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Mimo ofdm with sparse matrix detection thesis reporting


mimo ofdm with sparse matrix detection thesis reporting

from the FFT1 processor and let denote the vector of received symbols from the FFT2 processor. And utilizing the proposed low complexity detection scheme we can obtain this improvement in the performance with a low computational cost. The main application of mimo-ofdm with espar antenna receiver is to improve the bit error rate performance and diversity gain without increasing the number of RF front-end circuits. Also, for comparison, the number of flops required by the ML detector 14 is where is the constellation size. Due to the large size of the channel matrix, calculating the pseudoinverse demands a very high computational effort and for this reason the detection process is the main limitation of this scheme. The proposed detection scheme is specifically designed to reduce the computational cost of the detection of mimo-ofdm with espar antenna receiver but it can be also applied in the detection of similar systems that have a large size channel matrix. The transmitter is based on the wlan standard ieee 802.11n.

mimo ofdm with sparse matrix detection thesis reporting

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Computational Cost The computational cost impact of vietnam war essay is analysed in terms of the number of complex floating point operations (flops) required. For calculating the total computational cost required by the receiver, based on 16 the number of flops required by the two FFT blocks considering the data symbol and pilot symbol is where is the FFT size. The computational cost analysis and simulation results show that on average the proposed scheme can further reduce the computational cost and achieve a complexity comparable to the conventional mimo-ofdm detection schemes. Denote the elements of the matrix and, are column vectors of the matrix. Simulation Results To determine the bit error rate performance of the proposed algorithm, a software simulation model of mimo-ofdm with espar antenna receiver was developed in c using the it 17 communications library. Detection For the detection process the ZF V-blast 6 algorithm is used. In the simulation the proposed low complexity submatrix divided mmse sparse-sqrd detection is implemented with quarter-size, eighth-size and eighteenth-size, submatrices. Table 5: Simulation settings. However, this figure shows that the proposed scheme cannot overcome the BER performance of mimo 2 4 vblast without espar antenna but gives a considerable improvement compared to the BER of mimo 2 2 vblast without espar antenna receiver. The computational cost reduction is achieved by exploiting the sparse structure of the channel matrix. Figure 8: Proposed scheme with 16-QAM and 4-symbol overlapping.

mimo ofdm with sparse matrix detection thesis reporting


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