The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
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
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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. The 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
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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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On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
arXiv: Information Theory, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ACSSC - On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
2018 52nd Asilomar Conference on Signals Systems and Computers, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ICC - Cell-Free Massive MIMO with Limited Backhaul
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, Merouane DebbahAbstract:We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the Quantized Version of the estimated channel and the Quantized received signal are available at the central processing unit (CPU), and the case when only the Quantized Version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
Stergios I. Roumeliotis - One of the best experts on this subject based on the ideXlab platform.
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IROS - A communication-bandwidth-aware hybrid estimation framework for multi-robot cooperative localization
2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013Co-Authors: Esha D. Nerurkar, Stergios I. RoumeliotisAbstract:This paper presents hybrid Minimum Mean Squared Error-based estimators for wireless sensor networks with time-varying communication-bandwidth constraints, focusing on the particular application of multi-robot Cooperative Localization. When sensor nodes (e.g., robots) communicate only a Quantized Version of their analog measurements to the team, our proposed hybrid filters enable robots to process all available information, i.e., local analog measurements (recorded by its own sensors) as well as remote Quantized measurements (collected and communicated by other sensors). Moreover, these filters are resource-aware and can utilize additional bandwidth, whenever available, to maximize estimation accuracy. Specifically, in this paper, we present two filters, the Hybrid Batch-Quantized Kalman filter (H-BQKF) and the Hybrid Iteratively-Quantized Kalman filter (H-IQKF), that can process local analog measurements along with remote measurements Quantized to any number of bits. We test our proposed filters in simulations and experimentally, and demonstrate that they achieve performance comparable to the standard Kalman filter.
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A communication-bandwidth-aware hybrid estimation framework for multi-robot cooperative localization
2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013Co-Authors: Esha D. Nerurkar, Stergios I. RoumeliotisAbstract:This paper presents hybrid Minimum Mean Squared Error-based estimators for wireless sensor networks with time-varying communication-bandwidth constraints, focusing on the particular application of multi-robot Cooperative Localization. When sensor nodes (e.g., robots) communicate only a Quantized Version of their analog measurements to the team, our proposed hybrid filters enable robots to process all available information, i.e., local analog measurements (recorded by its own sensors) as well as remote Quantized measurements (collected and communicated by other sensors). Moreover, these filters are resource-aware and can utilize additional bandwidth, whenever available, to maximize estimation accuracy. Specifically, in this paper, we present two filters, the Hybrid Batch-Quantized Kalman filter (H-BQKF) and the Hybrid Iteratively-Quantized Kalman filter (H-IQKF), that can process local analog measurements along with remote measurements Quantized to any number of bits. We test our proposed filters in simulations and experimentally, and demonstrate that they achieve performance comparable to the standard Kalman filter.
Alister G. Burr - 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
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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. The 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
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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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On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
arXiv: Information Theory, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ACSSC - On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
2018 52nd Asilomar Conference on Signals Systems and Computers, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ICC - Cell-Free Massive MIMO with Limited Backhaul
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, Merouane DebbahAbstract:We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the Quantized Version of the estimated channel and the Quantized received signal are available at the central processing unit (CPU), and the case when only the Quantized Version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
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
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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. The 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
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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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On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
arXiv: Information Theory, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ACSSC - On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
2018 52nd Asilomar Conference on Signals Systems and Computers, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Dick Maryopi, Kanapathippillai Cumanan, Erik G. LarssonAbstract:Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the Quantized Version of the estimated channel and the Quantized Version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
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ICC - Cell-Free Massive MIMO with Limited Backhaul
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, Merouane DebbahAbstract:We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the Quantized Version of the estimated channel and the Quantized received signal are available at the central processing unit (CPU), and the case when only the Quantized Version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
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
IEEE Transactions on Communications, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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. The 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
arXiv: Information Theory, 2019Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, 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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ICC - Cell-Free Massive MIMO with Limited Backhaul
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, Merouane DebbahAbstract:We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the Quantized Version of the estimated channel and the Quantized received signal are available at the central processing unit (CPU), and the case when only the Quantized Version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
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Cell-Free Massive MIMO with Limited Backhaul
arXiv: Information Theory, 2018Co-Authors: Manijeh Bashar, Alister G. Burr, Kanapathippillai Cumanan, Merouane DebbahAbstract:We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the Quantized Version of the estimated channel and the Quantized received signal are available at the central processing unit (CPU), and the case when only the Quantized Version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub-problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
