Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020515
Chao Ji, Zaichen Zhang, X. You, Chuan Zhang
A general design method for pipelined belief propagation (BP) polar decoder is proposed in this paper. By associating data flow graph (DFG) of polar encoder with factor graph (FG) of BP polar decoder, regular structure of FG helps to determine the generation formula representing pipelined BP polar decoder. Using Python as a compiler, the generation formula is translated into a series of synthesizable Verilog HDL files for various code lengths and parallelisms. Considering the balance between performance and cost, this formula-to-hardware design can be extended to explore the design space, where we are able to make tradeoffs according to specific application requirements. With the evaluation of auto-generation system, implementation results have shown that our design is reliable and practicable.
{"title":"Pipelined Implementations for Belief Propagation Polar Decoder: From Formula to Hardware","authors":"Chao Ji, Zaichen Zhang, X. You, Chuan Zhang","doi":"10.1109/SiPS47522.2019.9020515","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020515","url":null,"abstract":"A general design method for pipelined belief propagation (BP) polar decoder is proposed in this paper. By associating data flow graph (DFG) of polar encoder with factor graph (FG) of BP polar decoder, regular structure of FG helps to determine the generation formula representing pipelined BP polar decoder. Using Python as a compiler, the generation formula is translated into a series of synthesizable Verilog HDL files for various code lengths and parallelisms. Considering the balance between performance and cost, this formula-to-hardware design can be extended to explore the design space, where we are able to make tradeoffs according to specific application requirements. With the evaluation of auto-generation system, implementation results have shown that our design is reliable and practicable.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115413222","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020441
Yann Delomier, B. Gal, J. Crenne, C. Jégo
Recent advances in 5G digital communication standard implementations advocate for the use of polar codes for the Enhanced Mobile Broad Band (EMBB) control channels. However, in many cases, implementing efficient hardware decoder over a short duration is very challenging. Specialized knowledge is required to facilitate testing, rapid design iterations and fast prototyping. In this paper, we present a model-based design methodology to generate efficient hardware SC polar decoders from high-level synthesis tools. The abstraction level flexibility is evaluated and generated decoders architectures are compared to competing approaches. It is shown that the fine-tuning of computation parallelism, bit width, pruning level and working frequency enable high throughput decoder designs with moderate hardware complexities. Decoding throughput between 10 to 310 Mbit/s and hardware complexity between 1,000 and 21,000 LUTs are reported for the generated architectures.
{"title":"Generation of Efficient Self-adaptive Hardware Polar Decoders Using High-Level Synthesis","authors":"Yann Delomier, B. Gal, J. Crenne, C. Jégo","doi":"10.1109/SiPS47522.2019.9020441","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020441","url":null,"abstract":"Recent advances in 5G digital communication standard implementations advocate for the use of polar codes for the Enhanced Mobile Broad Band (EMBB) control channels. However, in many cases, implementing efficient hardware decoder over a short duration is very challenging. Specialized knowledge is required to facilitate testing, rapid design iterations and fast prototyping. In this paper, we present a model-based design methodology to generate efficient hardware SC polar decoders from high-level synthesis tools. The abstraction level flexibility is evaluated and generated decoders architectures are compared to competing approaches. It is shown that the fine-tuning of computation parallelism, bit width, pruning level and working frequency enable high throughput decoder designs with moderate hardware complexities. Decoding throughput between 10 to 310 Mbit/s and hardware complexity between 1,000 and 21,000 LUTs are reported for the generated architectures.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529070","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020603
H. Habi, H. Messer
The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions
{"title":"RNN Models for Rain Detection","authors":"H. Habi, H. Messer","doi":"10.1109/SiPS47522.2019.9020603","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020603","url":null,"abstract":"The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128469995","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020428
Kuan-Chun Chen, Ching-Yao Chou, A. Wu
Compressive sensing (CS) is a novel technique to reduce overall transmission power in wireless sensors. For physiological signals telemonitoring of wearable devices, chip area and power efficiency need to be considered simultaneously. There are many prior studies aim to develop algorithms that applied to CS reconstruction chips with reconfigurable architecture. However, representative dictionaries are also important when these CS reconstruction chips are verified in real-time physiological signals monitoring tasks. That is, a more representative dictionary can not only enhance the reconstruction performance of these chips but also alleviate memory overhead. In this paper, we apply the concept of co-design between sparse coding algorithms and learned dictionaries. We also explore the representativeness and compatibility of each learned dictionary. In addition, the computational complexity of each reconstruction algorithm is provided through simulations. Our results show that the dictionaries trained by fast iterative shrinkage-thresholding algorithm (FISTA) are more representative according to the quality of reconstruction for physiological signals monitoring. Besides, FISTA reduces more than 90% of the computational time compared with other hardware-friendly reconstruction algorithms.
{"title":"Co-Design of Sparse Coding and Dictionary Learning for Real-Time Physiological Signals Monitoring","authors":"Kuan-Chun Chen, Ching-Yao Chou, A. Wu","doi":"10.1109/SiPS47522.2019.9020428","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020428","url":null,"abstract":"Compressive sensing (CS) is a novel technique to reduce overall transmission power in wireless sensors. For physiological signals telemonitoring of wearable devices, chip area and power efficiency need to be considered simultaneously. There are many prior studies aim to develop algorithms that applied to CS reconstruction chips with reconfigurable architecture. However, representative dictionaries are also important when these CS reconstruction chips are verified in real-time physiological signals monitoring tasks. That is, a more representative dictionary can not only enhance the reconstruction performance of these chips but also alleviate memory overhead. In this paper, we apply the concept of co-design between sparse coding algorithms and learned dictionaries. We also explore the representativeness and compatibility of each learned dictionary. In addition, the computational complexity of each reconstruction algorithm is provided through simulations. Our results show that the dictionaries trained by fast iterative shrinkage-thresholding algorithm (FISTA) are more representative according to the quality of reconstruction for physiological signals monitoring. Besides, FISTA reduces more than 90% of the computational time compared with other hardware-friendly reconstruction algorithms.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957040","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020319
Jing Zeng, Jun Lin, Zhongfeng Wang, Yun Chen
Massive Multi-Input Multi-Output (MIMO) is one of the key technologies for the fifth generation communication systems. Conjugate Gradient (CG) algorithm approximates the minimum mean-square error (MMSE) in an iterative manner, which avoids full matrix inversion. Pre-conditioned CG (PCG) was presented to improve the robustness of CG method. However, for the PCG, a sparse matrix inversion is still required in preprocessing and the performance is only comparable to MMSE. In this paper, a hybrid PCG algorithm (HPCG) with sequential update is proposed with superior performance and low complexity. The preconditioned matrix is replaced by a diagonal matrix by exploring its characteristics, which avoids matrix inversion and incomplete Cholesky factorization. Besides, to improve the bit error performance, a sequential update strategy is employed for estimated signals after PCG detection. For a MIMO system with 128 receive antennas, simulation results show the proposed HPCG algorithm outperforms MMSE by 0.25 dB to 1.5 dB under different numbers of users. Based on the channel hardening theories, several signal vectors can be transmitted in the same channel condition. When 10 signal vectors are considered, compared to the other CG based algorithms, the overall complexity of HPCG can be reduced by 3.9% to 56%.
{"title":"Hybrid Preconditioned CG Detection with Sequential Update for Massive MIMO Systems","authors":"Jing Zeng, Jun Lin, Zhongfeng Wang, Yun Chen","doi":"10.1109/SiPS47522.2019.9020319","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020319","url":null,"abstract":"Massive Multi-Input Multi-Output (MIMO) is one of the key technologies for the fifth generation communication systems. Conjugate Gradient (CG) algorithm approximates the minimum mean-square error (MMSE) in an iterative manner, which avoids full matrix inversion. Pre-conditioned CG (PCG) was presented to improve the robustness of CG method. However, for the PCG, a sparse matrix inversion is still required in preprocessing and the performance is only comparable to MMSE. In this paper, a hybrid PCG algorithm (HPCG) with sequential update is proposed with superior performance and low complexity. The preconditioned matrix is replaced by a diagonal matrix by exploring its characteristics, which avoids matrix inversion and incomplete Cholesky factorization. Besides, to improve the bit error performance, a sequential update strategy is employed for estimated signals after PCG detection. For a MIMO system with 128 receive antennas, simulation results show the proposed HPCG algorithm outperforms MMSE by 0.25 dB to 1.5 dB under different numbers of users. Based on the channel hardening theories, several signal vectors can be transmitted in the same channel condition. When 10 signal vectors are considered, compared to the other CG based algorithms, the overall complexity of HPCG can be reduced by 3.9% to 56%.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126381982","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020314
Xiangyang Zhang, Hongqing Liu, Zhen Luo, Yi Zhou
This paper describes a blind image reconstruction algorithm for blurred image under Poisson noise. To that aim, in this work, the group sparse domain is explored to sparsely represent the image and blur kernel, and then $ell_{1} -$norm is utilized to enforce the sparse solutions. In doing so, a joint optimization framework is developed to estimate the blur kernel matrix while removing Poisson noise. To effectively solve the developed optimization, a two-step iteration scheme involving two sub-problems is proposed. For each subproblem, the alternating direction method of multipliers (ADMM) algorithm is devised to estimate the blur or denoise. The experimental simulations demonstrate that the proposed algorithm is superior to other approaches in terms of restoration quality and performance metrics.
{"title":"Joint Image Deblur and Poisson Denoising based on Adaptive Dictionary Learning","authors":"Xiangyang Zhang, Hongqing Liu, Zhen Luo, Yi Zhou","doi":"10.1109/SiPS47522.2019.9020314","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020314","url":null,"abstract":"This paper describes a blind image reconstruction algorithm for blurred image under Poisson noise. To that aim, in this work, the group sparse domain is explored to sparsely represent the image and blur kernel, and then $ell_{1} -$norm is utilized to enforce the sparse solutions. In doing so, a joint optimization framework is developed to estimate the blur kernel matrix while removing Poisson noise. To effectively solve the developed optimization, a two-step iteration scheme involving two sub-problems is proposed. For each subproblem, the alternating direction method of multipliers (ADMM) algorithm is devised to estimate the blur or denoise. The experimental simulations demonstrate that the proposed algorithm is superior to other approaches in terms of restoration quality and performance metrics.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303495","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020610
Kai-Huan Shen, P. Tsai
Prioritized experience replay has been widely used in many online reinforcement learning algorithms, providing high efficiency in exploiting past experiences. However, a large replay buffer consumes system storage significantly. Thus, in this paper, a segmentation and classification scheme is proposed. The distribution of temporal-difference errors (TD errors) is first segmented. The experience for network training is classified according to its updated TD error. Then, a swap mechanism for similar experiences is implemented to change the lifetimes of experiences in the replay buffer. The proposed scheme is incorporated in the Deep Deterministic Policy Gradient (DDPG) algorithm, and the Inverted Pendulum and Inverted Double Pendulum tasks are used for verification. From the experiments, our proposed mechanism can effectively remove the buffer redundancy and further reduce the correlation of experiences in the replay buffer. Thus, better learning performance with reduced memory size is achieved at the cost of additional computations of updated TD errors.
{"title":"Memory Reduction through Experience Classification f or Deep Reinforcement Learning with Prioritized Experience Replay","authors":"Kai-Huan Shen, P. Tsai","doi":"10.1109/SiPS47522.2019.9020610","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020610","url":null,"abstract":"Prioritized experience replay has been widely used in many online reinforcement learning algorithms, providing high efficiency in exploiting past experiences. However, a large replay buffer consumes system storage significantly. Thus, in this paper, a segmentation and classification scheme is proposed. The distribution of temporal-difference errors (TD errors) is first segmented. The experience for network training is classified according to its updated TD error. Then, a swap mechanism for similar experiences is implemented to change the lifetimes of experiences in the replay buffer. The proposed scheme is incorporated in the Deep Deterministic Policy Gradient (DDPG) algorithm, and the Inverted Pendulum and Inverted Double Pendulum tasks are used for verification. From the experiments, our proposed mechanism can effectively remove the buffer redundancy and further reduce the correlation of experiences in the replay buffer. Thus, better learning performance with reduced memory size is achieved at the cost of additional computations of updated TD errors.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"23 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114095066","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 : 2019-10-01DOI: 10.1109/sips47522.2019.9020593
{"title":"SiPS 2019 Author Index","authors":"","doi":"10.1109/sips47522.2019.9020593","DOIUrl":"https://doi.org/10.1109/sips47522.2019.9020593","url":null,"abstract":"","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554218","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}
Physical unclonable function (PUF) is a promising security primitive. Configurable ring oscillator (CRO) PUF is an evolvement of conventional RO PUF, which improves the entropy and decrease the hardware cost by introducing configurability. Compared with other types of PUF structures, CRO PUFs are FPGA friendly. In this paper, a dynamic reconfigurable mechanism is proposed for the CRO PUF in FPGA implementation. Three different CRO PUFs are implemented using the proposed reconfigurable method and each CRO can be implemented in a single configurable logic block (CLB) of FPGA. Based on the partial reconFigure functions provided by Xilinx FPGAs, the PUF structures can be configured to any of the three PUF structures. The experimental results show that the dynamic reconfigurable PUF structure has a higher hardware efficiency, reliability and stability compared with the previous works.
{"title":"Dynamic Reconfigurable PUFs Based on FPGA","authors":"Yijun Cui, Chenghua Wang, Yunpeng Chen, Ziwei Wei, Mengxian Chen, Weiqiang Liu","doi":"10.1109/SiPS47522.2019.9020444","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020444","url":null,"abstract":"Physical unclonable function (PUF) is a promising security primitive. Configurable ring oscillator (CRO) PUF is an evolvement of conventional RO PUF, which improves the entropy and decrease the hardware cost by introducing configurability. Compared with other types of PUF structures, CRO PUFs are FPGA friendly. In this paper, a dynamic reconfigurable mechanism is proposed for the CRO PUF in FPGA implementation. Three different CRO PUFs are implemented using the proposed reconfigurable method and each CRO can be implemented in a single configurable logic block (CLB) of FPGA. Based on the partial reconFigure functions provided by Xilinx FPGAs, the PUF structures can be configured to any of the three PUF structures. The experimental results show that the dynamic reconfigurable PUF structure has a higher hardware efficiency, reliability and stability compared with the previous works.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134143626","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 : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020316
Rui Li, Tingqiang Deng, Yongming Huang, Chuan Zhang, Luxi Yang
Hybrid beamforming is a promising solution for multiple-input multiple-output (MIMO) systems with large scale antennas due to its low-cost and good performance compared with pure digital and analog beamforming. Unfortunately, conventional angle-of-arrival (AoA) estimation methods, such as MUSIC and ESPRIT algorithms, need a lot of calculations and must solve the issue of phase ambiguity. Therefore, this paper proposes a novel AoA estimation method based on layered ensemble learning. Because the training process can be completed off-line, only estimating complexity is taken into account which make the AoA detection process low complexity and meet real-time requirements. The simulation results indicate that the accuracy of the proposed AOA estimation method is higher than that of traditional algorithms. In addition, our proposed method is robust to the phase error.
{"title":"A Novel Approach to Angle-of-Arrival Estimation Based on Layered Ensemble Learning","authors":"Rui Li, Tingqiang Deng, Yongming Huang, Chuan Zhang, Luxi Yang","doi":"10.1109/SiPS47522.2019.9020316","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020316","url":null,"abstract":"Hybrid beamforming is a promising solution for multiple-input multiple-output (MIMO) systems with large scale antennas due to its low-cost and good performance compared with pure digital and analog beamforming. Unfortunately, conventional angle-of-arrival (AoA) estimation methods, such as MUSIC and ESPRIT algorithms, need a lot of calculations and must solve the issue of phase ambiguity. Therefore, this paper proposes a novel AoA estimation method based on layered ensemble learning. Because the training process can be completed off-line, only estimating complexity is taken into account which make the AoA detection process low complexity and meet real-time requirements. The simulation results indicate that the accuracy of the proposed AOA estimation method is higher than that of traditional algorithms. In addition, our proposed method is robust to the phase error.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115956714","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：