Modern financial systems theory and applications edwin h neave
MIMO Systems Theory and Applications Part 1 ppt
... 1) ⎛ ⎞ ( 1) ⎞ y1 b1 h 11 h12 ⎜ ( 1) ⎟ ⎜ ( 1) ⎟ ⎜ y ⎟ ⎜ h 21 h22 ⎟ x1 ⎜ b ⎟ ⎜ ⎟=⎜ ∗ ⎟ + ⎜ (2 ) ⎟ ∗ ⎠· x ⎜ ( 2) ∗ ⎟ ⎝ h − h ⎝ (b )∗ ⎠ 12 11 ⎝ ( y1 ) ⎠ ∗ ∗ h22 − h 21 ( 2) (b2(2) )∗ ( y2 ) ∗ (10 ) 10 ... 10 MIMO Systems, Theory and Applications Time t =1 t =E x1 Tx1 ∗ − x2 x2 ∗ x1 Space Tx2 c Fig Alamouti code ⎛ Let us denote : h 11 ⎜ h 21 Hequ = ⎜ ∗ ⎝h 12 ∗ h22 ⎞ h12 h22 ⎟ ⎟ ∗ − h 11 ⎠ ∗ − h 21 The ... and simulation of MIMO- OFDM was introduced in (Yu et al., 2004) 12 MIMO Systems, Theory and Applications 10 OFDM Theory BER NoOFDM 1 10 −2 BER 10 −3 10 −4 10 −5 10 −6 10 10 SNR(dB) Fig BER...
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MIMO Systems Theory and Applications Part 2 ppt
... 35 AS (2 Among 4) AS (3 Among 4) No AS 30 Ergodic capacity 25 20 15 10 5 10 SNR(dB) 15 Fig 25 Antenna selection in MIMO (4 × 2) : Impact on ergodic capacity 20 36 MIMO Systems, Theory and Applications ... length), then becomes the 2l order Hadamard matrix C C C −C and C2l = C2 ⊗ C2l−1 (116) where ⊗ is the Kronecker product and C2l−1 , ≤ l is expressed as : C2l = C2l−1 C2l−1 C2l−1 − C2l−1 (117) Collaborative ... 34 MIMO Systems, Theory and Applications −1 10 M=4 M=3 2 BER 10 −3 10 −4 10 SNR(dB) Fig 23 Multi user BF (K = 3, M = 3/M = 4) Processing techniques for MIMO systems: Antenna selection MIMO...
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MIMO Systems Theory and Applications Part 3 potx
... example Fig 3- D lattice space example MIMO Systems, Theory and Applications Geometrical Detection Algorithm for MIMO Systems 65 b 3- D lattice space Here, we continue to consider the case of 3- dimensional ... points are examined 66 MIMO Systems, Theory and Applications (a) (b) Fig Comparison of SER performance of ESA, ML decoding, ZF and MMSE using 4-QAM (a) × MIMO systems (b) × MIMO systems Geometrical ... fading MIMO system in three cases, MIMO 2×2 (with and without Alamouti coding) and MIMO 4×4 are presented For this type of channel estimation, and 32 training bits are used for MIMO 2×2 and MIMO...
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MIMO Systems Theory and Applications Part 4 pot
... for (a) 2×2 -MIMO and (b) 4 4 -MIMO systems SV1 SV2 SV3 SV4 Space Div (LOS) 4 4 -MIMO 1.0000 0.0067 0.0008 0.0000 Angle Div (LOS) 4 4 -MIMO 1.0000 0.1120 0.0011 0.0005 Space Div (NLOS) 4 4 -MIMO 1.0000 ... height Relative height of Tx and Rx Distance between Tx and Rx Room’s dimension Scatterers’ radius Scatterers’ number 10 .4 (1.3m) 14. 4λ (1.8m) 4 (0.5m) 32. 24 (4. 3m) 5 4 3(m3) 0.1m 30 Table A typical ... -48 .44 2 dBm (1 .43 2×10-8 W) 0.073 -115.719 Ray Tracing -48 .46 1 dBm (1 .42 5×10-8 W) 0.073 -121.210 Table Numerical results for a SISO system configuration shown in Fig Channel capacity for the MIMO...
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MIMO Systems Theory and Applications Part 5 docx
... D (pr ) ΔD% 0.0 15 0.130 0.0 458 0.1372 199.41 TS-MD-OPT 0.084 0.212 0.08 25 0.1 058 28.24 REP 0.026 0.108 0.1069 0. 155 1 45. 01 SM 0.013 0. 157 0.0338 0. 158 9 369.76 ¯ Table pd , pr and respective distortions ... Letters 7(10): 481–483 154 MIMO Systems, Theory and Applications Telatar, E (1999) Capacity of multi-antenna Gaussian channels, Europ Trans on Telecomm 10(6): 58 5 59 5 Tse, D & Viswanath, P (2006) ... communication systems, IEEE Transactions on Communications 42(234): 1740–1 751 132 MIMO Systems, Theory and Applications Xin, Y., Wang, Z and Giannakis, G B (2003) Space-time diversity systems based...
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MIMO Systems Theory and Applications Part 6 doc
... respectively As shown in the figure, our 166 MIMO Systems, Theory and Applications 45 Theory results, tI=4 40 Theory results, t =5 I Theory results, t =6 35 I Theory results, t =7 Ergodic Capacity ... 1 .6% 1 .6% −α σ = 30 dB 3.0% Pt ⋅ r0−α σ = 10 dB 13 .6% Pt ⋅ r Pt ⋅ r Pt ⋅ r Pt ⋅ r 2 2 Pt ⋅ r0−α σ = 30 dB 2.9% −α σ = 10 dB 62 .9% 63 .1% 63 .4% 63 .5% 63 .6% 63 .7% 63 .7% −α σ = 30 dB 47.3% 47 .6% ... matrices and performance analysis of MIMO systems with co-channel interference IEEE Trans Wireless Commun., Vol.3, No.2, Feb -2004, pp.418-431, ISSN 15 36- 12 76 1 86 MIMO Systems, Theory and Applications...
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MIMO Systems Theory and Applications Part 7 docx
... Adachi and M Nakagawa, “On cellular MIMO spectrum efficiency,” IEEE Vehicular Technology Conference, pp 4 17- 421, October 20 07 202 MIMO Systems, Theory and Applications [12] Y J Choi, C S Kim and ... with less increase in complexity 206 MIMO Systems, Theory and Applications Notation: Random variables, vectors, and matrices are denoted by lower, lower bold, and upper bold italic letters, respectively ... users The entries of H and ΔH have ˆ been assumed to be zero mean i.i.d complex Gaussian random variables, i.e., H ~ CN (0,1) 218 MIMO Systems, Theory and Applications Fig Upper and lower bound of...
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MIMO Systems Theory and Applications Part 8 potx
... Rates and Power Loading in MIMO- THP Systems ", 3rd International Conference on Information and Communication Technologies (ICTTA),Page(s): - 7, 20 08 236 MIMO Systems, Theory and Applications [ 18] ... scheduling 2 38 MIMO Systems, Theory and Applications subchannel and all the M users be served simultaneously Based on the criterion of maximum system sum-capacity, two per-layer joint transmit and receive ... (Miao et al., 2009) 2 58 MIMO Systems, Theory and Applications 10 Average Uncoded BER 10-1 10-2 10-3 10-4 10-5 10-6 SW-THP UW-THP TxWF-THP 10-7 10 -8 10 12 SNR (dB) 14 16 18 Fig Performance comparison...
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MIMO Systems Theory and Applications Part 9 pot
... Channel capacity performance (Nt×2) MIMO Systems, Theory and Applications Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems 275 Fig 10 Channel capacity ... Error Rate Performance (K=8, N=3) MIMO Systems, Theory and Applications Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems 283 Conclusion We proposed ... Optimal MIMO- OFDM Loading with Power-Constrained Antennas, Proceedings of the IEEE PIMRC'06, Sept 2006 284 MIMO Systems, Theory and Applications [11] S S Rao Engineering Optimization, Theory and...
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MIMO Systems Theory and Applications Part 1 pot
... 1) ⎛ ⎞ ( 1) ⎞ y1 b1 h 11 h12 ⎜ ( 1) ⎟ ⎜ ( 1) ⎟ ⎜ y ⎟ ⎜ h 21 h22 ⎟ x1 ⎜ b ⎟ ⎜ ⎟=⎜ ∗ ⎟ + ⎜ (2 ) ⎟ ∗ ⎠· x ⎜ ( 2) ∗ ⎟ ⎝ h − h ⎝ (b )∗ ⎠ 12 11 ⎝ ( y1 ) ⎠ ∗ ∗ h22 − h 21 ( 2) (b2(2) )∗ ( y2 ) ∗ (10 ) 10 ... 10 MIMO Systems, Theory and Applications Time t =1 t =E x1 Tx1 ∗ − x2 x2 ∗ x1 Space Tx2 c Fig Alamouti code ⎛ Let us denote : h 11 ⎜ h 21 Hequ = ⎜ ∗ ⎝h 12 ∗ h22 ⎞ h12 h22 ⎟ ⎟ ∗ − h 11 ⎠ ∗ − h 21 The ... and simulation of MIMO- OFDM was introduced in (Yu et al., 2004) 12 MIMO Systems, Theory and Applications 10 OFDM Theory BER NoOFDM 1 10 −2 BER 10 −3 10 −4 10 −5 10 −6 10 10 SNR(dB) Fig BER...
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MIMO Systems Theory and Applications Part 4 doc
... for (a) 2×2 -MIMO and (b) 4 4 -MIMO systems SV1 SV2 SV3 SV4 Space Div (LOS) 4 4 -MIMO 1.0000 0.0067 0.0008 0.0000 Angle Div (LOS) 4 4 -MIMO 1.0000 0.1120 0.0011 0.0005 Space Div (NLOS) 4 4 -MIMO 1.0000 ... TBCE and SBCE-ML schemes in different SNRs for a MIMO 2×2 82 MIMO Systems, Theory and Applications As shown in Fig 12, the performance of both LS-based TBCE and SBCE-ML schemes in a MIMO 4 4 system ... time of LS-based TBCE and SBCE-ML schemes in a MIMO 2×2 Fig 15 The burst of LS-based TBCE A) MIMO 2×2, B) MIMO 4 4 Fig 16 The burst of LS-based SBCE-ML A) MIMO 2×2, B) MIMO 4 4 method needs to transmit...
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MIMO Systems Theory and Applications Part 8 ppt
... 200 MIMO Systems, Theory and Applications 20 FRF Hybrid FRF 18 P r −α/σ2=10dB Cellular MIMO Ergodic Capacity 16 t0 14 12 10 2 Number of antennas 8 (a) 20 FRF Hybrid FRF 18 P r −α/σ2=30dB ... users The entries of H and ΔH have ˆ been assumed to be zero mean i.i.d complex Gaussian random variables, i.e., H ~ CN (0,1) 2 18 MIMO Systems, Theory and Applications Fig Upper and lower bound of ... scheme for cellular OFDM systems, ” IEEE Electronics Letters, vol 38, pp 387 - 388 , April 2002 [16] Wei Peng and Fumiyuki Adachi, “Hybrid Frequency Reuse Scheme for Cellular MIMO Systems, ” IEICE Transactions...
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MIMO Systems Theory and Applications Part 10 ppt
... capacity performance (Nt×2) MIMO Systems, Theory and Applications Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems 275 Fig 10 Channel capacity performance ... Error Rate Performance (K=8, N=3) MIMO Systems, Theory and Applications Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems 283 Conclusion We proposed ... of antennas 280 MIMO Systems, Theory and Applications Figures 17 and 18 show BER performance as a function of SNRmax, where the number of users is set to 1∼3 for K=4 in Fig.17, and set to for...
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MIMO Systems Theory and Applications Part 11 docx
... beamforming MIMO systems, ” IEEE Trans Signal Process., vol 54, no 12, pp 4753-4766, Dec 2006 314 MIMO Systems, Theory and Applications K Huang, B Mondal, R W Heath, Jr., and J G Andrews, “Effect ... joint distribution of H(iK + k) and H(iK − p), p = 0, · · · , P Since this joint distribution is related to k and independent of i, so is 308 MIMO Systems, Theory and Applications Pe (iK + k) That ... Zhou and H Dai, “Asymptotic analysis in MIMO MRT/MRC systems, ” EURASIP J Wireless Commun Net., 2006 M Kang, and M.-S Alouini, “A comparative study on the performance of MIMO MRC systems with and...
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Báo cáo hóa học: " Editorial Millimeter-Wave Wireless Communication Systems: Theory and Applications" potx
... targeted for low-cost and lowpower 60 GHz communications systems and are evaluated and compared by considering the effects of front end nonideality The authors found that OQPSK with FDE and nonfractional ... heterodyne architecture can enable the compact and low-cost millimeter-wave receivers for the future wireless communications systems such as the IEEE 802.15.3c wireless personal area networks (WPAN) ... 2 EURASIP Journal on Wireless Communications and Networking system is used to compare the BER performance of both measured and modeled channels The authors conclude that...
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