Pub Date : 2022-07-04DOI: 10.1109/spawc51304.2022.9833924
Konstantinos D. Katsanos, P. Lorenzo, G. Alexandropoulos
The technology of Reconfigurable Intelligent Surfaces (RISs) has lately attracted considerable interest from both academia and industry as a low-cost solution for coverage extension and signal propagation control. In this paper, we study the downlink of a multi-cell wideband communication system comprising single-antenna Base Stations (BSs) and their associated single-antenna users, as well as multiple passive RISs. We assume that each BS controls a separate RIS and performs Orthogonal Frequency Division Multiplexing (OFDM) transmissions. Differently from various previous works where the RIS unit elements are considered as frequency-flat phase shifters, we model them as Lorentzian resonators and present a joint design of the BSs’ power allocation, as well as the phase profiles of the multiple RISs, targeting the sum-rate maximization of the multi-cell system. We formulate a challenging distributed nonconvex optimization problem, which is solved via successive concave approximation. The distributed implementation of the proposed design is discussed, and the presented simulation results showcase the interplay of the various system parameters on the sum rate, verifying the performance boosting role of RISs.
{"title":"Distributed Sum-Rate Maximization of Cellular Communications with Multiple Reconfigurable Intelligent Surfaces","authors":"Konstantinos D. Katsanos, P. Lorenzo, G. Alexandropoulos","doi":"10.1109/spawc51304.2022.9833924","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833924","url":null,"abstract":"The technology of Reconfigurable Intelligent Surfaces (RISs) has lately attracted considerable interest from both academia and industry as a low-cost solution for coverage extension and signal propagation control. In this paper, we study the downlink of a multi-cell wideband communication system comprising single-antenna Base Stations (BSs) and their associated single-antenna users, as well as multiple passive RISs. We assume that each BS controls a separate RIS and performs Orthogonal Frequency Division Multiplexing (OFDM) transmissions. Differently from various previous works where the RIS unit elements are considered as frequency-flat phase shifters, we model them as Lorentzian resonators and present a joint design of the BSs’ power allocation, as well as the phase profiles of the multiple RISs, targeting the sum-rate maximization of the multi-cell system. We formulate a challenging distributed nonconvex optimization problem, which is solved via successive concave approximation. The distributed implementation of the proposed design is discussed, and the presented simulation results showcase the interplay of the various system parameters on the sum rate, verifying the performance boosting role of RISs.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121996579","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833983
Tomer Raviv, Nir Shlezinger
Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled data into a larger data set for training deep receivers without transmitting more pilots. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose a dedicated augmentation scheme for exploiting the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Our method models each symbols class as Gaussian, using the available data to estimate its moments, while possibly leveraging data corresponding to related statistical models, e.g., past channel realizations, to improve the estimate. The estimated clusters are used to enrich the data set by generating new samples used for training the DNN. The superiority of our approach is numerically evaluated for training a deep receiver on a linear and non-linear synthetic channels, as well as a COST 2100 channel. We show that our augmentation allows DNN-aided receivers to achieve gain of up to 3dB in bit error rate, compared to regular non-augmented training.
{"title":"Adaptive Data Augmentation for Deep Receivers","authors":"Tomer Raviv, Nir Shlezinger","doi":"10.1109/spawc51304.2022.9833983","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833983","url":null,"abstract":"Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled data into a larger data set for training deep receivers without transmitting more pilots. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose a dedicated augmentation scheme for exploiting the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Our method models each symbols class as Gaussian, using the available data to estimate its moments, while possibly leveraging data corresponding to related statistical models, e.g., past channel realizations, to improve the estimate. The estimated clusters are used to enrich the data set by generating new samples used for training the DNN. The superiority of our approach is numerically evaluated for training a deep receiver on a linear and non-linear synthetic channels, as well as a COST 2100 channel. We show that our augmentation allows DNN-aided receivers to achieve gain of up to 3dB in bit error rate, compared to regular non-augmented training.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115334975","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833921
Georgios Mylonopoulos, C. D’Andrea, S. Buzzi
This paper considers the user localization problem in a single-user and single-cell scenario with an active reconfigurable intelligent surface (RIS). Design perspectives on the RIS configuration and on the power split between the base station (BS) and the active RIS are illustrated, and a location estimator based on multiple signal transmissions and particle filtering (PF) is proposed. The said algorithm exploits additional features and degrees of freedom not available when a passive RIS is used. Theoretical performance bounds are derived and extensive numerical simulations show the effectiveness of the proposed approach with respect to a solution based on passive RIS and corroborate analytical findings.
{"title":"Active Reconfigurable Intelligent Surfaces for User Localization in mmWave MIMO Systems","authors":"Georgios Mylonopoulos, C. D’Andrea, S. Buzzi","doi":"10.1109/spawc51304.2022.9833921","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833921","url":null,"abstract":"This paper considers the user localization problem in a single-user and single-cell scenario with an active reconfigurable intelligent surface (RIS). Design perspectives on the RIS configuration and on the power split between the base station (BS) and the active RIS are illustrated, and a location estimator based on multiple signal transmissions and particle filtering (PF) is proposed. The said algorithm exploits additional features and degrees of freedom not available when a passive RIS is used. Theoretical performance bounds are derived and extensive numerical simulations show the effectiveness of the proposed approach with respect to a solution based on passive RIS and corroborate analytical findings.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129069361","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833923
Ortal Agiv, Nir Shlezinger
Hybrid precoding is expected to play a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost, size, and power. MIMO transmitters are required to frequently adapt their precoding patterns based on the variation in the channel conditions. In the hybrid setting, such an adaptation often involves lengthy optimization which may affect the network performance. In this work we employ the emerging learn-to-optimize paradigm to enable rapid optimization of hybrid precoders. In particular, we leverage data to learn iteration-dependent hyperparameter setting of projected gradient optimization, thus preserving the fully interpretable flow of the optimizer while improving its convergence speed. Numerical results demonstrate that our approach yields six to twelve times faster convergence compared to conventional optimization with shared hyperparameters, while achieving similar and even improved sum-rate performance.
{"title":"Learn to Rapidly Optimize Hybrid Precoding","authors":"Ortal Agiv, Nir Shlezinger","doi":"10.1109/spawc51304.2022.9833923","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833923","url":null,"abstract":"Hybrid precoding is expected to play a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost, size, and power. MIMO transmitters are required to frequently adapt their precoding patterns based on the variation in the channel conditions. In the hybrid setting, such an adaptation often involves lengthy optimization which may affect the network performance. In this work we employ the emerging learn-to-optimize paradigm to enable rapid optimization of hybrid precoders. In particular, we leverage data to learn iteration-dependent hyperparameter setting of projected gradient optimization, thus preserving the fully interpretable flow of the optimizer while improving its convergence speed. Numerical results demonstrate that our approach yields six to twelve times faster convergence compared to conventional optimization with shared hyperparameters, while achieving similar and even improved sum-rate performance.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129908550","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833992
Rui Deng, Wenyi Zhang
Massive connectivity demands cheap hardware and low computational complexity. We propose a scheme without need of channel state information (CSI) or multiple antennas, in which users transmit messages encoded by Bloom filter with On-Off Keying (OOK) modulation, and base station (BS) performs hard-decision envelope detection on received signals. For scenarios with inter-user synchronization (IUS), a Noisy-Combinatorial Orthogonal Matching Pursuit (NCOMP) decoding strategy is applied, and for scenarios without IUS, a sliding window strategy is proposed to modify the NCOMP decoding strategy. Based on a many-access model, we study the theoretical performance of our scheme for activity recognition and message transmission problems. Theoretical analysis guarantees that the error probability of our scheme vanishes asymptotically with the number of users, and this trend is verified by numerical experiments for finite number of users.
{"title":"Massive Connectivity with Hard-decision Envelope Detection and Bloom Filter Based Coding","authors":"Rui Deng, Wenyi Zhang","doi":"10.1109/spawc51304.2022.9833992","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833992","url":null,"abstract":"Massive connectivity demands cheap hardware and low computational complexity. We propose a scheme without need of channel state information (CSI) or multiple antennas, in which users transmit messages encoded by Bloom filter with On-Off Keying (OOK) modulation, and base station (BS) performs hard-decision envelope detection on received signals. For scenarios with inter-user synchronization (IUS), a Noisy-Combinatorial Orthogonal Matching Pursuit (NCOMP) decoding strategy is applied, and for scenarios without IUS, a sliding window strategy is proposed to modify the NCOMP decoding strategy. Based on a many-access model, we study the theoretical performance of our scheme for activity recognition and message transmission problems. Theoretical analysis guarantees that the error probability of our scheme vanishes asymptotically with the number of users, and this trend is verified by numerical experiments for finite number of users.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128745921","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833990
Tze-Yang Tung, Deniz Gündüz
We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect, which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 on average in terms of the multi-scale structural similarity index measure (MS-SSIM).
{"title":"Deep-Learning-Aided Wireless Video Transmission","authors":"Tze-Yang Tung, Deniz Gündüz","doi":"10.1109/spawc51304.2022.9833990","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833990","url":null,"abstract":"We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect, which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 on average in terms of the multi-scale structural similarity index measure (MS-SSIM).","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"290 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120970798","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834033
I. A. Arriaga-Trejo, A. Orozco-Lugo
In this paper, the identification of widely linear (WL) systems using sequences with an impulse-like periodic autocorrelation and a zero complementary periodic autocorrelation is addressed. Closed form expressions for sequences with unitary peak to average power ratio (PAPR) suited for the identification of these systems are presented. The analysis shows that the filter impulse responses of the WL system can be estimated from the second order statistics of the system output and the probing sequence. Numerical simulations are provided to verify the variance of the estimation error.
{"title":"Widely Linear System Estimation with Zero Complementary Autocorrelation Sequences","authors":"I. A. Arriaga-Trejo, A. Orozco-Lugo","doi":"10.1109/spawc51304.2022.9834033","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834033","url":null,"abstract":"In this paper, the identification of widely linear (WL) systems using sequences with an impulse-like periodic autocorrelation and a zero complementary periodic autocorrelation is addressed. Closed form expressions for sequences with unitary peak to average power ratio (PAPR) suited for the identification of these systems are presented. The analysis shows that the filter impulse responses of the WL system can be estimated from the second order statistics of the system output and the probing sequence. Numerical simulations are provided to verify the variance of the estimation error.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115090352","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833920
Imène Ghamnia, D. Slock, Y. Yuan-Wu
In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.
{"title":"Multi-Cell MIMO User Rate Balancing with Imperfect CSIT: SESIP vs. RESIP","authors":"Imène Ghamnia, D. Slock, Y. Yuan-Wu","doi":"10.1109/spawc51304.2022.9833920","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833920","url":null,"abstract":"In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330229","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834024
Jianxiang Yan, Jianping Zheng
In this paper, the transmit signal design of dual function radar communication (DFRC) with 1-bit digital-to-analog converters (DACs) is studied, where one multiple-antenna DFRC base station tracks and communicates with multiple users simultaneously. Concretely, the 1-bit transmit signal design is formulated as an optimization problem with weighted radar and communication mean-square errors as the objective function. To solve this problem efficiently, the alternating minimization framework is employed. Specifically, the alternating direction method of multipliers algorithm and the coordinate descent method algorithm are presented in the optimization of transmit signal. Finally, computer simulations are given to demonstrate the effectiveness of the proposed method.
{"title":"Transmit Signal Design of MIMO Dual-Function Radar Communication With 1-bit DACs","authors":"Jianxiang Yan, Jianping Zheng","doi":"10.1109/spawc51304.2022.9834024","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834024","url":null,"abstract":"In this paper, the transmit signal design of dual function radar communication (DFRC) with 1-bit digital-to-analog converters (DACs) is studied, where one multiple-antenna DFRC base station tracks and communicates with multiple users simultaneously. Concretely, the 1-bit transmit signal design is formulated as an optimization problem with weighted radar and communication mean-square errors as the objective function. To solve this problem efficiently, the alternating minimization framework is employed. Specifically, the alternating direction method of multipliers algorithm and the coordinate descent method algorithm are presented in the optimization of transmit signal. Finally, computer simulations are given to demonstrate the effectiveness of the proposed method.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711631","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833976
M. Rajiv, U. Mitra
A two-layer perturbation scheme is introduced to the blind communication strategy based on modulation on conjugate-reciprocal zeros. The proposed strategy enables the intended receiver to decode while obscuring the message to an unintended receiver. A new decoder strategy is proposed and analyzed. Furthermore, the "learning" rate of the unintended receiver is analyzed via the computation of Cramér-Rao bounds. Numerical results show that the proposed scheme does provide a meaningful loss in performance to the unintended receiver.
{"title":"Securing BMOCZ Signaling: A Two Layer Artificial Noise Injection Scheme","authors":"M. Rajiv, U. Mitra","doi":"10.1109/spawc51304.2022.9833976","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833976","url":null,"abstract":"A two-layer perturbation scheme is introduced to the blind communication strategy based on modulation on conjugate-reciprocal zeros. The proposed strategy enables the intended receiver to decode while obscuring the message to an unintended receiver. A new decoder strategy is proposed and analyzed. Furthermore, the \"learning\" rate of the unintended receiver is analyzed via the computation of Cramér-Rao bounds. Numerical results show that the proposed scheme does provide a meaningful loss in performance to the unintended receiver.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132929523","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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