Pub Date : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114921
Nagwa Ibrahim, A. Eltholth, M. El-Soudani
Cloud radio access network (C-RAN) architecture is actively considered as a major candidate for future wireless communications. The aerial communication network such as high altitude balloon (HAB) is used to transport the fronthaul among radio transceivers and processing units. Both free space optic (FSO) and millimeter wave (mmWave) are promising technologies, but each one has its impairments that affect its efficiency under different weather conditions. So, a hybrid channel is considered to match with the requirements of fronthaul networks. This paper aims to optimize the hand over process between FSO and mmWave channels to maximize the sum data rate for the fronthaul in C-RAN architecture. The problem is formulated as an integer linear programming (ILP) problem. The mathematical programming is applied on the hybrid transmission technology FSO/mmWave channel. The obtained numerical results indicate the potential of hybrid FSO/mmWave channel in counteracting the effect of different weather conditions.
{"title":"Hybrid FSO/mmWave based Fronthaul C-RAN Optimization for Future Wireless Communications","authors":"Nagwa Ibrahim, A. Eltholth, M. El-Soudani","doi":"10.1109/WOCC48579.2020.9114921","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114921","url":null,"abstract":"Cloud radio access network (C-RAN) architecture is actively considered as a major candidate for future wireless communications. The aerial communication network such as high altitude balloon (HAB) is used to transport the fronthaul among radio transceivers and processing units. Both free space optic (FSO) and millimeter wave (mmWave) are promising technologies, but each one has its impairments that affect its efficiency under different weather conditions. So, a hybrid channel is considered to match with the requirements of fronthaul networks. This paper aims to optimize the hand over process between FSO and mmWave channels to maximize the sum data rate for the fronthaul in C-RAN architecture. The problem is formulated as an integer linear programming (ILP) problem. The mathematical programming is applied on the hybrid transmission technology FSO/mmWave channel. The obtained numerical results indicate the potential of hybrid FSO/mmWave channel in counteracting the effect of different weather conditions.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133941189","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114928
Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Mofadal Alymani, Mohsen H. Alhazmi, Zikang Sheng, Yu-dong Yao
Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.
{"title":"Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning","authors":"Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Mofadal Alymani, Mohsen H. Alhazmi, Zikang Sheng, Yu-dong Yao","doi":"10.1109/WOCC48579.2020.9114928","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114928","url":null,"abstract":"Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132894694","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114913
A. Ghasemi, S. Zekavat
This paper offers a new approach for antenna clustering and hybrid beamforming applicable to massive MIMO systems. Simultaneous clustering and hybrid beamforming across Tx and Rx antennas is an NP-hard problem. To address this issue, first, the paper proposes an antenna clustering that is applied to both Tx and Rx. In this regard, antenna arrays at Tx and Rx are modeled as a Bipartite graph and for the first time, one bi-clustering algorithm, Spectral Co-Clustering algorithm, is applied to achieve simultaneous clustering. Next, singular vectors of subchannels, which are the channels between subantenna arrays of Tx and Rx, are comprised to determine optimal precoders/combiners. Performance evaluations in terms of Tx-Rx data streaming sum-rate demonstrate the effectiveness of the proposed algorithm.
{"title":"Joint Hybrid Beamforming and Dynamic Antenna Clustering for Massive MIMO","authors":"A. Ghasemi, S. Zekavat","doi":"10.1109/WOCC48579.2020.9114913","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114913","url":null,"abstract":"This paper offers a new approach for antenna clustering and hybrid beamforming applicable to massive MIMO systems. Simultaneous clustering and hybrid beamforming across Tx and Rx antennas is an NP-hard problem. To address this issue, first, the paper proposes an antenna clustering that is applied to both Tx and Rx. In this regard, antenna arrays at Tx and Rx are modeled as a Bipartite graph and for the first time, one bi-clustering algorithm, Spectral Co-Clustering algorithm, is applied to achieve simultaneous clustering. Next, singular vectors of subchannels, which are the channels between subantenna arrays of Tx and Rx, are comprised to determine optimal precoders/combiners. Performance evaluations in terms of Tx-Rx data streaming sum-rate demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132143701","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114943
J. Dabin, A. Haimovich, Justin Mauger, Annan Dong
Blind source separation of co-channel communication signals can be performed by structuring the problem with an over-complete dictionary of the channel and solving for the sparse coefficients, which represent the latent transmitted signals. $L_{1}$ regularized least squares is a common approach to imposing sparsity on the latent signal representation while minimizing the reconstruction error. In this paper we propose an unsupervised learning approach for blind source separation using an $L_{1}$ regularized sparse autoencoder with a softthreshold activation function at the hidden layer that is able to separate and fully recover multiple overlapping binary phase shift keying co-channel signals.
{"title":"Blind Source Separation with L1 Regularized Sparse Autoencoder","authors":"J. Dabin, A. Haimovich, Justin Mauger, Annan Dong","doi":"10.1109/WOCC48579.2020.9114943","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114943","url":null,"abstract":"Blind source separation of co-channel communication signals can be performed by structuring the problem with an over-complete dictionary of the channel and solving for the sparse coefficients, which represent the latent transmitted signals. $L_{1}$ regularized least squares is a common approach to imposing sparsity on the latent signal representation while minimizing the reconstruction error. In this paper we propose an unsupervised learning approach for blind source separation using an $L_{1}$ regularized sparse autoencoder with a softthreshold activation function at the hidden layer that is able to separate and fully recover multiple overlapping binary phase shift keying co-channel signals.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130818762","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114936
E. Verdugo, R. Nebuloni, L. Luini, C. Riva, L. Mello, Giuseppe Roveda
Optical and mmWave terrestrial links are somewhat considered complementary as they have a different sensitivity to fog and rain, i.e. the most frequent atmospheric impairments at mid-latitude. Hence, hybrid optical-mmWave systems that back-up each other according to weather conditions, have been proposed as they put together extremely large-bandwidth and high availability. However, in order to assess whether optical and mmWave systems can be considered complementary rather than competitors, the propagation effects should be quantified, possibly on a statistical basis. This paper presents preliminary results of the effects of rain on a commercial optical link at 1550 nm and a co-located dual-band mmWave link. It is shown that the degradation of the optical signal is not always well correlated with the microphysical properties of rain, Signal attenuation can be substantially underestimated if predicted by the electromagnetic theory, due to the concurrent action of other factors.
{"title":"Rain Effects on FSO and mmWave Links: Preliminary Results from an Experimental Study","authors":"E. Verdugo, R. Nebuloni, L. Luini, C. Riva, L. Mello, Giuseppe Roveda","doi":"10.1109/WOCC48579.2020.9114936","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114936","url":null,"abstract":"Optical and mmWave terrestrial links are somewhat considered complementary as they have a different sensitivity to fog and rain, i.e. the most frequent atmospheric impairments at mid-latitude. Hence, hybrid optical-mmWave systems that back-up each other according to weather conditions, have been proposed as they put together extremely large-bandwidth and high availability. However, in order to assess whether optical and mmWave systems can be considered complementary rather than competitors, the propagation effects should be quantified, possibly on a statistical basis. This paper presents preliminary results of the effects of rain on a commercial optical link at 1550 nm and a co-located dual-band mmWave link. It is shown that the degradation of the optical signal is not always well correlated with the microphysical properties of rain, Signal attenuation can be substantially underestimated if predicted by the electromagnetic theory, due to the concurrent action of other factors.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125595552","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114950
Mario V. Bnyamin, M. Feuer, Xin Jiang
Polarization-shift keying (PolSK) and Stokes vector modulation (SVM) offer multi-dimensional signaling for high-throughput data links in terabit-class data center networks, using low-cost, direct detection (DD) receivers. In this paper, we develop and characterize a system based on a cubic constellation for 8-SVM, using an off-the-shelf integrated modulator driven with simple bias points and data waveforms. Symbol error rates (SER) and bit error rates (BER) are measured up to 7.5 Gb/s, and analysis of the symbol errors reveals a significant effect of inter-symbol interference.
{"title":"Symbol Error Rate Analysis of 8-state Stokes Vector Modulation for Large Capacity Data Centers","authors":"Mario V. Bnyamin, M. Feuer, Xin Jiang","doi":"10.1109/WOCC48579.2020.9114950","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114950","url":null,"abstract":"Polarization-shift keying (PolSK) and Stokes vector modulation (SVM) offer multi-dimensional signaling for high-throughput data links in terabit-class data center networks, using low-cost, direct detection (DD) receivers. In this paper, we develop and characterize a system based on a cubic constellation for 8-SVM, using an off-the-shelf integrated modulator driven with simple bias points and data waveforms. Symbol error rates (SER) and bit error rates (BER) are measured up to 7.5 Gb/s, and analysis of the symbol errors reveals a significant effect of inter-symbol interference.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643785","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114919
Chengxiao Liu, W. Feng, Yunfei Chen, Chengxiang Wang, Xiangling Li, N. Ge
The coverage area of terrestrial 4G/5G networks is usually limited, far from satisfying the communication demand in the remote rural, the post disaster and maritime scenarios. Both satellite and unmanned aerial vehicle (UAV) can be adopted to solve this problem in the 6G era. Towards this end, we consider a cognitive satellite-UAV network (CSUN), where satellite and UAVs are managed in a coordinated manner, and opportunistically share spectrum to alleviate the spectrum scarcity problem. Particularly, we use the UAV swarm to mitigate the satellite-UAV interference. Motivated by practical applications, the limited on-board energy and imperfectly acquired channel state information (CSI) are discussed. We propose a process-oriented optimization scheme to maximize the data transmission efficiency, which jointly optimizes the transmit power and hovering time of UAV swarm for the whole flight process. The scheme takes both energy constraints and interference power constraints into account, and performs in an iterative way. Simulation results demonstrate the superiority of the proposed algorithm, which could be an effective solution for extending the coverage performance of terrestrial 4G/5G networks.
{"title":"Process-Oriented Optimization for Beyond 5G Cognitive Satellite-UAV Networks (Invited Paper)","authors":"Chengxiao Liu, W. Feng, Yunfei Chen, Chengxiang Wang, Xiangling Li, N. Ge","doi":"10.1109/WOCC48579.2020.9114919","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114919","url":null,"abstract":"The coverage area of terrestrial 4G/5G networks is usually limited, far from satisfying the communication demand in the remote rural, the post disaster and maritime scenarios. Both satellite and unmanned aerial vehicle (UAV) can be adopted to solve this problem in the 6G era. Towards this end, we consider a cognitive satellite-UAV network (CSUN), where satellite and UAVs are managed in a coordinated manner, and opportunistically share spectrum to alleviate the spectrum scarcity problem. Particularly, we use the UAV swarm to mitigate the satellite-UAV interference. Motivated by practical applications, the limited on-board energy and imperfectly acquired channel state information (CSI) are discussed. We propose a process-oriented optimization scheme to maximize the data transmission efficiency, which jointly optimizes the transmit power and hovering time of UAV swarm for the whole flight process. The scheme takes both energy constraints and interference power constraints into account, and performs in an iterative way. Simulation results demonstrate the superiority of the proposed algorithm, which could be an effective solution for extending the coverage performance of terrestrial 4G/5G networks.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125829896","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114910
Yaoqing Liu, Anthony Dowling, Lauren M. Huie
Named Data Networking is one of the most promising future Internet architectures with many advanced characteristics that are lacking in the existing TCP/IP-based Internet architecture. NDN features named content, built-in security, in-network caching, adaptive traffic routing, and multi-path forwarding. NDN can be used to mitigate traffic congestion and prioritize critical messages in both wired and wireless networks. It has particular advantages to deliver traffic over a disrupted and highly dynamic network environment because of its delay-tolerant and content-centric features. However, very few works have shown the real-world capacity of NDN over different types of network links. In this work, we benchmark the performance of NDN in various real network settings and make side-by-side comparisons with TCP/IP based approaches. We also demonstrate the strong capabilities of flexible forwarding strategies through prioritizing critical traffic over the network.
{"title":"Benchmarking Network Performance in Named Data Networking (NDN)","authors":"Yaoqing Liu, Anthony Dowling, Lauren M. Huie","doi":"10.1109/WOCC48579.2020.9114910","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114910","url":null,"abstract":"Named Data Networking is one of the most promising future Internet architectures with many advanced characteristics that are lacking in the existing TCP/IP-based Internet architecture. NDN features named content, built-in security, in-network caching, adaptive traffic routing, and multi-path forwarding. NDN can be used to mitigate traffic congestion and prioritize critical messages in both wired and wireless networks. It has particular advantages to deliver traffic over a disrupted and highly dynamic network environment because of its delay-tolerant and content-centric features. However, very few works have shown the real-world capacity of NDN over different types of network links. In this work, we benchmark the performance of NDN in various real network settings and make side-by-side comparisons with TCP/IP based approaches. We also demonstrate the strong capabilities of flexible forwarding strategies through prioritizing critical traffic over the network.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"562 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418296","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 : 2020-05-01DOI: 10.1109/WOCC48579.2020.9114949
Xuesong Wang, Y. He, Yang Sun, Yueying Zhan
In this paper, we first propose the Automatic Modulation Classification (AMC) problem based on the Physical layer Network Coding (PNC) system and elaborate in detail. We use Convolutional Neural Networks (CNN) to identify nine cases including three modulation formats with three phase shifts respectively, and estimate the Signal-to-Noise Ratio (SNR) simultaneously. As the result, we correctly identify several modulation formats and typical phase offsets with a 100% recognition rate, and estimate the received signal-to-noise ratio effectively with recognition rate above 98%.
{"title":"Automatic Modulation Classification and SNR Estimation Based on CNN in Physical-layer Network Coding","authors":"Xuesong Wang, Y. He, Yang Sun, Yueying Zhan","doi":"10.1109/WOCC48579.2020.9114949","DOIUrl":"https://doi.org/10.1109/WOCC48579.2020.9114949","url":null,"abstract":"In this paper, we first propose the Automatic Modulation Classification (AMC) problem based on the Physical layer Network Coding (PNC) system and elaborate in detail. We use Convolutional Neural Networks (CNN) to identify nine cases including three modulation formats with three phase shifts respectively, and estimate the Signal-to-Noise Ratio (SNR) simultaneously. As the result, we correctly identify several modulation formats and typical phase offsets with a 100% recognition rate, and estimate the received signal-to-noise ratio effectively with recognition rate above 98%.","PeriodicalId":187607,"journal":{"name":"2020 29th Wireless and Optical Communications Conference (WOCC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134173488","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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