Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104337
Hanyu Wang, Jun Fang, Huiping Duan, Hongbin Li
We consider the problem of channel estimation for millimeter wave (mmWave) MIMO-OFDM systems. To efficiently probe the channel, the transmitter forms multiple beams simultaneously and steer them towards different directions. The objective of this paper is to devise the beamtraining patterns and develop an efficient algorithm to estimate the channel. By exploiting the common sparsity inherent in MIMO-OFDM mmWave channels, we develop a sparse bipartite graph coding-based method for joint beamforming training and channel estimation. Simulation results are provided to show the effectiveness of the proposed method.
{"title":"Efficient Beamforming Training and Channel Estimation for mmWave MIMO-OFDM Systems","authors":"Hanyu Wang, Jun Fang, Huiping Duan, Hongbin Li","doi":"10.1109/SAM48682.2020.9104337","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104337","url":null,"abstract":"We consider the problem of channel estimation for millimeter wave (mmWave) MIMO-OFDM systems. To efficiently probe the channel, the transmitter forms multiple beams simultaneously and steer them towards different directions. The objective of this paper is to devise the beamtraining patterns and develop an efficient algorithm to estimate the channel. By exploiting the common sparsity inherent in MIMO-OFDM mmWave channels, we develop a sparse bipartite graph coding-based method for joint beamforming training and channel estimation. Simulation results are provided to show the effectiveness of the proposed method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"4 0 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72758215","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104402
Jiang Zhu, Zhennan Liu, Chunyi Song, Zhiwei Xu, C. Zhong
The problem of channel estimation for millimeter wave (mmWave) systems employing few-bit ADCs is studied. Since the mmWave channel is usually characterized by a geometric channel model, which is low rank and sparse in angular domains, utilizing the low-rank structure along with the sparsity improves the channel estimation performance. Specifically, this paper develops a two stage approach for mmWave channel estimation, namely, a low rank matrix recovery stage and a gridless angle recovery stage. At the first stage, because the low rank matrix undergoes a linear transform followed by a componentwise nonlinear transform, three modules named sparse Bayesian learning, linear minimum mean squared error (LMMSE) module, MMSE module are designed respectively for the signal recovery. At the second stage, utilizing the recovered low rank matrix along with the subspace, MUSIC is adopted to recover the angular information, which further improves the channel estimation performance. Numerical experiments are conducted to show the effectiveness of the proposed approach.
{"title":"Low-rank and Angular Structures aided mmWave MIMO Channel Estimation with Few-bit ADCs","authors":"Jiang Zhu, Zhennan Liu, Chunyi Song, Zhiwei Xu, C. Zhong","doi":"10.1109/SAM48682.2020.9104402","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104402","url":null,"abstract":"The problem of channel estimation for millimeter wave (mmWave) systems employing few-bit ADCs is studied. Since the mmWave channel is usually characterized by a geometric channel model, which is low rank and sparse in angular domains, utilizing the low-rank structure along with the sparsity improves the channel estimation performance. Specifically, this paper develops a two stage approach for mmWave channel estimation, namely, a low rank matrix recovery stage and a gridless angle recovery stage. At the first stage, because the low rank matrix undergoes a linear transform followed by a componentwise nonlinear transform, three modules named sparse Bayesian learning, linear minimum mean squared error (LMMSE) module, MMSE module are designed respectively for the signal recovery. At the second stage, utilizing the recovered low rank matrix along with the subspace, MUSIC is adopted to recover the angular information, which further improves the channel estimation performance. Numerical experiments are conducted to show the effectiveness of the proposed approach.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"70 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75127304","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104276
Fazal-E. Asim, A. D. Almeida, M. Haardt, C. Cavalcante, J. Nossek
The objective of future wireless communication systems is to provide a reliable and high quality of service. We propose multi-linear encoding and decoding strategies by exploiting Kronecker-structured constant modulus constellations for providing a low bit error ratio (BER) in multiple-inputmultiple-output (MIMO) systems. The encoding schemes are based on the one-layer Khatri-Rao, two-layer Khatri-Rao and hybrid Kronecker-Khatri-Rao encoding processes. The corresponding multi-linear decoders consist of closed-form algorithms based on rank-one approximations of matrices and/or tensors. Compared with the convolutional codes with hard and soft Viterbi decoders, the proposed multi-linear encoding and decoding strategies outperform the latter in terms of BER for the same spectral efficiency.
{"title":"Multi-Linear Encoding and Decoding for MIMO Systems","authors":"Fazal-E. Asim, A. D. Almeida, M. Haardt, C. Cavalcante, J. Nossek","doi":"10.1109/SAM48682.2020.9104276","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104276","url":null,"abstract":"The objective of future wireless communication systems is to provide a reliable and high quality of service. We propose multi-linear encoding and decoding strategies by exploiting Kronecker-structured constant modulus constellations for providing a low bit error ratio (BER) in multiple-inputmultiple-output (MIMO) systems. The encoding schemes are based on the one-layer Khatri-Rao, two-layer Khatri-Rao and hybrid Kronecker-Khatri-Rao encoding processes. The corresponding multi-linear decoders consist of closed-form algorithms based on rank-one approximations of matrices and/or tensors. Compared with the convolutional codes with hard and soft Viterbi decoders, the proposed multi-linear encoding and decoding strategies outperform the latter in terms of BER for the same spectral efficiency.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"10 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81717819","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104357
Jiapeng Wu, Panfei Du, Zihao Zhang, Qing Wang
Deep learning (DL) have been widely applied in cognitive radio, including cognitive jamming, cognitive communication and cognitive radar. Many functional properties of DL are amenable to numerous electromagnetic waveform recognition tasks. In fact, there exists a gap between the DL network design and the real time application. This prompts us to adopt the software defined radio testbed to realize the online "cognition-action" demonstration. Via an innovative artificial intelligent (AI)-baseband co-design, the system can realize the modulation recognition and demodulation adaption, which is associating with a demonstration of "cognition-action" cycle. In addition, to realize the online recognition and adaption, we design the over-the-air demodulation reconstruction method. By our experimental results, we demonstrates that such cognitive cycle can bring about noticable improvement in cognitive applications.
{"title":"A Software Defined Radio Testbed for Over-the-air Cognitive Cycle Demonstration","authors":"Jiapeng Wu, Panfei Du, Zihao Zhang, Qing Wang","doi":"10.1109/SAM48682.2020.9104357","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104357","url":null,"abstract":"Deep learning (DL) have been widely applied in cognitive radio, including cognitive jamming, cognitive communication and cognitive radar. Many functional properties of DL are amenable to numerous electromagnetic waveform recognition tasks. In fact, there exists a gap between the DL network design and the real time application. This prompts us to adopt the software defined radio testbed to realize the online \"cognition-action\" demonstration. Via an innovative artificial intelligent (AI)-baseband co-design, the system can realize the modulation recognition and demodulation adaption, which is associating with a demonstration of \"cognition-action\" cycle. In addition, to realize the online recognition and adaption, we design the over-the-air demodulation reconstruction method. By our experimental results, we demonstrates that such cognitive cycle can bring about noticable improvement in cognitive applications.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"55 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85538046","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104353
Junjie Wu, Hongyang An, Zhichao Sun, Jianyu Yang
Low-earth-orbit (LEO) synthetic aperture radar (SAR) can achieve advanced remote sensing applications benefiting from the large beam coverage and long duration time of interested area provided by a geosynchronous (GEO) SAR illuminator. In this paper, an imaging method for GEO-LEO SAR is proposed. After analyzing the sampling characteristics of GEO-LEO SAR, it is found that only 12.5 % sampling data can be acquired in azimuth direction. To handle the serious sub-Nyquist sampling problem and achieve good focusing results, an imaging method combined with multi-receiving technique and compressed sensing is proposed. The multi-receiving imaging model is firstly obtained based on the inverse process of a nonlinear chirp scaling imaging method. Then, the imaging problem of GEO-LEO SAR is converted to an L1 regularization problem. Finally, an effective recovery method named complex approximate message passing is applied to obtain the final nonambiguous image. The simulation results show that the proposed method can suppress 8 times Doppler ambiguity and obtain the well focused image with 3 receiving channels.
{"title":"Multichannel LEO SAR Imaging with GEO SAR Illuminator","authors":"Junjie Wu, Hongyang An, Zhichao Sun, Jianyu Yang","doi":"10.1109/SAM48682.2020.9104353","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104353","url":null,"abstract":"Low-earth-orbit (LEO) synthetic aperture radar (SAR) can achieve advanced remote sensing applications benefiting from the large beam coverage and long duration time of interested area provided by a geosynchronous (GEO) SAR illuminator. In this paper, an imaging method for GEO-LEO SAR is proposed. After analyzing the sampling characteristics of GEO-LEO SAR, it is found that only 12.5 % sampling data can be acquired in azimuth direction. To handle the serious sub-Nyquist sampling problem and achieve good focusing results, an imaging method combined with multi-receiving technique and compressed sensing is proposed. The multi-receiving imaging model is firstly obtained based on the inverse process of a nonlinear chirp scaling imaging method. Then, the imaging problem of GEO-LEO SAR is converted to an L1 regularization problem. Finally, an effective recovery method named complex approximate message passing is applied to obtain the final nonambiguous image. The simulation results show that the proposed method can suppress 8 times Doppler ambiguity and obtain the well focused image with 3 receiving channels.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"86 7 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84001063","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104330
D. Rakhimov, Sai Pavan Deram, Bruno Sokal, Kristina Naskovska, A. D. Almeida, M. Haardt
In this paper, we present a tensor MIMO-GFDM system model that is based on the double contraction operator. Based on the derived system model, we propose an iterative tensor based MIMOGFDM receiver, that is initialized with the channel estimation obtained via pilots transmitted in the first data frame. The proposed algorithm exploits the tensor structure by using several unfoldings of the received signal sequentially to obtain estimates of the transmitted symbols and the channel. Simulation results show the tensor gain for the proposed algorithm in addition to the improved channel estimation. Numerical results confirm that the receiver requires the same amount of pilots as the Zero Forcing (ZF) receiver, while having a better symbol error rate (SER) performance and a better channel estimation accuracy.
{"title":"Iterative Tensor Receiver for MIMO-GFDM systems","authors":"D. Rakhimov, Sai Pavan Deram, Bruno Sokal, Kristina Naskovska, A. D. Almeida, M. Haardt","doi":"10.1109/SAM48682.2020.9104330","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104330","url":null,"abstract":"In this paper, we present a tensor MIMO-GFDM system model that is based on the double contraction operator. Based on the derived system model, we propose an iterative tensor based MIMOGFDM receiver, that is initialized with the channel estimation obtained via pilots transmitted in the first data frame. The proposed algorithm exploits the tensor structure by using several unfoldings of the received signal sequentially to obtain estimates of the transmitted symbols and the channel. Simulation results show the tensor gain for the proposed algorithm in addition to the improved channel estimation. Numerical results confirm that the receiver requires the same amount of pilots as the Zero Forcing (ZF) receiver, while having a better symbol error rate (SER) performance and a better channel estimation accuracy.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"5 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84117299","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104317
Tong Wei, Ping Chu, Ziyang Cheng, B. Liao
In this paper, the problem of transmit beampattern design in multiple-input multiple-output (MIMO) radar with one-bit digital-to-analog converts (DACs) is investigated. The one-bit waveform sequence can be properly designed by minimizing the integrated sidelobe to mainlobe ratio (ISMR) of the transmit beampattern. However, due to the minimum ISMR criterion and discrete constraint, the formulated optimization problem for such a design is nonconvex and thus difficult to tackle directly. To this end, we employ the semidefinite relaxation (SDR) technique to modify the original problem to its convex counterpart. More importantly, the dimension of resulting large-scale semidefinite programming (SDP) problem is greatly reduced via exploiting the special block-spare structure. Simulation results will demonstrate the effectiveness and improved performance of our method.
{"title":"Transmit Beampattern Design for MIMO Radar with One-bit DACs via Block-Sparse SDR","authors":"Tong Wei, Ping Chu, Ziyang Cheng, B. Liao","doi":"10.1109/SAM48682.2020.9104317","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104317","url":null,"abstract":"In this paper, the problem of transmit beampattern design in multiple-input multiple-output (MIMO) radar with one-bit digital-to-analog converts (DACs) is investigated. The one-bit waveform sequence can be properly designed by minimizing the integrated sidelobe to mainlobe ratio (ISMR) of the transmit beampattern. However, due to the minimum ISMR criterion and discrete constraint, the formulated optimization problem for such a design is nonconvex and thus difficult to tackle directly. To this end, we employ the semidefinite relaxation (SDR) technique to modify the original problem to its convex counterpart. More importantly, the dimension of resulting large-scale semidefinite programming (SDP) problem is greatly reduced via exploiting the special block-spare structure. Simulation results will demonstrate the effectiveness and improved performance of our method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"5 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88735488","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104277
Minghe Zhu, Tsung-Hui Chang
For real-time wireless networks with strict latency and energy constraints, deep neural networks have been used to approximate the resource allocation solutions that are previously obtained by advanced but computationally expensive optimization algorithms. In this paper, we consider the multi-user beamforming design problem for sum rate maximization in multi-antenna interference channels. Specifically, we propose a gradient projection inspired recurrent neural network for efficient beamforming optimization. The key ingredient is to explore the structure of the gradient vector of the sum rate so that the network learns only a set of dimension reduced coefficients. Furthermore, we extend it to the robust beamforming design for worst-case sum rate maximization in the presence of bounded channel errors. Numerical results show that the proposed learning networks can achieve high accuracy of the sum rates while with significantly reduced runtime.
{"title":"Optimization Inspired Learning Network for Multiuser Robust Beamforming","authors":"Minghe Zhu, Tsung-Hui Chang","doi":"10.1109/SAM48682.2020.9104277","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104277","url":null,"abstract":"For real-time wireless networks with strict latency and energy constraints, deep neural networks have been used to approximate the resource allocation solutions that are previously obtained by advanced but computationally expensive optimization algorithms. In this paper, we consider the multi-user beamforming design problem for sum rate maximization in multi-antenna interference channels. Specifically, we propose a gradient projection inspired recurrent neural network for efficient beamforming optimization. The key ingredient is to explore the structure of the gradient vector of the sum rate so that the network learns only a set of dimension reduced coefficients. Furthermore, we extend it to the robust beamforming design for worst-case sum rate maximization in the presence of bounded channel errors. Numerical results show that the proposed learning networks can achieve high accuracy of the sum rates while with significantly reduced runtime.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"75 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90979710","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104352
Chen Wu, T. Jin, Yongpeng Dai, D. He, Peng You
In recent years, digital coding and programmable metamaterials have brought new ideas to the fields of communication, detection, and imaging. Optimizing the reference signal matrix to achieve fast, accurate radar imaging has great practical significance for environment detecting. We selected the reference matrix based on the correlation of the matrix, and simulated the impact on the imaging results. The results of the experiment will inspire us to achieve accurate target imaging with less measurements and promote the development of fast and real-time imaging.
{"title":"Coded Aperture Imaging Based on Selected Reference Matrix","authors":"Chen Wu, T. Jin, Yongpeng Dai, D. He, Peng You","doi":"10.1109/SAM48682.2020.9104352","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104352","url":null,"abstract":"In recent years, digital coding and programmable metamaterials have brought new ideas to the fields of communication, detection, and imaging. Optimizing the reference signal matrix to achieve fast, accurate radar imaging has great practical significance for environment detecting. We selected the reference matrix based on the correlation of the matrix, and simulated the impact on the imaging results. The results of the experiment will inspire us to achieve accurate target imaging with less measurements and promote the development of fast and real-time imaging.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"3 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89875308","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104358
Wenjie Huang, Ronghao Lin
We present an efficient method to design long discrete-phase periodic sequence sets with good auto- and cross-ambiguity function properties in the presence of Doppler shifts. Our goal is to minimize the integrated sidelobe level within a desired time-delay and Doppler-shift region of the ambiguity function related metric. A coordinate descent (CD) framework, with efficient updating procedures within the CD iterations, is introduced to achieve low computational complexities. We use numerical examples to demonstrate that we can design long sequence sets with good ambiguity function properties.
{"title":"Efficient Design of Doppler Sensitive Long Discrete-Phase Periodic Sequence Sets for Automotive Radars","authors":"Wenjie Huang, Ronghao Lin","doi":"10.1109/SAM48682.2020.9104358","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104358","url":null,"abstract":"We present an efficient method to design long discrete-phase periodic sequence sets with good auto- and cross-ambiguity function properties in the presence of Doppler shifts. Our goal is to minimize the integrated sidelobe level within a desired time-delay and Doppler-shift region of the ambiguity function related metric. A coordinate descent (CD) framework, with efficient updating procedures within the CD iterations, is introduced to achieve low computational complexities. We use numerical examples to demonstrate that we can design long sequence sets with good ambiguity function properties.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"9 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87305339","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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