Pub Date : 2019-06-01DOI: 10.1109/IWCMC.2019.8766560
Faten Hajjej, Monia Hamdi, R. Ejbali, M. Zaied
Wireless Sensor Networks (WSNs) are scalable research domain with a multitude of application contexts. Sensor nodes deployment is a decisive step that has a major impact on the performance of the network, since it directly influences the cost, the sensing capability and even the WSNs lifetimes. In this paper, we are interested in the placement problem of sensor nodes for WSNs. First, the issue is formulated as constrained multi-objective optimization problem (MOOP). Then, a novel approach based on Multi-Objective Flower Pollination Algorithm (MOFPA) was proposed. This new method aimed to approximate optimal trade-offs among multiple objective functions, which are enhancing the coverage, reducing the network energy dissipation, maximizing the network lifetime and maintaining the connectivity. Finally, we compared the proposed approach with two popular algorithms, namely, the classic Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The simulation experiments show that our approach outperforms PSO and NSGA-II.
{"title":"A New Optimal Deployment Model of Internet of Things Based on Wireless Sensor Networks","authors":"Faten Hajjej, Monia Hamdi, R. Ejbali, M. Zaied","doi":"10.1109/IWCMC.2019.8766560","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766560","url":null,"abstract":"Wireless Sensor Networks (WSNs) are scalable research domain with a multitude of application contexts. Sensor nodes deployment is a decisive step that has a major impact on the performance of the network, since it directly influences the cost, the sensing capability and even the WSNs lifetimes. In this paper, we are interested in the placement problem of sensor nodes for WSNs. First, the issue is formulated as constrained multi-objective optimization problem (MOOP). Then, a novel approach based on Multi-Objective Flower Pollination Algorithm (MOFPA) was proposed. This new method aimed to approximate optimal trade-offs among multiple objective functions, which are enhancing the coverage, reducing the network energy dissipation, maximizing the network lifetime and maintaining the connectivity. Finally, we compared the proposed approach with two popular algorithms, namely, the classic Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The simulation experiments show that our approach outperforms PSO and NSGA-II.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124207784","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766681
Hamid Nouasria, Mohamed Et-tolba, Abla Bedoui
Compressive sensing is a new methodology to reconstruct sparse signals from a few number of measurements. In this paper, we propose new sensing matrices for compressive sensing using orthogonal Hadamard matrix. The conventional sensing matrices based on orthogonal Hadamard matrix give acceptable performance but have some drawbacks. In contrast, the proposed sensing matrices are more suitable to compressive sensing compared with the conventional ones. Because they surpass their conventional drawbacks and give higher performance simultaneously. Extensive simulations on both synthesized signals and real images are conducted to show the power of the proposed sensing matrices compared with the conventional ones using convex optimization algorithms.
{"title":"New Sensing Matrices Based On Orthogonal Hadamard Matrices For Compressive Sensing","authors":"Hamid Nouasria, Mohamed Et-tolba, Abla Bedoui","doi":"10.1109/IWCMC.2019.8766681","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766681","url":null,"abstract":"Compressive sensing is a new methodology to reconstruct sparse signals from a few number of measurements. In this paper, we propose new sensing matrices for compressive sensing using orthogonal Hadamard matrix. The conventional sensing matrices based on orthogonal Hadamard matrix give acceptable performance but have some drawbacks. In contrast, the proposed sensing matrices are more suitable to compressive sensing compared with the conventional ones. Because they surpass their conventional drawbacks and give higher performance simultaneously. Extensive simulations on both synthesized signals and real images are conducted to show the power of the proposed sensing matrices compared with the conventional ones using convex optimization algorithms.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123468456","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766703
Ghassan Alnwaimi, H. Boujemaa, K. Arshad
In this paper, a method to optimize packet length for two way relaying systems is suggested. Two source nodes N1 and N2 are transmitting data simultaneously to each other through a selected relay. The relay amplifies the sum of N1 and N2 signals. N1 (respectively N2) retrieves its own signal to be able to detect that of node N2 (respectively N1). In the existing literature, a Fixed Packet Length (FPL) is used and it is demonstrated, in this paper, that the proposed Optimal Packet Length (OPL) allows to enhance the system throughput.
{"title":"Packet Length Optimization for Two Way Relaying","authors":"Ghassan Alnwaimi, H. Boujemaa, K. Arshad","doi":"10.1109/IWCMC.2019.8766703","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766703","url":null,"abstract":"In this paper, a method to optimize packet length for two way relaying systems is suggested. Two source nodes N<inf>1</inf> and N<inf>2</inf> are transmitting data simultaneously to each other through a selected relay. The relay amplifies the sum of N<inf>1</inf> and N<inf>2</inf> signals. N<inf>1</inf> (respectively N<inf>2</inf>) retrieves its own signal to be able to detect that of node N<inf>2</inf> (respectively N<inf>1</inf>). In the existing literature, a Fixed Packet Length (FPL) is used and it is demonstrated, in this paper, that the proposed Optimal Packet Length (OPL) allows to enhance the system throughput.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152555","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766650
Yanhua He, Liangrui Tang, Zhenyu Zhou, Yun Ren
Considering the neglect of the long-term quality of experience (QoE) in the previous work, this paper applies a cross-layer resource management algorithm to optimize users’ long-term QoE in the Internet of vehicles (IoV). Based on the multi-hop vehicle-to-everything (V2X) communication downlink transmission system, the rate arrival and departure are designed into a stochastic queue model. Then the optimization problem is transformed to a trade-off problem between queue stability and long-term QoE, through Lyapunov optimization. Moreover, the trade-off problem is decomposed into a series of online sub-problems, which involves the joint optimization of rate control, power allocation and mobile relay selection. On one hand, the rate control problem is decoupled and solved by the Lagrangian method independently. On the other hand, a two-side matching algorithm is introduced into the joint power allocation and mobile relay selection optimization, to obtain low complexity. At last, simulation results demonstrate the queue stability and the superiority of system performance.
{"title":"Long-term QoE Optimization in IoV Based on Cross-layer Resource Management","authors":"Yanhua He, Liangrui Tang, Zhenyu Zhou, Yun Ren","doi":"10.1109/IWCMC.2019.8766650","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766650","url":null,"abstract":"Considering the neglect of the long-term quality of experience (QoE) in the previous work, this paper applies a cross-layer resource management algorithm to optimize users’ long-term QoE in the Internet of vehicles (IoV). Based on the multi-hop vehicle-to-everything (V2X) communication downlink transmission system, the rate arrival and departure are designed into a stochastic queue model. Then the optimization problem is transformed to a trade-off problem between queue stability and long-term QoE, through Lyapunov optimization. Moreover, the trade-off problem is decomposed into a series of online sub-problems, which involves the joint optimization of rate control, power allocation and mobile relay selection. On one hand, the rate control problem is decoupled and solved by the Lagrangian method independently. On the other hand, a two-side matching algorithm is introduced into the joint power allocation and mobile relay selection optimization, to obtain low complexity. At last, simulation results demonstrate the queue stability and the superiority of system performance.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"7 14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121631558","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766562
Jun Zheng, Donghong Jia, Baoxian Zhang
This paper studies the downlink spectrum resource allocation problem in a small cell network and focuses on mitigating intra-tier interference between different small cells. A two-tier clustering based downlink resource allocation (TCRA) algorithm is proposed to perform spectrum resource allocation. To increase spectrum utilization, the algorithm allows different small-cell user equipments (SUEs) to share the same physical resource blocks (PRBs). Meanwhile, to mitigate intra-tier interference, those SUEs who are close to each other in distance are avoided to share the same PRBs as much as possible. To implement this, a two-tier clustering approach is introduced in PRB allocation. In the first tier, all small cells in the system are partitioned into a set of small cell clusters based on graph coloring, and those small cells with the same color are partitioned into the same cluster. In the second tier, all SUEs in each small cell cluster are further partitioned into a set of user clusters based on the interference graph of each small cell cluster. After the two-tier clustering, PRB allocation is performed based on the user clusters obtained. Simulation results show that the proposed TCRA algorithm can significantly improve the system performance in terms of total system capacity.
{"title":"A Two-Tier Clustering based Downlink Resource Allocation Algorithm for Small Cell Networks","authors":"Jun Zheng, Donghong Jia, Baoxian Zhang","doi":"10.1109/IWCMC.2019.8766562","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766562","url":null,"abstract":"This paper studies the downlink spectrum resource allocation problem in a small cell network and focuses on mitigating intra-tier interference between different small cells. A two-tier clustering based downlink resource allocation (TCRA) algorithm is proposed to perform spectrum resource allocation. To increase spectrum utilization, the algorithm allows different small-cell user equipments (SUEs) to share the same physical resource blocks (PRBs). Meanwhile, to mitigate intra-tier interference, those SUEs who are close to each other in distance are avoided to share the same PRBs as much as possible. To implement this, a two-tier clustering approach is introduced in PRB allocation. In the first tier, all small cells in the system are partitioned into a set of small cell clusters based on graph coloring, and those small cells with the same color are partitioned into the same cluster. In the second tier, all SUEs in each small cell cluster are further partitioned into a set of user clusters based on the interference graph of each small cell cluster. After the two-tier clustering, PRB allocation is performed based on the user clusters obtained. Simulation results show that the proposed TCRA algorithm can significantly improve the system performance in terms of total system capacity.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114773154","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766661
Hosni Manai, L. B. Slama, R. Bouallègue
Wireless communications systems are the most prosperous technologies of this decade. Driven by newfangled innovative applications such as internet of things and augmented reality, this trend certainly continues exponentially. Massive MIMO technique use almost infinite number of antennas at the base station are expected to play a key role in future communication systems as part of horizon 2020 project. However, the large number of antenna elements in massive MIMO also pose a major challenge, due to the interference is the main impairment to successful communication. In this paper, we propose an original massive MIMO system. Adaptive Beamforming algorithm’s used since it is a promising solution in order to avoid interference problems where conventional beamformers are susceptible to interference signals on the contrary the algorithm can account for interference signals, the simulate results evaluate the interference inter-beams by giving the coherence between the different beams which each beam is appropriate to a specific terminal in communication system and demonstrates the effectiveness of our solution to mitigate interference, thereby contributing to the increase of network capacity. Our research affords a suitable solution for the design of next-generation wireless communication systems.
{"title":"Interference Management by Adaptive Beamforming Algorithm in Massive MIMO Networks","authors":"Hosni Manai, L. B. Slama, R. Bouallègue","doi":"10.1109/IWCMC.2019.8766661","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766661","url":null,"abstract":"Wireless communications systems are the most prosperous technologies of this decade. Driven by newfangled innovative applications such as internet of things and augmented reality, this trend certainly continues exponentially. Massive MIMO technique use almost infinite number of antennas at the base station are expected to play a key role in future communication systems as part of horizon 2020 project. However, the large number of antenna elements in massive MIMO also pose a major challenge, due to the interference is the main impairment to successful communication. In this paper, we propose an original massive MIMO system. Adaptive Beamforming algorithm’s used since it is a promising solution in order to avoid interference problems where conventional beamformers are susceptible to interference signals on the contrary the algorithm can account for interference signals, the simulate results evaluate the interference inter-beams by giving the coherence between the different beams which each beam is appropriate to a specific terminal in communication system and demonstrates the effectiveness of our solution to mitigate interference, thereby contributing to the increase of network capacity. Our research affords a suitable solution for the design of next-generation wireless communication systems.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124427860","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766356
Sahbi Mazlout, M. Salah, A. Samet
The L-shaped array structure is considered as an attractive alternative to estimate entire direction of arrival (both azimuth and elevation). Most of the techniques developed around this specific array structure assume a small elevation angle range (70 to 90 degrees) which is not enough realistic in the context of small cells environment. In this paper, we propose a decision fusion rule of a parallel 2-D direction of arrival (DOA) estimation of shifted signal. The performance of the proposed algorithm is investigated through numerical simulations and compared with the 2D propagator method estimator. The impact of angular spread on the precision of the DOA estimation approaches is also considered. Our algorithm accurately estimates the DOA over a wide range of elevation angle (10 to 90 degrees).
{"title":"Decision-based Algorithm for Robust 2D-DoA Estimation with L-shaped Array","authors":"Sahbi Mazlout, M. Salah, A. Samet","doi":"10.1109/IWCMC.2019.8766356","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766356","url":null,"abstract":"The L-shaped array structure is considered as an attractive alternative to estimate entire direction of arrival (both azimuth and elevation). Most of the techniques developed around this specific array structure assume a small elevation angle range (70 to 90 degrees) which is not enough realistic in the context of small cells environment. In this paper, we propose a decision fusion rule of a parallel 2-D direction of arrival (DOA) estimation of shifted signal. The performance of the proposed algorithm is investigated through numerical simulations and compared with the 2D propagator method estimator. The impact of angular spread on the precision of the DOA estimation approaches is also considered. Our algorithm accurately estimates the DOA over a wide range of elevation angle (10 to 90 degrees).","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124070312","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 : 2019-06-01DOI: 10.1109/IWCMC.2019.8766457
D. Tarek, A. Benslimane, M. Darwish, A. Kotb
In interweave Cognitive Radio Networks (CRNs), monitoring the spectrum to detect unused portions (holes) is done by the spectrum sensing function however, it consumes both time and energy. So, some protocols use prediction to estimate the channel availability. One of these protocols use Hidden Markov Model (HMM) but in a very simple way. So, it does not perform well in several cases. In this paper, we propose two new protocols for cognitive radio channel availability prediction. Both protocols use HMM but in a more advanced way. They divide the data into two sets, thus create two HMM models. The first protocol uses Bayes theorem together with these two models, while the second one uses Support Vector Machine (SVM) with the two models HMM parameters. Evaluation of the two protocols proves that both protocols perform better than the old one that uses HMM in a classical way. It also proves that using SVM with HMM parameters is better than using HMM only. This is because dividing the data into two sets for training the protocols with, gives more flexibility to both protocols.
{"title":"Cognitive Radio Networks Channel State Estimation Using Machine Learning Techniques","authors":"D. Tarek, A. Benslimane, M. Darwish, A. Kotb","doi":"10.1109/IWCMC.2019.8766457","DOIUrl":"https://doi.org/10.1109/IWCMC.2019.8766457","url":null,"abstract":"In interweave Cognitive Radio Networks (CRNs), monitoring the spectrum to detect unused portions (holes) is done by the spectrum sensing function however, it consumes both time and energy. So, some protocols use prediction to estimate the channel availability. One of these protocols use Hidden Markov Model (HMM) but in a very simple way. So, it does not perform well in several cases. In this paper, we propose two new protocols for cognitive radio channel availability prediction. Both protocols use HMM but in a more advanced way. They divide the data into two sets, thus create two HMM models. The first protocol uses Bayes theorem together with these two models, while the second one uses Support Vector Machine (SVM) with the two models HMM parameters. Evaluation of the two protocols proves that both protocols perform better than the old one that uses HMM in a classical way. It also proves that using SVM with HMM parameters is better than using HMM only. This is because dividing the data into two sets for training the protocols with, gives more flexibility to both protocols.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704497","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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