Ĭ���aSu, Hsuan-Jung�O�W�j��:�q�H�u�{�Ǭ�s��L h @Lin, Shih-ChunShih-ChunLin2010-07-012018-07-052010-07-012018-07-052007U0001-2712200711062800http://ntur.lib.ntu.edu.tw//handle/246246/188176Recently, multi-terminal network equipped with multiple antennas has gained lots of attentions due to its strong potential in performance enhancement. The coding for vectoride-information channels plays a fundamental role in this system, since each terminal in the network can provide the others some side-information to help encoding/decoding theata. In this thesis, we will study how to design and implement this new coding scheme.e have developed a lattice-based vector dirty paper coding (DPC) for communication channels with transmitter side-information and perfect transmitter channel state information (CSIT). This coding can completely remove the inference known only at the transmitter (as side-information) but not at the receiver. When only statistics of CSIT is available, good (sometimes optimal) performance can still be obtained by modifying this coding structure.e also show how to achieve the capacity region of the multiple-input multiple-output Gaussian broadcast channel with proposed coding. Motivated by this lattice-based codingcheme, we also develop another vector DPC based on superposition coding which can be implemented with existing channel and source coding schemes. An design example is provided and close-to-capacity rate performance is obtained. Finally, we propose a vector Wyner-Ziv coding (WZC) which is a compression technique using decoder side information to help reconstruction. The key is exploring the duality between the DPC and WZC problems. This vector WZC can be applied to the vector CEO problem in sensor networks, and achieve the Berger-Tung sum rate which is the best known compression rate for this problem.1. Introduction . . . . . . . . . . . . . . . . . . . . . 1.1 Motivation: Practical Coding for Vector Side-information Channels . . . ................................2.2 Overview of Thesis . . . . . . . . . . . . ........... 4.3 Notations . . . . . . . . . . . . . . . . . . . . . . .5. Lattice-based VDPC with Perfect CSIT . . . . . . . . . 7.1 System Model and Preliminaries . . . . . . . . . . . . 8.1.1 Vector Dirty-paper Channel . . . . . . . . . . . . . 8.1.2 Perfect CSIT: Costa��s Coding and Interference-free Rate Achievement. . . . . . ..............................10.1.3 Review of lattices and lattice quantization noise . 12.2 Lattice Coding for Vector Dirty-paper Channels with Perfect CSIT . . . . .....................................13.2.1 Vector Precoding with Lattice Quantizer . . . . . . 14.2.2 Capacity of the Equivalent Modulo Channel in Lemma 1 ........................................................16.2.3 Nested Lattice Based Vector Dirty Paper Coding . . .21.3 Applications in the MIMO GBC . . . . . . . . . . . . .25.4 Summary . . . . . . . . . . . . . . . . . . . . . . . 28. Lattice-based Coding with Statistics of CSIT . . . . . 29.1 Statistics of CSIT: Linear-assignment Gel��fand-Pinsker Coding and Its Achievable Rate . . .......................31.1.1 Achievable Rate of the Linear-assignment Gel��fand-Pinsker Coding. . . . . . . . . . . . . . . . . . ........33.2 Lattice Coding for the Fading Vector Dirty-paper Channels with Statistics of CSIT . . . . . . . . . . . . .36.3 Numerical example . . . . . . . . . . . . . . . ......40.4 Summary . . . . . . . . . . . . . . . . . . . . . . . 42. Practical VDPC Based on Superposition Coding . . . . . 43.1 System Model . . . . . . . . . . . . . . . . . . . .. 44.2 Vector Superposition Dirty Paper Coding . . . . . . . 45.3 Code Parameters Selection and the Interference-free Rate Achievement ........................................ 50.3.1 Feasibility of Successive Decoding . . . . . . . . .53.3.2 Extension to more than Two Users . . . . . . . . . .55.4 Summary . . . . . . . . . . . . . . . . . . . . . . . 56. VDPC Implementation Using IRA and TCQ . . . . . . . . .58.1 End-to-end Implementation and Practical Design Issues . . . . . . . . . .................................58.1.1 Performance Loss due to the Practical Quantization Code . . . . .............................................60.1.2 Maximum Achievable Channel Code Rate using Finite Alphabet Constellation . . . . . . .......................62.1.3 End-to-End Vector Dirty Paper Coding Structure . . .65.2 A Design Example and Detailed Code Parameters Selection . . . . . . . ..................................66.2.1 Vector Quantizer Parameters Selection and Performance . . . . . ....................................67.2.2 Channel Coding Parameters Selection and Performance . . . . ..................................... 70.3 Summary . . . . . . . . . . . . . . . . . . . . . . . 73. Duality : Vector WZC . . . . . . . . . . . . . . . . . 74.1 System Model . . . . . . . . . . . . . . . . . . . . 75.2 Nested-Lattice Based Vector Wyner-Ziv Coding . . . . .79.3 Conclusion . . . . . . . . . . . . . . . . . . . . . .84. Conclusion . . . . . . . . . . . . . . . . . . . . . . 85ppendices:. Proof of Theorem 2 . . . . . . . . . . . . . . . . . . 87. Proof of Theorem 5 and Its Corollary . . . . . . . . . 94.1 Proof of the Encoder Part . . . . . . . . . . . . . . 95.2 Proof of the Decoder Part . . . . . . . . . . . . . . 97.3 Proof of Corollary 1 . . . . . . . . . . . . . . . . .99. Receiver Decoding Algorithms of Chapter 5 . . . . . . 101.1 Joint Trellis Processing . . . . . . . . . . . . . . 101. Proof of Theorem 6 . . . . . . . . . . . . . . . . ...104.1 Reconstruction with Zero Receiver Side-information . 104.2 Reconstruction with Receiver Side-information . . . .107ibliography . . . . . . . . . . . . . . . . . . . . . . 110683016 bytesapplication/pdfen-US�s�X�B�~��Tcodingside informationthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/188176/1/ntu-96-F89921044-1.pdf