863","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555863","url":null,"abstract":"Caching at base stations (BSs) is a promising scheme to alleviate the traffic burden in mobile communication systems. In this paper, we aim to minimize the average delay of all users in the cache-enabled mobile network where the BSs can exchange data with each other via X2 interface. We jointly consider cache placement and user association problems and employ graph theory to deal with the optimization task. For a given network graph, we aim to find the maximum cliques and place different files in the maximum clique so as to improve local cache hit probability. In the user association procedure, we make the BSs which store the requested files of users serve these users as many as possible. Simulation results show that our proposed algorithm yields the lowest delay among the other representative algorithms.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130666318","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555703
Liyuan Song, Tingting Zhang, Qinyu Zhang
Target detection and positioning in harsh environments plays a main role in wireless radar sensor networks (WRSNs). Due to the difficulties in high accuracy clock synchronization among multiple sensor nodes, a novel asynchronous measurement model based on a combination of round trip measurements (RTM) and time difference of arrival (TDOA) is presented in this paper. We then investigate the fundamental accuracy limits of target localization based on the RTM-TDOA model. In order to achieve the tradeoff between the target localization accuracy and energy consumption, an optimal power allocation framework among the sensor nodes is thus presented. Furthermore, a corresponding robust power allocation strategy is also given to deal with the position uncertainty of the target. Both power allocation problems are proved to be convex and can be solved efficiently. We validate the analysis, and evaluate the performance of the proposed strategies through numerical results. Meaningful performance benchmarks can also be achieved by the presented frameworks in this paper.
{"title":"Power Allocation for Target Positioning in Asynchronous Wireless Radar Sensor Networks","authors":"Liyuan Song, Tingting Zhang, Qinyu Zhang","doi":"10.1109/WCSP.2018.8555703","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555703","url":null,"abstract":"Target detection and positioning in harsh environments plays a main role in wireless radar sensor networks (WRSNs). Due to the difficulties in high accuracy clock synchronization among multiple sensor nodes, a novel asynchronous measurement model based on a combination of round trip measurements (RTM) and time difference of arrival (TDOA) is presented in this paper. We then investigate the fundamental accuracy limits of target localization based on the RTM-TDOA model. In order to achieve the tradeoff between the target localization accuracy and energy consumption, an optimal power allocation framework among the sensor nodes is thus presented. Furthermore, a corresponding robust power allocation strategy is also given to deal with the position uncertainty of the target. Both power allocation problems are proved to be convex and can be solved efficiently. We validate the analysis, and evaluate the performance of the proposed strategies through numerical results. Meaningful performance benchmarks can also be achieved by the presented frameworks in this paper.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130624871","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555584
Bin Zhou, Qing Wang, H. Quan
The problem of grating lobes false alarm is easy to occur in Direction of Arrival (DoA) estimation of large array with nonuniform spacing. The sparse spatial signal reconstruction can be used to suppress the grating lobes. This paper applied the Sparse Bayesian Learning (SBL) algorithm in DOA estimation for large spacing array. To solve the steering vector mismatch problem, the concept of peak confidence is proposed. Utilizing the confidence function to screen the peaks, the false alarms of grating lobes are avoided effectively. The effect of different input k value on the grating lobes suppression performance is analyzed, and the threshold selection in practical application is given. Simulation and sea trial data results confirm the grating lobes suppression performance of the proposed algorithm.
{"title":"DOA estimation for large array with nonuniform spacing based on sparse representation","authors":"Bin Zhou, Qing Wang, H. Quan","doi":"10.1109/WCSP.2018.8555584","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555584","url":null,"abstract":"The problem of grating lobes false alarm is easy to occur in Direction of Arrival (DoA) estimation of large array with nonuniform spacing. The sparse spatial signal reconstruction can be used to suppress the grating lobes. This paper applied the Sparse Bayesian Learning (SBL) algorithm in DOA estimation for large spacing array. To solve the steering vector mismatch problem, the concept of peak confidence is proposed. Utilizing the confidence function to screen the peaks, the false alarms of grating lobes are avoided effectively. The effect of different input k value on the grating lobes suppression performance is analyzed, and the threshold selection in practical application is given. Simulation and sea trial data results confirm the grating lobes suppression performance of the proposed algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660560","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 : 2018-10-01DOI: 10.1109/WCSP.2018.8555643
Shan Zhang, Junjie Li, Hongbin Luo, Jie Gao, Lian Zhao, Xuemin Shen
Mobile edge caching which exploits the similarity in content requests to reduce duplicated transmissions, is considered as an effective solution to address the challenge of increasing mobile traffic demand and constrained radio resources. Different from conventional networks, vehicular networks are highly dynamic, and thus the cached contents should update timely to guarantee the freshness of vehicle received information. However, content update also consumes radio resource and results in a tradeoff between content freshness and service latency, calling for the joint optimization of content update, delivery, and radio resource allocation. To address this issue, this work proposes a cache-assisted lazy update and delivery (CALUD) scheme to balance content freshness and service latency in vehicular networks. Firstly, the age of information (AoI) and service latency of vehicular-received contents are derived in closed form under the CALUD scheme. Then, the CALUD scheme is further optimized jointly with the radio resource allocation from the network aspect to meet the diversified service latency and AoI requirements of different applications. Extensive simulations are conducted to validate the theoretical analysis using the OMNET++ simulator. The results demonstrate that the proposed CALUD scheme can reduce the service latency to milliseconds while guaranteeing the required content freshness.
{"title":"Towards Fresh and Low-Latency Content Delivery in Vehicular Networks: An Edge Caching Aspect","authors":"Shan Zhang, Junjie Li, Hongbin Luo, Jie Gao, Lian Zhao, Xuemin Shen","doi":"10.1109/WCSP.2018.8555643","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555643","url":null,"abstract":"Mobile edge caching which exploits the similarity in content requests to reduce duplicated transmissions, is considered as an effective solution to address the challenge of increasing mobile traffic demand and constrained radio resources. Different from conventional networks, vehicular networks are highly dynamic, and thus the cached contents should update timely to guarantee the freshness of vehicle received information. However, content update also consumes radio resource and results in a tradeoff between content freshness and service latency, calling for the joint optimization of content update, delivery, and radio resource allocation. To address this issue, this work proposes a cache-assisted lazy update and delivery (CALUD) scheme to balance content freshness and service latency in vehicular networks. Firstly, the age of information (AoI) and service latency of vehicular-received contents are derived in closed form under the CALUD scheme. Then, the CALUD scheme is further optimized jointly with the radio resource allocation from the network aspect to meet the diversified service latency and AoI requirements of different applications. Extensive simulations are conducted to validate the theoretical analysis using the OMNET++ simulator. The results demonstrate that the proposed CALUD scheme can reduce the service latency to milliseconds while guaranteeing the required content freshness.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122572168","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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