The Experts below are selected from a list of 11319 Experts worldwide ranked by ideXlab platform
Shahram Yousefi - One of the best experts on this subject based on the ideXlab platform.
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Capacity analysis and bit Allocation design for variable-resolution ADCs in Massive MIMO
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of $\cite{Zakir1}$
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MILCOM - Capacity Analysis and Bit Allocation Design for Variable-Resolution ADCs in Massive MIMO
MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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SPCOM - Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
2018 International Conference on Signal Processing and Communications (SPCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of [1].
Michael Jones - One of the best experts on this subject based on the ideXlab platform.
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generation of an industry specific physico chemical Allocation Matrix application in the dairy industry and implications for systems analysis 9 pp
International Journal of Life Cycle Assessment, 2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy by-products that would otherwise be neglected. Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology. The industry specific Allocation Matrix approach will assist with Allocation in multi-product LCAs where the level of technology in an industry is similar. The Matrix will also benefit dairy manufacturing companies and help them more accurately allocate resources and impacts (i.e. costs) to different products within the one plant. It is recommended that similar physico-chemical Allocation matrices be developed for other industry sectors with a view of ultimately coupling them with input-output analysis.
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Generation of an Industry-specific Physico-chemical Allocation Matrix. Application in the Dairy Industry and Implications for Systems Analysis (9 pp)
The International Journal of Life Cycle Assessment, 2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Background, Aims and Scope Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Methods Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy by-products that would otherwise be neglected. Results and Discussion Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. Conclusion The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology. Recommendation and Perspective The industry specific Allocation Matrix approach will assist with Allocation in multi-product LCAs where the level of technology in an industry is similar. The Matrix will also benefit dairy manufacturing companies and help them more accurately allocate resources and impacts (i.e. costs) to different products within the one plant. It is recommended that similar physico-chemical Allocation matrices be developed for other industry sectors with a view of ultimately coupling them with input-output analysis.
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Application in the Dairy Industry and Implications for Systems Analysis
2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Background, Aims and Scope. Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Methods. Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy 'by-products' that would otherwise be neglected. Results and Discussion. Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. Conclusion. The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology.
I. Zakir Ahmed - One of the best experts on this subject based on the ideXlab platform.
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Capacity analysis and bit Allocation design for variable-resolution ADCs in Massive MIMO
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of $\cite{Zakir1}$
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MILCOM - Capacity Analysis and Bit Allocation Design for Variable-Resolution ADCs in Massive MIMO
MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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SPCOM - Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
2018 International Conference on Signal Processing and Communications (SPCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of [1].
Andrew J Feitz - One of the best experts on this subject based on the ideXlab platform.
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generation of an industry specific physico chemical Allocation Matrix application in the dairy industry and implications for systems analysis 9 pp
International Journal of Life Cycle Assessment, 2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy by-products that would otherwise be neglected. Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology. The industry specific Allocation Matrix approach will assist with Allocation in multi-product LCAs where the level of technology in an industry is similar. The Matrix will also benefit dairy manufacturing companies and help them more accurately allocate resources and impacts (i.e. costs) to different products within the one plant. It is recommended that similar physico-chemical Allocation matrices be developed for other industry sectors with a view of ultimately coupling them with input-output analysis.
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Generation of an Industry-specific Physico-chemical Allocation Matrix. Application in the Dairy Industry and Implications for Systems Analysis (9 pp)
The International Journal of Life Cycle Assessment, 2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Background, Aims and Scope Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Methods Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy by-products that would otherwise be neglected. Results and Discussion Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. Conclusion The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology. Recommendation and Perspective The industry specific Allocation Matrix approach will assist with Allocation in multi-product LCAs where the level of technology in an industry is similar. The Matrix will also benefit dairy manufacturing companies and help them more accurately allocate resources and impacts (i.e. costs) to different products within the one plant. It is recommended that similar physico-chemical Allocation matrices be developed for other industry sectors with a view of ultimately coupling them with input-output analysis.
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Application in the Dairy Industry and Implications for Systems Analysis
2007Co-Authors: Andrew J Feitz, Sven Lundie, Gary Dennien, Marc Morain, Michael JonesAbstract:Background, Aims and Scope. Allocation is required when quantifying environmental impacts of individual products from multi-product manufacturing plants. The International Organization for Standardization (ISO) recommends in ISO 14041 that Allocation should reflect underlying physical relationships between inputs and outputs, or in the absence of such knowledge, Allocation should reflect other relationships (e.g. economic value). Economic Allocation is generally recommended if process specific information on the manufacturing process is lacking. In this paper, a physico-chemical Allocation Matrix, based on industry-specific data from the dairy industry, is developed and discussed as an alternative Allocation method. Methods. Operational data from 17 dairy manufacturing plants was used to develop an industry specific physico-chemical Allocation Matrix. Through an extensive process of substraction/substitution, it is possible to determine average resource use (e.g. electricity, thermal energy, water, etc) and wastewater emissions for individual dairy products within multi-product manufacturing plants. The average operational data for individual products were normalised to maintain industry confidentiality and then used as an industry specific Allocation Matrix. The quantity of raw milk required per product is based on the milk solids basis to account for dairy 'by-products' that would otherwise be neglected. Results and Discussion. Applying fixed type Allocation methods (e.g. economic) for all input and outputs based on the quantity of product introduces order of magnitude sized deviations from physico-chemical Allocation in some cases. The error associated with the quality of the whole of factory plant data or truncation error associated with setting system boundaries is insignificant in comparison. The profound effects of the results on systems analysis are discussed. The results raise concerns about using economic Allocation as a default when allocating intra-industry sectoral flows (i.e. mass and process energy) in the absence of detailed technical information. It is recommended that economic Allocation is better suited as a default for reflecting inter-industry sectoral flows. Conclusion. The study highlights the importance of accurate causal Allocation procedures that reflect industry-specific production methods. Generation of industry-specific Allocation matrices is possible through a process of substitution/subtraction and optimisation. Allocation using such matrices overcomes the inherit bias of mass, process energy or price Allocations for a multi-product manufacturing plant and gives a more realistic indication of resource use or emissions per product. The approach appears to be advantageous for resource use or emissions Allocation if data is only available on a whole of factory basis for several plants with a similar level of technology.
Hamid R. Sadjadpour - One of the best experts on this subject based on the ideXlab platform.
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Capacity analysis and bit Allocation design for variable-resolution ADCs in Massive MIMO
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
arXiv: Signal Processing, 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of $\cite{Zakir1}$
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MILCOM - Capacity Analysis and Bit Allocation Design for Variable-Resolution ADCs in Massive MIMO
MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:We derive an expression for the capacity of massive multiple-input multiple-output Millimeter wave (mmWave) channel where the receiver is equipped with a variable-resolution Analog to Digital Converter (ADC) and a hybrid combiner. The capacity is shown to be a function of Cramer-Rao Lower Bound (CRLB) for a given bit-Allocation Matrix and hybrid combiner. The condition for optimal ADC bit-Allocation under a receiver power constraint is derived. This is derived based on the maximization of capacity with respect to bit-Allocation Matrix for a given channel, hybrid precoder, and hybrid combiner. It is shown that this condition coincides with that obtained using the CRLB minimization proposed by Ahmed et al. Monte-carlo simulations show that the capacity calculated using the proposed condition matches very closely with the capacity obtained using the Exhaustive Search bit Allocation.
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SPCOM - Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design
2018 International Conference on Signal Processing and Communications (SPCOM), 2018Co-Authors: I. Zakir Ahmed, Hamid R. Sadjadpour, Shahram YousefiAbstract:In this paper, we propose a Singular-Value-Decomposition-based variable-resolution Analog to Digital Converter (ADC) bit Allocation design for a single-user Millimeter wave massive Multiple-Input Multiple-Output receiver. We derive the optimality condition for bit Allocation under a power constraint. This condition ensures optimal receiver performance in the Mean Squared Error (MSE) sense. We derive the MSE expression and show that it approaches the Cramer-Rao Lower Bound (CRLB). The CRLB is seen to be a function of the analog combiner, the digital combiner, and the bit Allocation Matrix. We attempt to minimize the CRLB with respect to the bit Allocation Matrix by making suitable assumptions regarding the structure of the combiners. In doing so, the bit Allocation design reduces to a set of simple inequalities consisting of ADC bits, channel singular values and covariance of the quantization noise along each RF path. This results in a simple and computationally efficient bit Allocation algorithm. Using simulations, we show that the MSE performance of our proposed bit Allocation is very close to that of the Full Search (FS) bit Allocation. We also show that the computational complexity of our proposed method has an order of magnitude improvement compared to FS and Genetic Algorithm based bit Allocation of [1].
