The Experts below are selected from a list of 6030 Experts worldwide ranked by ideXlab platform
Pei Xiao - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Pei XiaoAbstract:Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
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max min rate of cell free massive mimo uplink with optimal uniform quantization
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Hien Quoc Ngo, Pei XiaoAbstract:Cell-free massive multiple-input–multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max–min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink–downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.
Manijeh Bashar - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Pei XiaoAbstract:Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
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max min rate of cell free massive mimo uplink with optimal uniform quantization
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Hien Quoc Ngo, Pei XiaoAbstract:Cell-free massive multiple-input–multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max–min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink–downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.
Emil Bjornson - One of the best experts on this subject based on the ideXlab platform.
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admm based one bit Quantized Signal detection for massive mimo systems with hardware impairments
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Ozlem Tugfe Demir, Emil BjornsonAbstract:This paper considers Signal detection in massive multiple-input multiple-output (MIMO) systems with general additive hardware impairments and one-bit quantization. First, we present the quantization-unaware and Bussgang decomposition-based linear receivers by generalizing them for the considered hardware impairment model. We propose an optimization problem to estimate the uplink data Signals by choosing a suitable cost function that treats the unQuantized received Signal at the base station as the variable. We exploit the additional structure of the one-bit quantization and Signal modulation by including proper constraints. To solve the non-convex quadratically-constrained quadratic programming (QCQP) problem, we propose an ADMM-based algorithm with closed-form update equations. Then, we replace the harsh projectors in the updates with their soft versions to improve the detection performance. We show that the proposed ADMM-based algorithm outperforms the state-of-the-art linear receivers significantly in terms of bit error rate (BER) and the performance gain increases with the number of antennas and users.
Kanapathippillai Cumanan - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Pei XiaoAbstract:Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
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max min rate of cell free massive mimo uplink with optimal uniform quantization
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Hien Quoc Ngo, Pei XiaoAbstract:Cell-free massive multiple-input–multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max–min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink–downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.
Merouane Debbah - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Pei XiaoAbstract:Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
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max min rate of cell free massive mimo uplink with optimal uniform quantization
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Kanapathippillai Cumanan, Alister G. Burr, Merouane Debbah, Hien Quoc Ngo, Pei XiaoAbstract:Cell-free massive multiple-input–multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received Signal by the conjugate of the estimated channel, and send back a Quantized version of this weighted Signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the Quantized version of the estimated channel and the Quantized Signal are available at the CPU, and the case when only the Quantized weighted Signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max–min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink–downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.
