Pub Date : 2018-10-01DOI: 10.1109/WCSP.2018.8555660
Chaozhun Wen, Peng Yang, Qiong Liu, Jingjing Luo, Li Yu
In software defined networks, network states are frequently updated by controllers. Unfortunately, due to resource and time constraints, there are scenarios in which transient congestion and packet loss are inevitable. In this regard, minimizing the packet loss ratio becomes crucial. Previous efforts on congestion-free updates suggest link-based solutions, which aim at minimizing the overloaded data volume on the bottleneck links. Observing the fact that the least overloaded data volume on links still does not guarantee the least packet loss, in this paper, we propose a flow-based update solution that directly minimizes the packet loss by jointly optimizing the congestion duration and rate limitation. Specifically, congestion impairment is defined to jointly accommodate the flow's importance and packet loss. Then, we present the FBU (Flow-Based Update problem), which minimizes the congestion impairment on a flow basis. To deal with the NP-hardness of this optimization problem, we propose MIC, which is an efficient two-phase heuristic algorithm based on the relationship between rate limitation, congestion duration and packet loss. Experimental results show that MIC can reduce up to 84% of packet loss compared to previous algorithms.
{"title":"Minimizing Congestion Impairment of Network Update in SDN: A Flow-Based Solution","authors":"Chaozhun Wen, Peng Yang, Qiong Liu, Jingjing Luo, Li Yu","doi":"10.1109/WCSP.2018.8555660","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555660","url":null,"abstract":"In software defined networks, network states are frequently updated by controllers. Unfortunately, due to resource and time constraints, there are scenarios in which transient congestion and packet loss are inevitable. In this regard, minimizing the packet loss ratio becomes crucial. Previous efforts on congestion-free updates suggest link-based solutions, which aim at minimizing the overloaded data volume on the bottleneck links. Observing the fact that the least overloaded data volume on links still does not guarantee the least packet loss, in this paper, we propose a flow-based update solution that directly minimizes the packet loss by jointly optimizing the congestion duration and rate limitation. Specifically, congestion impairment is defined to jointly accommodate the flow's importance and packet loss. Then, we present the FBU (Flow-Based Update problem), which minimizes the congestion impairment on a flow basis. To deal with the NP-hardness of this optimization problem, we propose MIC, which is an efficient two-phase heuristic algorithm based on the relationship between rate limitation, congestion duration and packet loss. Experimental results show that MIC can reduce up to 84% of packet loss compared to previous algorithms.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"33 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":"130057141","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.2018.8555570
S. Pandav, P. Ubaidulla
Two dimensional (2-D) direction of arrival (DOA) estimation is a fundamental problem in array signal processing with wide range of applications. In this paper, an approach to 2-D DOA estimation with a modified parallel coprime linear subarrays using MUSIC algorithm and least squares is proposed. A virtual difference co-array is synthesized by vectorizing the crosscovariance matrix of sub-array data. In the proposed method, the 2-D DOA estimation problem is decomposed as two one dimensional (1-D) DOA estimation problems in which azimuth and elevation DOAs are estimated and automatically paired using MUSIC and Least Squares (LS). Simulation results are presented to verify the performance of the proposed method and the improvements resulting from the proposed array geometry.
{"title":"Two-Dimensional DOA Estimation with Modified Parallel Coprime Linear Sub-Arrays","authors":"S. Pandav, P. Ubaidulla","doi":"10.1109/WCSP.2018.8555570","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555570","url":null,"abstract":"Two dimensional (2-D) direction of arrival (DOA) estimation is a fundamental problem in array signal processing with wide range of applications. In this paper, an approach to 2-D DOA estimation with a modified parallel coprime linear subarrays using MUSIC algorithm and least squares is proposed. A virtual difference co-array is synthesized by vectorizing the crosscovariance matrix of sub-array data. In the proposed method, the 2-D DOA estimation problem is decomposed as two one dimensional (1-D) DOA estimation problems in which azimuth and elevation DOAs are estimated and automatically paired using MUSIC and Least Squares (LS). Simulation results are presented to verify the performance of the proposed method and the improvements resulting from the proposed array geometry.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"5 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":"121604138","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}
Pub Date : 2018-10-01DOI: 10.1109/WCSP.2018.8555916
Zhengming Zhang, Yaru Zheng, Chunguo Li, Yongming Huang, Luxi Yang
Caching and rate allocation are two promising approaches to support video streaming over wireless networks. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled video rate allocation. We establish a mathematical model for this problem, and point out that it is difficult to solve it with traditional dynamic programming. Then we propose a deep reinforcement learning approach to solve it. Firstly, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out an effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality of service. We also investigate the impact of critical parameters on the performance of our algorithm.
{"title":"Cache-Enabled Adaptive Bit Rate Streaming via Deep Self-Transfer Reinforcement Learning","authors":"Zhengming Zhang, Yaru Zheng, Chunguo Li, Yongming Huang, Luxi Yang","doi":"10.1109/WCSP.2018.8555916","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555916","url":null,"abstract":"Caching and rate allocation are two promising approaches to support video streaming over wireless networks. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled video rate allocation. We establish a mathematical model for this problem, and point out that it is difficult to solve it with traditional dynamic programming. Then we propose a deep reinforcement learning approach to solve it. Firstly, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out an effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality of service. We also investigate the impact of critical parameters on the performance of our algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"43 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":"116469603","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}
Pub Date : 2018-10-01DOI: 10.1109/WCSP.2018.8555621
Zhenyuan Wang, Jianjun Wei, Xiaohui Li, Zelin Liu, F. Su
This paper proposes a new fetal monitoring system, including the acquisition and the processing of fetal heart sounds (FHS). Based on the foundation of the stethoscope principle, a single-channel, non-invasive sensor is designed to acquire the fetal heart sounds, in which polyvinylidene fluoride (PVDF) membrane material is used as the core transducer. In the fetal heart sounds processing part, we propose a new method for denoising based on adaptive support vector regression (SVR) which has a good performance on curve fitting and effectively weakens the interference of additive noise. Thus, the clean fetal heart signals extracted from the interfered source can be further utilized to draw the fetal phonocardiogram (FPCG) and calculate the fetal heart rate (FHR).
{"title":"Adaptive SVR Denoising Algorithm for Fetal Monitoring System","authors":"Zhenyuan Wang, Jianjun Wei, Xiaohui Li, Zelin Liu, F. Su","doi":"10.1109/WCSP.2018.8555621","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555621","url":null,"abstract":"This paper proposes a new fetal monitoring system, including the acquisition and the processing of fetal heart sounds (FHS). Based on the foundation of the stethoscope principle, a single-channel, non-invasive sensor is designed to acquire the fetal heart sounds, in which polyvinylidene fluoride (PVDF) membrane material is used as the core transducer. In the fetal heart sounds processing part, we propose a new method for denoising based on adaptive support vector regression (SVR) which has a good performance on curve fitting and effectively weakens the interference of additive noise. Thus, the clean fetal heart signals extracted from the interfered source can be further utilized to draw the fetal phonocardiogram (FPCG) and calculate the fetal heart rate (FHR).","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"14 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":"132620602","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.2018.8555863
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}
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