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Table 1: Comparison Report Fig 4: RTL view of proposed method

Table 1 Comparison Report Fig 4: RTL view of proposed method

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The ML detection method used to minimizes the average error probability. The ML detector calculates the Euclidean distances (EDs) between the received signal vector and lattice points Hx, and returns the vector x with the smallest distance, i.e. it minimizes, QR Decomposition is required by most MIMO detection schemes to decompose the channel matrix H into a unitary matrix Q and an upper triangular matrix R [9]. The objective of this paper is to develop a hybrid QR Decomposition with pipelined design that allows low-complexity decomposition of large complex matrices, by reducing the number of computations required and by increasing their execution parallelism, to resolve the throughput.
The ML detection method used to minimizes the average error probability. The ML detector calculates the Euclidean distances (EDs) between the received signal vector and lattice points Hx, and returns the vector x with the smallest distance, i.e. it minimizes, QR Decomposition is required by most MIMO detection schemes to decompose the channel matrix H into a unitary matrix Q and an upper triangular matrix R [9]. The objective of this paper is to develop a hybrid QR Decomposition with pipelined design that allows low-complexity decomposition of large complex matrices, by reducing the number of computations required and by increasing their execution parallelism, to resolve the throughput.
So the ML detector detects Xx is to difficult the nultiplication of channel matrix H and x is formulated as [5].
So the ML detector detects Xx is to difficult the nultiplication of channel matrix H and x is formulated as [5].
Householder transformations also provide the capability of annihilating multiple elements simultaneously by reflecting a multi-dimensional input vector onto a plane. However, a straightforward VLSI implementation of the Householder algorithm requires square-root operations, division and multiplication and hence it leads to very high hardware complexity. To resolve this issue, presents novel Householder algorithms that use sequences of simple Householder reflections, which can be easily implemented using simple arithmetic operations.
Householder transformations also provide the capability of annihilating multiple elements simultaneously by reflecting a multi-dimensional input vector onto a plane. However, a straightforward VLSI implementation of the Householder algorithm requires square-root operations, division and multiplication and hence it leads to very high hardware complexity. To resolve this issue, presents novel Householder algorithms that use sequences of simple Householder reflections, which can be easily implemented using simple arithmetic operations.
Fig 2: Architecture diagram for hybrid QR decomposition with pipelined design
Fig 2: Architecture diagram for hybrid QR decomposition with pipelined design
XILINX software tool is used for hybrid QR decomposition to split the complex matrix into unitary matrix and upper triangular matrix. We are expecting further reduction in power comparing to the existing technique. Fig 3 Fig 3: simulation result for proposed system To demonstrate the performance of the proposed system, a Xilinx Virtex-6 FPGA was used to process the QR decomposition with a block size of 4 x 4. This design is coded in VH DL and synthesized on a Xilinx Virtex-6 device (XC6VLX550T) using the Xilinx’s ISE software. Fig 4 shows an RT ‘floorp L, view of the proposed method. According to the an area’ synthesis result, our proposed method fig 5 shows implementation can occupied lesser area than existing method . Tablel shows the comparison report for proposed method.
XILINX software tool is used for hybrid QR decomposition to split the complex matrix into unitary matrix and upper triangular matrix. We are expecting further reduction in power comparing to the existing technique. Fig 3 Fig 3: simulation result for proposed system To demonstrate the performance of the proposed system, a Xilinx Virtex-6 FPGA was used to process the QR decomposition with a block size of 4 x 4. This design is coded in VH DL and synthesized on a Xilinx Virtex-6 device (XC6VLX550T) using the Xilinx’s ISE software. Fig 4 shows an RT ‘floorp L, view of the proposed method. According to the an area’ synthesis result, our proposed method fig 5 shows implementation can occupied lesser area than existing method . Tablel shows the comparison report for proposed method.
Fig 5: Floorplan design view for proposed method I would like to thank my guide Mr. D .sellathambi M.E., Assistant Professor in the Electronics and Communication Engineering Department at the Parisutham Institute of Technology and Science, Thanjavur. For his help and guidance to enable us to propose this paper.
Fig 5: Floorplan design view for proposed method I would like to thank my guide Mr. D .sellathambi M.E., Assistant Professor in the Electronics and Communication Engineering Department at the Parisutham Institute of Technology and Science, Thanjavur. For his help and guidance to enable us to propose this paper.
Related topics:
Computer ScienceField Programmable Gate ArrayQR Decomposition
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