Pub Date : 2018-10-01DOI: 10.1109/WCSP.2018.8555674
Yucheng Liao, Zhaojie Sun, L. Dan, Yue Xiao
Energy borrowing (EB) technique has been proposed recently to improve the performance of wireless communications, which also decreases the burden of the smart grid (SG). To further reduce the burden of SG, we propose an energy depositing (ED) strategy. Specifically, the energy harvesting (EH) device can deposit its unused energy in the SG for decreasing the burden of SG, and it also can extract the deposited energy with additional amount of energy as incentive. An EB-and-ED structure is also proposed to promote a more energy-efficient wireless system. This paper focuses on the ED process, a joint optimization of both ED policy and power scheduling for maximizing the system throughput has been formulated. The simulation results using real solar irradiance data confirm the effectiveness of the proposed ED strategy.
{"title":"Energy Depositing for Energy Harvesting Wireless Communications","authors":"Yucheng Liao, Zhaojie Sun, L. Dan, Yue Xiao","doi":"10.1109/WCSP.2018.8555674","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555674","url":null,"abstract":"Energy borrowing (EB) technique has been proposed recently to improve the performance of wireless communications, which also decreases the burden of the smart grid (SG). To further reduce the burden of SG, we propose an energy depositing (ED) strategy. Specifically, the energy harvesting (EH) device can deposit its unused energy in the SG for decreasing the burden of SG, and it also can extract the deposited energy with additional amount of energy as incentive. An EB-and-ED structure is also proposed to promote a more energy-efficient wireless system. This paper focuses on the ED process, a joint optimization of both ED policy and power scheduling for maximizing the system throughput has been formulated. The simulation results using real solar irradiance data confirm the effectiveness of the proposed ED strategy.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"49 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113942069","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.2018.8555588
Hong Chen, Dongmei Zhao, Qianbin Chen, Rong Chai
Mobile computation offloading (MCO) is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices to resource-rich devices (such as a cloud server) via wireless access. For applications that are time sensitive, offloading to nearby cloudlets is preferred, compared to offloading to a remote cloud server, in order to save the data transmission delay. On the other hand, the limited computing capabilities and the wireless transmission conditions to access the cloudlet servers can both affect the offloading performance, especially when multiple users are competing for offloading services. In this paper, we study joint computation offloading and radio resource allocations in small cell cellular systems, where cloudlet servers are colocated at the base stations. Our objective is to minimize the total energy consumption of the system, for both data transmissions and task executions, subject to the hard latency requirements of the applications. The problem is first formulated as a mixed integer nonlinear optimization problem, and then decomposed into multiple power allocation subproblems and an offloading decision subproblem. The power allocation subproblems are non-convex, which are reformulated and solved iteratively. Their results are fed into the offloading decision subproblem, which then becomes a linear integer (bi- nary) problem, and can be converted into a matching problem and solved using a modified Kuhn-Munkres (K-M) algorithm. Simulation results demonstrate that the joint optimization can significantly improve the offloading efficiency, compared to other resource allocation methods.
{"title":"Joint Computation Offloading and Radio Resource Allocations in Wireless Cellular Networks","authors":"Hong Chen, Dongmei Zhao, Qianbin Chen, Rong Chai","doi":"10.1109/WCSP.2018.8555588","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555588","url":null,"abstract":"Mobile computation offloading (MCO) is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices to resource-rich devices (such as a cloud server) via wireless access. For applications that are time sensitive, offloading to nearby cloudlets is preferred, compared to offloading to a remote cloud server, in order to save the data transmission delay. On the other hand, the limited computing capabilities and the wireless transmission conditions to access the cloudlet servers can both affect the offloading performance, especially when multiple users are competing for offloading services. In this paper, we study joint computation offloading and radio resource allocations in small cell cellular systems, where cloudlet servers are colocated at the base stations. Our objective is to minimize the total energy consumption of the system, for both data transmissions and task executions, subject to the hard latency requirements of the applications. The problem is first formulated as a mixed integer nonlinear optimization problem, and then decomposed into multiple power allocation subproblems and an offloading decision subproblem. The power allocation subproblems are non-convex, which are reformulated and solved iteratively. Their results are fed into the offloading decision subproblem, which then becomes a linear integer (bi- nary) problem, and can be converted into a matching problem and solved using a modified Kuhn-Munkres (K-M) algorithm. Simulation results demonstrate that the joint optimization can significantly improve the offloading efficiency, compared to other resource allocation methods.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"74 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":"134167904","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.2018.8555632
Nan Lu, Hongfeng Qin, Changyin Sun, Fan Jiang
In the ultra-dense network scenario, a joint power allocation scheme is proposed to maximize the sum rate of user-centric overlapped virtual cell systems. The optimal power allocation is firstly modeled with hierarchical framework and decomposed into two subproblems: power splitting and virtual power allocation, then it is solved by the alternating iteration between the two subproblems. The power splitting is obtained by an SLNR (signal to leakage plus noise ratio)-based algorithm with the introducing of Lagrangian function, and virtual power allocation is achieved by a binary iterative water-filling algorithm. As the power splitting coefficient takes the form of eigenvector which maximizes SLNR, a balanced effect on signal enhancement and interference reduction is achieved. Simulation results show that the proposed algorithm is superior to conventional power allocation algorithms in performance, as it effectively reduces interference and increases the sum rate of the system.
{"title":"Power Splitting and Virtual Power Allocation for Virtual Cell in Ultra-Dense Networks","authors":"Nan Lu, Hongfeng Qin, Changyin Sun, Fan Jiang","doi":"10.1109/WCSP.2018.8555632","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555632","url":null,"abstract":"In the ultra-dense network scenario, a joint power allocation scheme is proposed to maximize the sum rate of user-centric overlapped virtual cell systems. The optimal power allocation is firstly modeled with hierarchical framework and decomposed into two subproblems: power splitting and virtual power allocation, then it is solved by the alternating iteration between the two subproblems. The power splitting is obtained by an SLNR (signal to leakage plus noise ratio)-based algorithm with the introducing of Lagrangian function, and virtual power allocation is achieved by a binary iterative water-filling algorithm. As the power splitting coefficient takes the form of eigenvector which maximizes SLNR, a balanced effect on signal enhancement and interference reduction is achieved. Simulation results show that the proposed algorithm is superior to conventional power allocation algorithms in performance, as it effectively reduces interference and increases the sum rate of the system.","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":"115212266","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.2018.8555566
Ran Zhang, Hamid Saber, Yiqun Ge, Wuxian Shi
The paper studies rate matching for polar codes with Reed-Solomon (RS) kernels. A low-complexity rate matching scheme, referred to as smallest index puncturing, is put forward with validity proof. To resolve the dramatically increased complexity of reliability sequence generation due to rate matching, a piecewise sequence adaptation method is designed. The method significantly cuts down the computation complexity while keeping a negligible performance loss. Simulation results demonstrate the performance gain of the 4-dimension RS kernel over the original binary 2-by-2 kernel under rate matching, and verify the efficacy of the proposed piecewise method.
{"title":"Rate Matching and Piecewise Sequence Adaptation for Polar Codes with Reed-Solomon Kernels","authors":"Ran Zhang, Hamid Saber, Yiqun Ge, Wuxian Shi","doi":"10.1109/WCSP.2018.8555566","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555566","url":null,"abstract":"The paper studies rate matching for polar codes with Reed-Solomon (RS) kernels. A low-complexity rate matching scheme, referred to as smallest index puncturing, is put forward with validity proof. To resolve the dramatically increased complexity of reliability sequence generation due to rate matching, a piecewise sequence adaptation method is designed. The method significantly cuts down the computation complexity while keeping a negligible performance loss. Simulation results demonstrate the performance gain of the 4-dimension RS kernel over the original binary 2-by-2 kernel under rate matching, and verify the efficacy of the proposed piecewise method.","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":"114094253","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.2018.8555622
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}
In this paper, we study the problem of resource allocation in a cache-enabled software defined cellular network (SDCN) with mobile users, where the SDCN controller has global information of the network and the popular contents that the users request are stored at the content server and cache-enabled small base stations (SBSs). We propose a Markov chain based model to predict the users’ mobility patterns and then use the predicted mobility patterns to determine optimal resource allocation. The mobility prediction and resource allocation problem are jointly formulated as an optimization problem whose goal is to maximize the network throughput. Based on the predicted users’ mobility patterns, a distributed alternating direction method of multipliers (ADMM) is proposed to solve the resource allocation problem. The proposed ADMM algorithm enables the multiple SBSs implement their resource allocation simultaneously and, hence decreases the control overhead of the SDCN controller. Simulation results show that the proposed algorithm achieves up to 9.35% and 33.17% gains in terms of the average throughput compared to the random algorithm and the nearest association with equal allocated resource algorithm.
{"title":"Distributed Resource Allocation for Mobile Users in Cache-Enabled Software Defined Cellular Networks","authors":"Xiangqun Yang, Chunyu Pan, Mingzhe Chen, Changchuan Yin","doi":"10.1109/WCSP.2018.8555613","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555613","url":null,"abstract":"In this paper, we study the problem of resource allocation in a cache-enabled software defined cellular network (SDCN) with mobile users, where the SDCN controller has global information of the network and the popular contents that the users request are stored at the content server and cache-enabled small base stations (SBSs). We propose a Markov chain based model to predict the users’ mobility patterns and then use the predicted mobility patterns to determine optimal resource allocation. The mobility prediction and resource allocation problem are jointly formulated as an optimization problem whose goal is to maximize the network throughput. Based on the predicted users’ mobility patterns, a distributed alternating direction method of multipliers (ADMM) is proposed to solve the resource allocation problem. The proposed ADMM algorithm enables the multiple SBSs implement their resource allocation simultaneously and, hence decreases the control overhead of the SDCN controller. Simulation results show that the proposed algorithm achieves up to 9.35% and 33.17% gains in terms of the average throughput compared to the random algorithm and the nearest association with equal allocated resource algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"47 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":"121049657","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.2018.8555615
Leilei Huang, Rong Chai, Qianbin Chen, Chun Jin
The integration of software-defined networking (S-DN) and network function virtualization (NFV) is expected to achieve the convenient management and flexible deployment of various sophisticated network functions, and support user applications with guaranteed quality of service (QoS). In this paper, we jointly study route selection and network function placement problem. To stress the service sensitivity on delay, we formulate an optimization problem which minimizes the total end-to-end delay subject to data transmission, service requirement and various available resource constraints. As the formulated problem is an NP-hard problem, which cannot be solved easily, we transform it into three subproblems, i.e., route selection subproblem, network function placement subproblem and resource sharing subproblem of user flows, and solve the three subproblems by applying the K-shortest paths algorithm, Kuhn-Munkres (K-M) algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.
{"title":"End-to-End Delay Minimization based Joint Route Selection and Network Function Placement in SDN","authors":"Leilei Huang, Rong Chai, Qianbin Chen, Chun Jin","doi":"10.1109/WCSP.2018.8555615","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555615","url":null,"abstract":"The integration of software-defined networking (S-DN) and network function virtualization (NFV) is expected to achieve the convenient management and flexible deployment of various sophisticated network functions, and support user applications with guaranteed quality of service (QoS). In this paper, we jointly study route selection and network function placement problem. To stress the service sensitivity on delay, we formulate an optimization problem which minimizes the total end-to-end delay subject to data transmission, service requirement and various available resource constraints. As the formulated problem is an NP-hard problem, which cannot be solved easily, we transform it into three subproblems, i.e., route selection subproblem, network function placement subproblem and resource sharing subproblem of user flows, and solve the three subproblems by applying the K-shortest paths algorithm, Kuhn-Munkres (K-M) algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.","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":"131314567","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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