The Experts below are selected from a list of 17211 Experts worldwide ranked by ideXlab platform
Shuqiang Xia - One of the best experts on this subject based on the ideXlab platform.
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WCNC - Robust URLLC Packet Scheduling of OFDM Systems
2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020Co-Authors: Jing Cheng, Chao Shen, Shuqiang XiaAbstract:In this paper, we consider the power minimization Problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated Problem is a combinatorial and mixed-integer Nonconvex Problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted $\ell_{1}$-norm, we approximate it by a convex Problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.
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Robust URLLC Packet Scheduling of OFDM Systems
arXiv: Information Theory, 2020Co-Authors: Jing Cheng, Chao Shen, Shuqiang XiaAbstract:In this paper, we consider the power minimization Problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated Problem is a combinatorial and mixed-integer Nonconvex Problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted $\ell_1$-norm, we approximate it by a convex Problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.
Jonathon A Chambers - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive beamforming for multiple input multiple output radar with spatial filtering techniques
Signal Processing, 2018Co-Authors: Junhui Qian, Wei Zhang, Yulong Huang, Jonathon A ChambersAbstract:Abstract In this paper, we consider robust adaptive beamformer design for multiple-input multiple-output (MIMO) radar systems. The desired transmit-receive steering vector is estimated through maximizing the output power subject to constraints upon correlation coefficient and steering vector norm. The original Nonconvex Problem is reformulated as two reduced dimension semi-definite programming (SDP) Problems. An iterative procedure is devised to tackle the two SDP Problems, whose convergence is analytically proven. Based on the estimated desired signal, we are then able to obtain the interference covariance matrix via the matrix rank-constrained minimization method. Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy. Simulation results are presented to confirm the effectiveness and robustness of the proposed approach.
Brendt Wohlberg - One of the best experts on this subject based on the ideXlab platform.
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a Nonconvex admm algorithm for group sparsity with sparse groups
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Rick Chartrand, Brendt WohlbergAbstract:We present an efficient algorithm for computing sparse representations whose nonzero coefficients can be divided into groups, few of which are nonzero. In addition to this group sparsity, we further impose that the nonzero groups themselves be sparse. We use a Nonconvex optimization approach for this purpose, and use an efficient ADMM algorithm to solve the Nonconvex Problem. The efficiency comes from using a novel shrinkage operator, one that minimizes Nonconvex penalty functions for enforcing sparsity and group sparsity simultaneously. Our numerical experiments show that combining sparsity and group sparsity improves signal reconstruction accuracy compared with either property alone. We also find that using Nonconvex optimization significantly improves results in comparison with convex optimization.
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ICASSP - A Nonconvex ADMM algorithm for group sparsity with sparse groups
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Rick Chartrand, Brendt WohlbergAbstract:We present an efficient algorithm for computing sparse representations whose nonzero coefficients can be divided into groups, few of which are nonzero. In addition to this group sparsity, we further impose that the nonzero groups themselves be sparse. We use a Nonconvex optimization approach for this purpose, and use an efficient ADMM algorithm to solve the Nonconvex Problem. The efficiency comes from using a novel shrinkage operator, one that minimizes Nonconvex penalty functions for enforcing sparsity and group sparsity simultaneously. Our numerical experiments show that combining sparsity and group sparsity improves signal reconstruction accuracy compared with either property alone. We also find that using Nonconvex optimization significantly improves results in comparison with convex optimization.
Jing Cheng - One of the best experts on this subject based on the ideXlab platform.
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WCNC - Robust URLLC Packet Scheduling of OFDM Systems
2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020Co-Authors: Jing Cheng, Chao Shen, Shuqiang XiaAbstract:In this paper, we consider the power minimization Problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated Problem is a combinatorial and mixed-integer Nonconvex Problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted $\ell_{1}$-norm, we approximate it by a convex Problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.
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Robust URLLC Packet Scheduling of OFDM Systems
arXiv: Information Theory, 2020Co-Authors: Jing Cheng, Chao Shen, Shuqiang XiaAbstract:In this paper, we consider the power minimization Problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated Problem is a combinatorial and mixed-integer Nonconvex Problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted $\ell_1$-norm, we approximate it by a convex Problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.
Junhui Qian - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive beamforming for multiple input multiple output radar with spatial filtering techniques
Signal Processing, 2018Co-Authors: Junhui Qian, Wei Zhang, Yulong Huang, Jonathon A ChambersAbstract:Abstract In this paper, we consider robust adaptive beamformer design for multiple-input multiple-output (MIMO) radar systems. The desired transmit-receive steering vector is estimated through maximizing the output power subject to constraints upon correlation coefficient and steering vector norm. The original Nonconvex Problem is reformulated as two reduced dimension semi-definite programming (SDP) Problems. An iterative procedure is devised to tackle the two SDP Problems, whose convergence is analytically proven. Based on the estimated desired signal, we are then able to obtain the interference covariance matrix via the matrix rank-constrained minimization method. Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy. Simulation results are presented to confirm the effectiveness and robustness of the proposed approach.