Pub Date : 2018-06-01DOI: 10.1109/SPAWC.2018.8446041
Mijeong Kang, E. Kim, Jinyang Li, W. Bentley, G. Payne
Molecular communication offers an exciting vision, but realizing this vision will require a bridging of device-based electromagnetic modalities and biology's molecular modalities. We are exploring redox (reduction-oxidation) reactions as a means to span these modalities. Here we use the example of the biological material melanin to illustrate how electrical signals can be used to probe for important redox-based molecular information. However, redox-probing is not limited to abiotic materials, but can be extended to probing living systems. We envision that the coupling of redox-probing with synthetic biology will allow the information processing capabilities of biology to be integrated with the speed and power of microelectronics to help realize the molecular communication vision.
{"title":"Redox: Electron-Based Approach to Bio-Device Molecular Communication","authors":"Mijeong Kang, E. Kim, Jinyang Li, W. Bentley, G. Payne","doi":"10.1109/SPAWC.2018.8446041","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446041","url":null,"abstract":"Molecular communication offers an exciting vision, but realizing this vision will require a bridging of device-based electromagnetic modalities and biology's molecular modalities. We are exploring redox (reduction-oxidation) reactions as a means to span these modalities. Here we use the example of the biological material melanin to illustrate how electrical signals can be used to probe for important redox-based molecular information. However, redox-probing is not limited to abiotic materials, but can be extended to probing living systems. We envision that the coupling of redox-probing with synthetic biology will allow the information processing capabilities of biology to be integrated with the speed and power of microelectronics to help realize the molecular communication vision.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"85 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":"134446190","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.2018.8445767
A. Truong, N. Kiyavash, S. Etesami
Learning with expert advice framework has drawn much attention in recent years especially in the context of recommendation systems. We consider two challenges that we face in broadly applying this framework in practice. One is the impact of adversarial attack strategies (malicious recommendations) and the other is lack of sufficient recommendation from quality experts (aka sleeping expert setting). In this paper, we discuss some recent results on understanding adversarial strategies and their effect on recommendation systems. In addition, in the sleeping expert setting, we discuss some novel designs for learning alaorithms and the analysis of their convergence properties.
{"title":"Adversarial Machine Learning: The Case of Recommendation Systems","authors":"A. Truong, N. Kiyavash, S. Etesami","doi":"10.1109/SPAWC.2018.8445767","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445767","url":null,"abstract":"Learning with expert advice framework has drawn much attention in recent years especially in the context of recommendation systems. We consider two challenges that we face in broadly applying this framework in practice. One is the impact of adversarial attack strategies (malicious recommendations) and the other is lack of sufficient recommendation from quality experts (aka sleeping expert setting). In this paper, we discuss some recent results on understanding adversarial strategies and their effect on recommendation systems. In addition, in the sleeping expert setting, we discuss some novel designs for learning alaorithms and the analysis of their convergence properties.","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":"132847542","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.2018.8445992
Kamal Biswas, S. K. Mohammed, E. Larsson
In this paper we consider Millimeter wave (mmWave) Massive MIMO systems where a large antenna array at the base station (BS) serves a few scheduled terminals. The high dimensional null space of the channel matrix to the scheduled terminals is utilized to broadcast system information to the non-scheduled terminals on the same time-frequency resource. Our analysis reveals the interesting result that with a sufficiently large antenna array this non-orthogonal broadcast strategy requires significantly less total transmit power when compared to the traditional orthogonal strategy where a fraction of the total resource is reserved for broadcast of system information.
{"title":"Efficient Techniques for Broadcast of System Information in mmWave Communication Systems","authors":"Kamal Biswas, S. K. Mohammed, E. Larsson","doi":"10.1109/SPAWC.2018.8445992","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445992","url":null,"abstract":"In this paper we consider Millimeter wave (mmWave) Massive MIMO systems where a large antenna array at the base station (BS) serves a few scheduled terminals. The high dimensional null space of the channel matrix to the scheduled terminals is utilized to broadcast system information to the non-scheduled terminals on the same time-frequency resource. Our analysis reveals the interesting result that with a sufficiently large antenna array this non-orthogonal broadcast strategy requires significantly less total transmit power when compared to the traditional orthogonal strategy where a fraction of the total resource is reserved for broadcast of system information.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"50 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":"128869916","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.2018.8445872
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, G. Caire
Massive MIMO is a powerful multiuser/multiantenna technology that exploits a very large number of antennas at the base station side and the knowledge of the channel matrix between base station antennas and multiple users in order to achieve large beamforming and multiplexing gain. Classical massive MIMO exploits Time-Division Duplexing (TDD) and channel reciprocity, such that the channel matrix can be learned at the base station from the incoming uplink pilot signals sent by the users. However, the large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where channel reciprocity does not hold and explicit downlink probing and uplink CSI feedback are required in order to achieve some spatial multiplexing gain. Unfortunately, the overhead incurred by explicit probing and feedback is very large in massive MIMO, since the channels are high-dimensional random vectors. In this paper, we present a new approach to achieve very competitive tradeoff between spatial multiplexing gain and probing/feedback overhead in FDD massive MIMO. Our approach is based on two novel concepts: 1) an efficient and mathematically rigorous technique to extrapolate the channel covariance matrix from the uplink to the downlink, such that the second order statistics of each downlink channel can be accurately learned for free from uplink pilots; 2) a novel “sparsifying precoding” approach, that introduces sparsity in the channel in a controlled form, such that for any assigned overhead (i.e., downlink pilot dimension) it is possible to set an optimal sparsity level for which the “effective” channels after sparsification can be estimated at the base station with low mean-square error. We compare our method with that of the state-of-the-art compressed sensing (CS) based method. Our results show that the proposed method is much more robust than compressed sensing methods, since it is able to “shape the channel sparsity” as desired, instead of being at the mercy of nature (i.e., at the mercy of the natural sparsity induced by the nronaaation environment).
{"title":"How to Achieve Massive MIMO Gains in FDD Systems?","authors":"Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, G. Caire","doi":"10.1109/SPAWC.2018.8445872","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445872","url":null,"abstract":"Massive MIMO is a powerful multiuser/multiantenna technology that exploits a very large number of antennas at the base station side and the knowledge of the channel matrix between base station antennas and multiple users in order to achieve large beamforming and multiplexing gain. Classical massive MIMO exploits Time-Division Duplexing (TDD) and channel reciprocity, such that the channel matrix can be learned at the base station from the incoming uplink pilot signals sent by the users. However, the large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where channel reciprocity does not hold and explicit downlink probing and uplink CSI feedback are required in order to achieve some spatial multiplexing gain. Unfortunately, the overhead incurred by explicit probing and feedback is very large in massive MIMO, since the channels are high-dimensional random vectors. In this paper, we present a new approach to achieve very competitive tradeoff between spatial multiplexing gain and probing/feedback overhead in FDD massive MIMO. Our approach is based on two novel concepts: 1) an efficient and mathematically rigorous technique to extrapolate the channel covariance matrix from the uplink to the downlink, such that the second order statistics of each downlink channel can be accurately learned for free from uplink pilots; 2) a novel “sparsifying precoding” approach, that introduces sparsity in the channel in a controlled form, such that for any assigned overhead (i.e., downlink pilot dimension) it is possible to set an optimal sparsity level for which the “effective” channels after sparsification can be estimated at the base station with low mean-square error. We compare our method with that of the state-of-the-art compressed sensing (CS) based method. Our results show that the proposed method is much more robust than compressed sensing methods, since it is able to “shape the channel sparsity” as desired, instead of being at the mercy of nature (i.e., at the mercy of the natural sparsity induced by the nronaaation environment).","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"25 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":"114634648","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.2018.8445889
Mrinmoy Jana, L. Lampe, J. Mitra
Dual-polarized (DP) faster-than-Nyquist (FTN) transmission using higher-order modulation (HoM) schemes can significantly increase the spectral efficiency (SE) of the existing wireless backhaul links. However, FTN transmissions introduce inter-symbol interference (ISI), antenna polarization multiplexing suffers from cross-polarization interference (XPI) and HoM makes a communication system vulnerable to phase-noise (PN) distortions. In this work, we investigate for the first time a DP-FTN HoM transmission system that offers more than 150% increase in SE compared to a single-polarized Nyquist transmission. We propose an interference cancellation and PN mitigation structure coupled with adaptive decision-feedback equalization to jointly mitigate XPI, FTN and multipath ISI, and accomplish carrier-phase tracking. Numerical results confirm that a DP-FTN transmission can yield a 3–6 dB performance improvement over an equivalent DP-Nyquist system that uses a higher modulation order to achieve the same data rate.
{"title":"Interference and Phase Noise Mitigation in a Dual-Polarized Faster-than-Nyquist Transmission","authors":"Mrinmoy Jana, L. Lampe, J. Mitra","doi":"10.1109/SPAWC.2018.8445889","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445889","url":null,"abstract":"Dual-polarized (DP) faster-than-Nyquist (FTN) transmission using higher-order modulation (HoM) schemes can significantly increase the spectral efficiency (SE) of the existing wireless backhaul links. However, FTN transmissions introduce inter-symbol interference (ISI), antenna polarization multiplexing suffers from cross-polarization interference (XPI) and HoM makes a communication system vulnerable to phase-noise (PN) distortions. In this work, we investigate for the first time a DP-FTN HoM transmission system that offers more than 150% increase in SE compared to a single-polarized Nyquist transmission. We propose an interference cancellation and PN mitigation structure coupled with adaptive decision-feedback equalization to jointly mitigate XPI, FTN and multipath ISI, and accomplish carrier-phase tracking. Numerical results confirm that a DP-FTN transmission can yield a 3–6 dB performance improvement over an equivalent DP-Nyquist system that uses a higher modulation order to achieve the same data rate.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"31 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":"115021225","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.2018.8445850
H. Naseri, V. Koivunen
A reliable and accurate positioning technology is crucial for a large variety of wireless services and applications. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the localization problem. However, the problem of cooperative localization using joint distance and direction estimates is still a largely unexplored problem. A novel convex relaxation of the maximum likelihood (ML) estimator for this problem called Semidefinite Programming Hybrid Localization (SDHL) algorithm is proposed in this paper. Numerical results are presented showing that the localization error is significantly reduced in almost every simulation scenario compared to the state of the art. This improvement in localization performance is due to the close approximation of the ML estimator.
{"title":"Convex Relaxation for Maximum-Likelihood Network Localization Using Distance and Direction Data","authors":"H. Naseri, V. Koivunen","doi":"10.1109/SPAWC.2018.8445850","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445850","url":null,"abstract":"A reliable and accurate positioning technology is crucial for a large variety of wireless services and applications. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the localization problem. However, the problem of cooperative localization using joint distance and direction estimates is still a largely unexplored problem. A novel convex relaxation of the maximum likelihood (ML) estimator for this problem called Semidefinite Programming Hybrid Localization (SDHL) algorithm is proposed in this paper. Numerical results are presented showing that the localization error is significantly reduced in almost every simulation scenario compared to the state of the art. This improvement in localization performance is due to the close approximation of the ML estimator.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"18 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":"116993137","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.2018.8445851
C. Kam, S. Kompella, G. Nguyen, J. Wieselthier, A. Ephremides
Since for Markov signals age minimization generally implies prediction error minimization, we pursue in this paper the potential connection between age and what we call effective age. Effective age is loosely defined as an age-related metric that captures both the information structure of the signal and the sampling pattern that is used, and that it is minimized when the error is minimized. We consider several options for sampling mecha nisms and signal models, and we evaluate age and prediction/estimation errors as steps in the quest for a meaningful effective age concept definition.
{"title":"Towards an “Effective Age” Concept","authors":"C. Kam, S. Kompella, G. Nguyen, J. Wieselthier, A. Ephremides","doi":"10.1109/SPAWC.2018.8445851","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445851","url":null,"abstract":"Since for Markov signals age minimization generally implies prediction error minimization, we pursue in this paper the potential connection between age and what we call effective age. Effective age is loosely defined as an age-related metric that captures both the information structure of the signal and the sampling pattern that is used, and that it is minimized when the error is minimized. We consider several options for sampling mecha nisms and signal models, and we evaluate age and prediction/estimation errors as steps in the quest for a meaningful effective age concept definition.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"42 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":"117139424","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.2018.8445943
Duo Xu, F. Fekri
In this paper, we propose a novel method for probabilistic time series prediction based on Recurrent Information Bottleneck (RIB). We propose to incorporate the stochastic latent states for modeling complex and non-linear time series optimized by RIB objective. Compared with previous work, the proposed method can yield better prediction and uncertainty estimation. It's built on the extension of information bottleneck principle to recurrent setting, to find the stochastic latent state maximally informative about the target with low complexity. The experiments over real-world datasets show the proposed method can outperform the state-of-the-art prediction performance on single and multi-dimensional data.
{"title":"Time Series Prediction Via Recurrent Neural Networks with the Information Bottleneck Principle","authors":"Duo Xu, F. Fekri","doi":"10.1109/SPAWC.2018.8445943","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445943","url":null,"abstract":"In this paper, we propose a novel method for probabilistic time series prediction based on Recurrent Information Bottleneck (RIB). We propose to incorporate the stochastic latent states for modeling complex and non-linear time series optimized by RIB objective. Compared with previous work, the proposed method can yield better prediction and uncertainty estimation. It's built on the extension of information bottleneck principle to recurrent setting, to find the stochastic latent state maximally informative about the target with low complexity. The experiments over real-world datasets show the proposed method can outperform the state-of-the-art prediction performance on single and multi-dimensional data.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"93 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":"124650737","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.2018.8445870
V. N. Ioannidis, Panagiotis A. Traganitis, Yanning Shen, G. Giannakis
Networks arise in fields such as sociology, biology, and machine learning among others, to describe complex and often interdependent systems. These increasingly complex systems call for flexible network models that allow for multiple types of interactions among the agents (nodes) known as multilayer networks. A frequently encountered task entails inference of nodal processes across the network given values on a subset of nodes. The present contribution relies on graph kernels, to put forth a novel inference approach that accounts for linear and nonlinear dependencies among nodes and leverages the layered network structure. Numerical tests with synthetic as well as real data corroborate the effectiveness of the proposed kernel-based multilayer learning scheme.
{"title":"Kernel-Based Semi-Supervised Learning Over Multilayer Graphs","authors":"V. N. Ioannidis, Panagiotis A. Traganitis, Yanning Shen, G. Giannakis","doi":"10.1109/SPAWC.2018.8445870","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445870","url":null,"abstract":"Networks arise in fields such as sociology, biology, and machine learning among others, to describe complex and often interdependent systems. These increasingly complex systems call for flexible network models that allow for multiple types of interactions among the agents (nodes) known as multilayer networks. A frequently encountered task entails inference of nodal processes across the network given values on a subset of nodes. The present contribution relies on graph kernels, to put forth a novel inference approach that accounts for linear and nonlinear dependencies among nodes and leverages the layered network structure. Numerical tests with synthetic as well as real data corroborate the effectiveness of the proposed kernel-based multilayer learning scheme.","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":"124662826","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.2018.8445950
Alexandros-Apostolos A. Boulogeorgos, S. Goudos, A. Alexiou
In this paper, we formulate a novel throughput aware user association scheme for ultra dense terahertz (THz) networks. In more detail, we introduce a user association problem, which takes into account the THz channel particularities, the directivity of the BSs' and UEs' antennas, as well as their position and the UEs' minimum rate requirements. Moreover, we provide the solution framework, which is based on the grey wolf optimizer (GWO) and returns the optimal user association table. Finally, we present comparative simulation results, which validate the superiority of the proposed framework against the commonly-used particle swarm optimizer (PSO) approach.
{"title":"Users Association in Ultra Dense THz Networks","authors":"Alexandros-Apostolos A. Boulogeorgos, S. Goudos, A. Alexiou","doi":"10.1109/SPAWC.2018.8445950","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445950","url":null,"abstract":"In this paper, we formulate a novel throughput aware user association scheme for ultra dense terahertz (THz) networks. In more detail, we introduce a user association problem, which takes into account the THz channel particularities, the directivity of the BSs' and UEs' antennas, as well as their position and the UEs' minimum rate requirements. Moreover, we provide the solution framework, which is based on the grey wolf optimizer (GWO) and returns the optimal user association table. Finally, we present comparative simulation results, which validate the superiority of the proposed framework against the commonly-used particle swarm optimizer (PSO) approach.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"19 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":"129035802","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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