Pub Date : 2018-06-01DOI: 10.1109/SPAWC.2018.8446030
Jiawei Yu, Min Dong
We consider downlink multicast beamforming in a multi-cell network with large-scale antennas. To reduce both communication and computation complexity in coordinated multicast beamforming, we propose the signal-to-leakage ratio (SLR) based coordinated multicast beamforming design to maximize the minimum SLR in the network. The SLR metric allows coordinated beamforming to be computed distributively and independently at each BS. To further reduce the complexity in obtaining the beam solution, we consider weighted maximum ratio transmission beamforming structure which transforms the multicast beamforming optimization into a weight optimization problem which is solved by the semi-definite relaxation approach. Our proposed distributed solution requires no communication or information sharing among cells and has very low computational complexity not depending on the number of antennas. Simulation shows that our proposed solution results in only a small loss to the centralized coordinated beamforming solution, but is significantly better than the non-coordinated methods.
{"title":"Distributed Low-Complexity Multi-Cell Coordinated Multicast Beamforming with Large-Scale Antennas","authors":"Jiawei Yu, Min Dong","doi":"10.1109/SPAWC.2018.8446030","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446030","url":null,"abstract":"We consider downlink multicast beamforming in a multi-cell network with large-scale antennas. To reduce both communication and computation complexity in coordinated multicast beamforming, we propose the signal-to-leakage ratio (SLR) based coordinated multicast beamforming design to maximize the minimum SLR in the network. The SLR metric allows coordinated beamforming to be computed distributively and independently at each BS. To further reduce the complexity in obtaining the beam solution, we consider weighted maximum ratio transmission beamforming structure which transforms the multicast beamforming optimization into a weight optimization problem which is solved by the semi-definite relaxation approach. Our proposed distributed solution requires no communication or information sharing among cells and has very low computational complexity not depending on the number of antennas. Simulation shows that our proposed solution results in only a small loss to the centralized coordinated beamforming solution, but is significantly better than the non-coordinated methods.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125148477","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445782
Luigi Vigneri, T. Spyropoulos, C. Barakat
Using vehicles equipped with small caches as small base stations has recently been proposed as an interesting middle ground between caching at fixed base stations (which has higher CAPEX/OPEX), and caching at user devices (which has resource limitations). A typical problem in this setup is which content to store in which vehicles. The correct answer depends on the application. Indeed, if the stored content will be streamed (not downloaded), then this offers a natural delay tolerance: latter parts of the content do not need to be downloaded immediately from expensive links (e.g., macro-cells), but could be fetched from encountered vehicles cheaply. In an earlier work, we formulated a related optimal cache allocation problem, in which the proposed solution stores a content in its entirety. In light of recent statistics suggesting that different parts of a content (e.g., YouTube clips) are not watched equally frequently, this method is suboptimal. In this paper, we thus consider per-chunk allocation, and propose a simple two-step heuristic that first allocates the vehicular cloud capacity among content items, then efficiently distributes the capacity for a specific content among its chunks. Trace-driven simulation results suggest that chunk-based allocation can lead to considerable gains.
{"title":"A Two-Step Chunk-Based Algorithm for Offloading Streaming Traffic Through a Vehicular Cloud","authors":"Luigi Vigneri, T. Spyropoulos, C. Barakat","doi":"10.1109/SPAWC.2018.8445782","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445782","url":null,"abstract":"Using vehicles equipped with small caches as small base stations has recently been proposed as an interesting middle ground between caching at fixed base stations (which has higher CAPEX/OPEX), and caching at user devices (which has resource limitations). A typical problem in this setup is which content to store in which vehicles. The correct answer depends on the application. Indeed, if the stored content will be streamed (not downloaded), then this offers a natural delay tolerance: latter parts of the content do not need to be downloaded immediately from expensive links (e.g., macro-cells), but could be fetched from encountered vehicles cheaply. In an earlier work, we formulated a related optimal cache allocation problem, in which the proposed solution stores a content in its entirety. In light of recent statistics suggesting that different parts of a content (e.g., YouTube clips) are not watched equally frequently, this method is suboptimal. In this paper, we thus consider per-chunk allocation, and propose a simple two-step heuristic that first allocates the vehicular cloud capacity among content items, then efficiently distributes the capacity for a specific content among its chunks. Trace-driven simulation results suggest that chunk-based allocation can lead to considerable gains.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131930114","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445922
M. Karaliopoulos, I. Koutsopoulos
When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning. Such instances arise in (i) mobile crowdsensing campaigns, where tasks are assigned to users through the app, and the goal is to optimize the quality of fulfilled tasks; (ii) smart-energy apps, where energy-saving recommendations are issued through the app, and the goal is to optimize energy savings; (iii) mobile advertising, where ads or offers are projected to the user, and the aim is to optimize revenue through user response to ads. Each user is modeled as a vector of feature values for a set of features. In an important class of decision-making models in behavioral science, the lexicographic fast-and-frugal-tree (FFT) heuristics, user decision emerges through a ranking of features that in turn gives rise to a decision tree. Having the incentive as a controllable feature that guides the user decision process, we study and characterize the complexity of the problem of allocating choices and incentives to users out of a limited budget. Numerical results indicate important performance gains when the incentive allocation policy adapts to user lexicographic choices.
{"title":"Mobile App User Choice Engineering Using Behavioral Science Models","authors":"M. Karaliopoulos, I. Koutsopoulos","doi":"10.1109/SPAWC.2018.8445922","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445922","url":null,"abstract":"When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning. Such instances arise in (i) mobile crowdsensing campaigns, where tasks are assigned to users through the app, and the goal is to optimize the quality of fulfilled tasks; (ii) smart-energy apps, where energy-saving recommendations are issued through the app, and the goal is to optimize energy savings; (iii) mobile advertising, where ads or offers are projected to the user, and the aim is to optimize revenue through user response to ads. Each user is modeled as a vector of feature values for a set of features. In an important class of decision-making models in behavioral science, the lexicographic fast-and-frugal-tree (FFT) heuristics, user decision emerges through a ranking of features that in turn gives rise to a decision tree. Having the incentive as a controllable feature that guides the user decision process, we study and characterize the complexity of the problem of allocating choices and incentives to users out of a limited budget. Numerical results indicate important performance gains when the incentive allocation policy adapts to user lexicographic choices.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132244289","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445967
S. Zhang, F. Xue, N. Himayat, S. Talwar, H. T. Kung
We apply machine learning techniques to predict the cell quality for the aerial drones connecting with a standard cellular network on the ground. Stationary and strong spatial correlation of the aerial channels allow for exploiting predictive techniques for optimal cell selection based on few available neighboring observations. Yet, drastic cell quality changes due to the side lobes of base-station antenna patterns require advanced solutions for accurate prediction. In this paper, we propose a conditional random field based framework to predict a drone's best (or top few) candidates for the serving cell. Our results, assuming realistic antenna patterns as well as errors in the location estimates, show a high prediction accuracy, thereby illustrating the feasibility of exploiting learning approaches to predict the aerial channel environment.
{"title":"A Machine Learning Assisted Cell Selection Method for Drones in Cellular Networks","authors":"S. Zhang, F. Xue, N. Himayat, S. Talwar, H. T. Kung","doi":"10.1109/SPAWC.2018.8445967","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445967","url":null,"abstract":"We apply machine learning techniques to predict the cell quality for the aerial drones connecting with a standard cellular network on the ground. Stationary and strong spatial correlation of the aerial channels allow for exploiting predictive techniques for optimal cell selection based on few available neighboring observations. Yet, drastic cell quality changes due to the side lobes of base-station antenna patterns require advanced solutions for accurate prediction. In this paper, we propose a conditional random field based framework to predict a drone's best (or top few) candidates for the serving cell. Our results, assuming realistic antenna patterns as well as errors in the location estimates, show a high prediction accuracy, thereby illustrating the feasibility of exploiting learning approaches to predict the aerial channel environment.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161974","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445923
Stefan Pratschner, S. Caban, Daniel Schützenhöfer, M. Lerch, E. Zöchmann, M. Rupp
Massive multiple-input multiple-output (MIMO) channel measurements are often obtained using virtual antenna arrays for complexity reasons. In this approach, a single antenna element is sequentially re-positioned to virtually form an antenna array in space. As a consequence, there are no mutual coupling effects when measuring with a virtual array. We perform realworld outdoor to indoor MIMO channel measurements with both, a virtual array approach and a full array approach, for various antenna spacings. From the obtained results, we observe that a spacing of a half-wavelength does not lead to the best match between the two approaches. We conclude that the deviation mainly originates from mutual coupling effects and quantify the difference in terms of error in channel gain.
{"title":"A Fair Comparison of Virtual to Full Antenna Array Measurements","authors":"Stefan Pratschner, S. Caban, Daniel Schützenhöfer, M. Lerch, E. Zöchmann, M. Rupp","doi":"10.1109/SPAWC.2018.8445923","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445923","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) channel measurements are often obtained using virtual antenna arrays for complexity reasons. In this approach, a single antenna element is sequentially re-positioned to virtually form an antenna array in space. As a consequence, there are no mutual coupling effects when measuring with a virtual array. We perform realworld outdoor to indoor MIMO channel measurements with both, a virtual array approach and a full array approach, for various antenna spacings. From the obtained results, we observe that a spacing of a half-wavelength does not lead to the best match between the two approaches. We conclude that the deviation mainly originates from mutual coupling effects and quantify the difference in terms of error in channel gain.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132386252","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445928
Olivier Renaudin, T. Zemen, Thomas Burgess
Empirical fingerprinting is currently one of the most efficient localization methods in indoor environments, due to the now ubiquitous deployment of wireless local area networks. A fingerprint is the pattern of received signal strengths from all the access points visible at a particular position. Reference fingerprints are obtained from extensive measurement campaigns and used during an offline phase to construct suitable radio maps of the environment of interest. However, this approach is very site-specific and the radio maps require therefore to be regularly updated in order to take into account changes in the environment, i.e. a labor-intensive and time-consuming task. Hence, ray-tracing simulations are instead used in this paper to construct these radio maps based on deterministic prediction of the radio wave propagation. To validate this approach, field experiments were conducted in an indoor office environment. The results show (i): the sensitivity of fingerprints to small-scale fading and human shadowing, as well as (ii): good agreement between the measured and ray-tracing simulated fingerprints, especially with strong line-of-sight.
{"title":"Ray-Tracing Based Fingerprinting for Indoor Localization","authors":"Olivier Renaudin, T. Zemen, Thomas Burgess","doi":"10.1109/SPAWC.2018.8445928","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445928","url":null,"abstract":"Empirical fingerprinting is currently one of the most efficient localization methods in indoor environments, due to the now ubiquitous deployment of wireless local area networks. A fingerprint is the pattern of received signal strengths from all the access points visible at a particular position. Reference fingerprints are obtained from extensive measurement campaigns and used during an offline phase to construct suitable radio maps of the environment of interest. However, this approach is very site-specific and the radio maps require therefore to be regularly updated in order to take into account changes in the environment, i.e. a labor-intensive and time-consuming task. Hence, ray-tracing simulations are instead used in this paper to construct these radio maps based on deterministic prediction of the radio wave propagation. To validate this approach, field experiments were conducted in an indoor office environment. The results show (i): the sensitivity of fingerprints to small-scale fading and human shadowing, as well as (ii): good agreement between the measured and ray-tracing simulated fingerprints, especially with strong line-of-sight.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375514","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445972
M. Aminu, M. Codreanu, M. Juntti
We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog two-stage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach.
{"title":"Bayesian Learning Based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Array","authors":"M. Aminu, M. Codreanu, M. Juntti","doi":"10.1109/SPAWC.2018.8445972","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445972","url":null,"abstract":"We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog two-stage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124352940","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445873
Yin Sun, Benjamin Cyr
In this paper, we propose a new measure for the freshness of information, which uses the mutual information between the real-time source value and the delivered samples at the receiver to quantify the freshness of the information contained in the delivered samples. Hence, the “aging” of the received information can be interpreted as a procedure that the above mutual information reduces as the age grows. In addition, we consider a sampling problem, where samples of a Markov source are taken and sent through a queue to the receiver. In order to optimize the freshness of information, we study the optimal sampling policy that maximizes the time-average expected mutual information. We prove that the optimal sampling policy is a threshold policy and find the optimal threshold exactly. Specifically, a new sample is taken once a conditional mutual information reduces to a threshold, and the threshold is equal to the optimum value of the time-average expected mutual information that is being maximized. Numerical results are provided to compare different sampling policies.
{"title":"Information Aging Through Queues: A Mutual Information Perspective","authors":"Yin Sun, Benjamin Cyr","doi":"10.1109/SPAWC.2018.8445873","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445873","url":null,"abstract":"In this paper, we propose a new measure for the freshness of information, which uses the mutual information between the real-time source value and the delivered samples at the receiver to quantify the freshness of the information contained in the delivered samples. Hence, the “aging” of the received information can be interpreted as a procedure that the above mutual information reduces as the age grows. In addition, we consider a sampling problem, where samples of a Markov source are taken and sent through a queue to the receiver. In order to optimize the freshness of information, we study the optimal sampling policy that maximizes the time-average expected mutual information. We prove that the optimal sampling policy is a threshold policy and find the optimal threshold exactly. Specifically, a new sample is taken once a conditional mutual information reduces to a threshold, and the threshold is equal to the optimum value of the time-average expected mutual information that is being maximized. Numerical results are provided to compare different sampling policies.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"396 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882779","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8446005
Haoran Sun, Ziping Zhao, Xiao Fu, Mingyi Hong
In frequency division duplex massive MIMO systems, one critical challenge is that the mobiles need to feed back a large downlink channel matrix to the base station, creating large signaling overhead. Estimating a large downlink channel matrix at the mobile may also be costly in terms of power and memory consumption. Prior work addresses these issues using appropriate angle parameterization and compressed sensing techniques, but this approach involves solving a challenging, and sometimes extremely large, sparse inverse problem-which is difficult to solve to global optimality, and often leads to unaffordable memory and computational costs. In this work, we propose an alternative framework that explores the fact that double directional channels for mmWave massive MIMO usually have low rank. The base station estimates the downlink channel via recovering a low-rank matrix, utilizing samples of the channel matrix compressed and fed back from the mobiles. This way, the mobile users can avoid performing resource-consuming tasks. In addition, the number of feedback measurements can be much smaller than the size of the channel matrix without losing channel recovery guarantees. Further, the low-rank estimation problem at the base station has a manageable size that scales gracefully with the channel size. Based on the new model, we propose two methods for channel estimation, which are based on iterative optimization and deep learning, respectively. Compared with the state-of-the-art, the optimization method obtains 10x improvement and the deep learning approach achieves up to 1000x improvement in computational complexity, while achieving high estimation quality in very low sample region.
{"title":"Limited Feedback Double Directional Massive MIMO Channel Estimation: From Low-Rank Modeling to Deep Learning","authors":"Haoran Sun, Ziping Zhao, Xiao Fu, Mingyi Hong","doi":"10.1109/SPAWC.2018.8446005","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446005","url":null,"abstract":"In frequency division duplex massive MIMO systems, one critical challenge is that the mobiles need to feed back a large downlink channel matrix to the base station, creating large signaling overhead. Estimating a large downlink channel matrix at the mobile may also be costly in terms of power and memory consumption. Prior work addresses these issues using appropriate angle parameterization and compressed sensing techniques, but this approach involves solving a challenging, and sometimes extremely large, sparse inverse problem-which is difficult to solve to global optimality, and often leads to unaffordable memory and computational costs. In this work, we propose an alternative framework that explores the fact that double directional channels for mmWave massive MIMO usually have low rank. The base station estimates the downlink channel via recovering a low-rank matrix, utilizing samples of the channel matrix compressed and fed back from the mobiles. This way, the mobile users can avoid performing resource-consuming tasks. In addition, the number of feedback measurements can be much smaller than the size of the channel matrix without losing channel recovery guarantees. Further, the low-rank estimation problem at the base station has a manageable size that scales gracefully with the channel size. Based on the new model, we propose two methods for channel estimation, which are based on iterative optimization and deep learning, respectively. Compared with the state-of-the-art, the optimization method obtains 10x improvement and the deep learning approach achieves up to 1000x improvement in computational complexity, while achieving high estimation quality in very low sample region.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494343","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445886
Deepak Mishra, E. Larsson
Backscatter communication (BSC) technology can enable ubiquitous deployment of low-cost sustainable wireless devices. In this work we investigate the efficacy of a full-duplex antenna array reader in overcoming the limited communication range bottleneck of monostatic BSCs. As performance is strongly influenced by the channel estimation (CE) quality, we first derive a novel least-squares estimator (LSE) for the forward and backward links between the reader and the tag, assuming that reciprocity holds. After defining the transceiver design at reader using this LSE, we optimize the energy allocation for the CE and information decoding phases, to maximize the average backscattered signal-to-noise ratio (SNR). The unimodality of this SNR in optimization variable along with a tight approximation for the global optimal design are also presented. Lastly, numerical results validate the proposed analysis and present key insights into the optimal LSE and energy allocation.
{"title":"Optimizing Reciprocity-Based Backscattering with a Full-Duplex Antenna Array Reader","authors":"Deepak Mishra, E. Larsson","doi":"10.1109/SPAWC.2018.8445886","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445886","url":null,"abstract":"Backscatter communication (BSC) technology can enable ubiquitous deployment of low-cost sustainable wireless devices. In this work we investigate the efficacy of a full-duplex antenna array reader in overcoming the limited communication range bottleneck of monostatic BSCs. As performance is strongly influenced by the channel estimation (CE) quality, we first derive a novel least-squares estimator (LSE) for the forward and backward links between the reader and the tag, assuming that reciprocity holds. After defining the transceiver design at reader using this LSE, we optimize the energy allocation for the CE and information decoding phases, to maximize the average backscattered signal-to-noise ratio (SNR). The unimodality of this SNR in optimization variable along with a tight approximation for the global optimal design are also presented. Lastly, numerical results validate the proposed analysis and present key insights into the optimal LSE and energy allocation.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"32 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126099415","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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