高速資料傳輸不僅被noise限制傳輸速率，尤其甚者，由於通道記憶特性所產生之ISI更是造成高速資料傳輸系統效能不彰的主要因素。除了傳統上使用Equalizer來抑制ISI外， 多載波(Multiple carrier)技術或正交分頻多工系統(Orthogonal Frequency Division Multiplexing, OFDM)技術被廣泛的應用在寬頻無線通訊系統，其抗多路徑衰減等優良特性，為DAB、DVB與無線區域網路(802.11a/g) 等標準所採用。此外近年來，為提昇system capacity與頻譜使用效率，Multiple input multiple output (MIMO) 系統效能的研究亦成為學術界之顯學；不僅學界如此，工業界也投注大量資源研發，對此技術多有著墨。總括來說，結合多載波(Multiple carrier)技術與Multiple input multiple output (MIMO) 技術的MIMO-OFDM技術已被公認為下一代寬頻無線高速資料傳輸系統重要的candidates之一，此外MIMO-OFDM技術因具有高頻譜效率、抗多路徑衰減等特性，目前在工業界為寬頻無線固接/行動系統(Metro. Broadband Wireless Access Network、802.16d/e)及無線區域網路(802.11n、HiperLAN/2)等標準所採用。本論文針對多載波(Multiple carrier)技術與Multiple input multiple output (MIMO) 技術，分別應用於MC-CDMA接收機設計、效能分析及低複雜度channel feedback coding scheme。
此篇論文分析在MC-CDMA的上傳(Uplink, UL)系統上設計最佳(optimal)多用戶偵測接收機 (Multi-user Detector, MUD)並利用Belief propagation (BP)技術來做效能分析，此外為使channel model更實用，我們亦考量channel mismatch效應。利用Belief propagation技術我們設計出一iterative optimal MUD，不僅效能分析上與replica method的理論結果完全吻合，其實現複雜度遠低於傳統的optimal MUD (複雜度與使用者個數成指數成長) ，只與使用者個數平方成正比，也就是說，此BP-based iterative optimal MUD的實現複雜度約與Linear MUD相當。此篇論文亦設計一極有效率的Channel State Information (CSI) feedback的機制應用於 MIMO-OFDM系統上；我們提出利用QRD的分解方式來分解MIMO channel matrix以取代SVD，此種方法在非對稱的multiple antenna array configuration下的效能幾乎與SVD-based vector coding scheme相當，但複雜度卻遠低於SVD。此外我們亦提出一interpolation scheme，不但讓回傳的CSI大幅下降，也能繼續維持precoder的正交性。
最後本論文針對 OFDM 在天線陣列上達成平行頻道問題提出全新的低複雜度多用戶空間-頻率編碼排程技術 (Multi-user Space-Frequency Coding Scheme、MU-SFCS scheduling)，我們考慮在無線通道下採用空間-時間編碼技術並應用於多用戶MIMO (Multi-input Multi-output)系統以獲得multiuser diversity。可事先identify通道結構，再透過使用傳送端Precoder與極有效率的排程演算法設計出之少量空間軸傅氏基底向量來實現低複雜度的 SDMA (Spatial Division Multiple Access)，而獲致極大的系統容量。我們利用在空間通道上採用傅利葉轉換於MIMO通道以獲得angle-domain資訊，使得每位使用者所看到的空間通道結構可被identify出來，因而定義出每位使用者所需要的空間平行頻道，而payload data便可透過這些各別使用者所喜愛的空間平行頻道來傳送與接收進而提升系統容量。簡言之，我們使用的策略包括1) 在傳送端設計Precoder，利用空間軸傅氏基底向量當作spatial codeword。2) 利用training period在不同時間傳送不同的空間軸傅氏基底向量，以達成Switched beamforming 效果。3)接收端利用training period時所送來的spatial codeword來追蹤空間－時間無線頻道結構，以找出最佳的angle-frequency組合。4)在傳送端採用極有效率的排程演算法。

High data rate communications are limited not only by noise but also by inter-symbol interference (ISI) due to the memory of the dispersive wireless communication channel. Except the conventional channel equalization techniques are used to suppress the ISI caused by channel, an multi-carrier approach, e.g., orthogonal frequency division multiplexing (OFDM), towards transmitting data over a multipath channel also allow us to design a system supporting high data rate. Combining OFDM transmissions with code division multiple access (CDMA) exploit
the wideband channel’s inherent frequency diversity by spreading each symbol across multiple sub-carriers. The combination has two major advantages. One is its own capability to lower the symbol rate in each subcarrier enough to have a quasi-synchronous signal reception in uplink.
The other is that it can effectively combine the energy of the received signal scattered in the
frequency domain. That is, it is possible to prevent the obliteration of certain sub-carriers by
deep frequency domain fades. This is achieved by spreading each sub-carrier’s signal with the
aid of a spreading code and thereby increasing the achievable error-resilience. In this thesis, we
analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with
channel mismatch in multi-carrier code-division-multiple-access (MC-CDMA) systems. However,
it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance
and to make the optimum MUD feasible, based on Pearl’s belief propagation (BP) scheme, we
put together a low-complexity iterative MUD algorithm for MC-CDMA systems.
On the other hand, systems that employ multiple antennas in both the transmitter and the
receiver of a wireless system have been shown to promise extraordinary spectral efficiency. One
way to realize the enormous throughput is to exploit the spatial multiplexing (SM) gain by transmitting
several data streams across the wireless MIMO channel simultaneously. Unfortunately,
an SM system is sensitive to the rank of its MIMO channel matrix. To prevent ill-conditioned
MIMO channel matrix from affecting the system data throughput, e.g., in the downlink scenarios
where the number of the transmit antennas is larger than the number of the receive antennas
and the corresponding MIMO channel matrix has a non-empty null space, a transmit spatial
pre-filtering scheme should be designed to feed the simultaneously-transmitted data streams into
the signal space of the MIMO channel matrix, instead of wasting the transmit power in the null
space of the MIMO channel matrix. However, the efficiency of the spatial pre-filtering scheme
highly depends on the availability of the MIMO channel state information (CSI), which can be
estimated at the receiver. Therefore, feedback of sufficiently reliable CSI from the receiver to
the transmitter is crucial, especially in the downlink scenarios. Hence, in this thesis, we also
focus on the development of efficient coding schemes for channel feedback in downlink scenario,
which expects a higher data throughput and is considered the bottleneck in a MIMO system.
The thesis contains Four results. First, we analyze the bit-error-rate (BER) performance
of the optimum multiuser detection (MUD) with channel mismatch in MC-CDMA systems.
To justify the BER performance and to make the optimum MUD feasible, based on Pearl’s
belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for
MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD
algorithm to make the scenario general. With channel mismatch, the analytical results of the
BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD
algorithm conform very closely to the BER performance of the optimum MUD derived using
the replica method, which is a non-trivial extension of the existing replica approach mentioned
above. Without channel mismatch, the problem becomes a special case of our contribution.
Second, Raleigh and Cioffi proposed a singular-value-decomposition based space-time architecture
for multiple-input-multiple-output (MIMO) wireless systems using antenna arrays, discrete
matrix multi-tone (DMMT) coding scheme, which claims to achieve near-optimum performance
in both signal diversity and channel capacity. However, the DMMT coding scheme suffers high
computational complexity in non-stationary channel environments, where channel information
update is frequently needed at both the transmitter and the receiver. In addition, the transmitter
may need to rely on a wideband feedback channel to obtain the entire set of vector
channel information, which is obviously impractical. By exploring the MIMO channel structures,
we develop an adaptive version of the DMMT coding scheme for a high-capacity MIMO
system with time-varying frequency-selective channels. In the proposed coding scheme, a lowv
complexity Jacobi-SVD is utilized to iteratively optimize the transmit signaling by tracking
merely the dominant fading paths in the MIMO wireless channel, while only very little feedback
information is required. An analytic capacity lower bound considering channel-tracking errors
is derived for systems employing the proposed coding scheme. Simulation results reconfirm that
the proposed coding scheme works efficiently in indoor wireless applications.
Third, for MIMO-OFDM wireless systems, gain in channel throughput educed through sufficient
feedback of the CSI is significant, particularly when the number of transmit antennas is
larger than the number of receive antennas. In this part, we demonstrate that, in such scenarios,
1) the CSI of each OFDM sub-carrier can be parameterized into a short bit stream by a proposed
low-complexity QR decomposition on the corresponding MIMO channel matrix, 2) the overall
CSI can be reliably represented by a proposed parameter interpolation on the above bit streams
of only a fraction of sub-carriers, and 3) a MIMO-OFDM system with a low-rate CSI feedback
parameterized above can provide a channel throughput comparable to the channel capacity.
Finally, we propose a novel scheduling mechanism to enhance the throughput in a multiuser
MIMO system. As it is known, based on the information theory, that a wireless system
with antenna array at both sides of a communication link is able to achieve excellent spectral
efficiency. For multiuser multiple-input-multiple-output(MU-MIMO) services, orthogonal
multiple accesses, e.g., frequency division multiple access (FDMA) and time division multiple
access (TDMA) are popular options to avoid multiuser interference. However, for the FDMA
(or TDMA) system, the spatial resources of a frequency band (or time slot) are consumed by
a single user. The spatial utility of such a system is very low if it happens to have a common
clustered channel structure. A high sum-rate is achievable in an MU-MIMO system where a
common frequency or time resource is shared by multiple users if transmitters assume perfect
knowledge of the corresponding channels. However, in order to reach this capacity, existing
coding schemes suffer not only from high computational complexity but also from the need for
excess channel state information (CSI) feedback. It is shown in the previous work that the
ergodic capacity of a system employing the simple multiuser angle-frequency coding scheme is
close to that of a system employing dirty paper coding but significantly better than that of an
orthogonal multiplexing system. In this part of thesis, we focus on the scheduling mechanism for
angle-frequency subchannels. With the channel identification, we propose two efficient scheduling
strategies to make MU transmit simultaneously without serious packet collisions. With the
proposed approaches, the packets transmitted to different subscriber units can be scheduled
efficiently at the access point to increase the channel utilization and decrease the average packet
delay.

[1] L. Hanzo, L. L. Yang, E. L. Kuan, and K. Yen, Single and Multi-Carrier DS-CDMA, Multi-
User Detection, Space-Time Spreading, Synchronisation, Networking and Standards, IEEE
Press–John Wiley, 2003.
[2] S. Verd´u, Multiuser Detection, Cambridge University Press, New York, 1998.
[3] T. Tanaka, “A ststistical mechanics approach to large-system analysis of CDMA multiuser
detectors,” IEEE Trans. Inform., vol. 48, pp. 2888–2910, Nov. 2002.
[4] S. F. Edwards and P. W. Anderson, “Theory of spin glasses,” J. Phys. F: Metal Phys., vol.
5, pp. 965–974, May 1975.
[5] H. Nishimori, Statistical Physics of Spin Glasses and Information Processing: An Introduction.
ser. Number 111 in International Series on Monographs on Physics. Oxford U.K.:
Oxford Univ. Press, 2001.
[6] D. Guo and S. Verd´u, “Multiuser detection and statistical mechanics,” in Communiacation,
Information and Network Security. Boston MA: Kluwer, 2002.
[7] D. Guo and S. Verd´u, “Randomly spread CDMA: Asymptotics via statistical physics,”
IEEE Trans. Inform. Theory, vol. 51, pp. 1982–2010, June 2005.
[8] R. M¨uller, “Channel capacity and minimum probability of error in large dual antenna array
systems with binary modulation,” IEEE Trans. Signal Process., vol. 51, pp. 2821–2828, Nov.
2003.
[9] R. R. M¨uller and W. Gerstacker, “On the capacity loss due to separation of detection and
decoding,” IEEE Trans. Inform. Theory, vol. 50, pp. 1769–1778, Aug. 2004.
[10] C. K. Wen, P. Ting, and J. T. Chen “Asymptotic analysis of MIMO wireless systems with
spatial correlation at the receiver,” to appear in IEEE. Trans. Commun., 2005.
[11] R. M¨uller and A. Tulino, “Minimum bit error probability of large randomly spread MCCDMA
systems in multipath Rayleigh fading,” 2004 IEEE Eighth International Symposium
108
109
on Spread Spectrum Techniques and Applications (ISSSTA), Sydney, Australia, Aug. 2004,
pp. 560–564.
[12] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
San Francisco, CA: Morgan Kaufuman, 1988.
[13] Y. Kabashima, “A CDMA multiuser detection algorithm on the basis of belief propagation,”
J. Phys. A: Math. Gen. pp. 11111–11121, Oct. 2003.
[14] R. M¨uller, “Random matrices, free probability, and the replica method,” European Signal
Processing Conference (Eusipco), Vienna, Austria, Sep. 2004.
[15] H. Li and H. V. Poor, “Impact of channel estimation error on multiuser detection via
the replica method,” 2004 IEEE Global Telecommunications Conference (GLOBECOM04),
Texas, USA, Dec. 2004, pp. 3636–3640.
[16] T. Tanaka, “Performance analysis of optimum multiuser detector under phase mismatch,”
in Proceedings 2004 IEEE International Symposium on Information Theory, Chicago, USA,
Jun. 2004, p. 317.
[17] H. V. Poor, An Introduction to Signal Detection and Estimation. Springer-Verlag, New
York, 1994.
[18] V. M. DaSilva and E. S. Sousa, “Multicarrier orthogonal CDMA signals for quasisynchronous
communication systems,” IEEE J. Select. Areas Commun., vol. 12, pp. 842–852,
June 1994.
[19] G. B. Giannakis, Z. Wang, A. Scaglione, and S. Barbarossa, “AMOUR-generalized multicarrier
transceivers for blind CDMA regardless of multipath,” IEEE Trans. Commun., vol.
48, pp. 2064–2076, Dec. 2000.
[20] P. M. Crespo, M. L. Honig, and J. A. Salehi, “Spread-time code-division multiple access,”
IEEE Trans. Commun., vol. 43, pp. 2139–2148, June 1995.
[21] M. J. M. Peacock, I. B. Collings, and M. L. Honig, “Asymptotic analysis of LMMSE multiuser
receivers for multi-signature multicarrier CDMA in Rayleigh fading,” IEEE Trans.
Commun., vol. 52, pp. 964–972, June 2004.
110
[22] M. J. M. Peacock, I. B. Collings, and M. L. Honig, “Asymptotic spectral efficiency of multiuser
multi-signature CDMA in frequency-selective channels,” to appear in IEEE Trans.
Inform. Theory, 2005.
[23] C. K. Wen, “Design and analysis of high-capacity multiple-input multiple-out wireless systems,”
PhD Thesis, 2004.
[24] L. Hanzo, M. M¨unster, B. J. Choi, and T. Keller, OFDM and MC-CDMA for Broadband
Multi-User Communications, WLANs and Broadcasting, IEEE Press–John Wiley, 2003.
[25] J. G. Andrews and T. H. Meng, “Performance of multicarrier CDMA with successive interference
cancellation in a multipath fading channel,” IEEE Trans. Commun., vol. 52, pp.
811–822, May 2004.
[26] A. J. Goldsmith, L. J. Greenstein, and G. J. Foschini, “Error statistics of real-time power
measurements in cellular channels with multipath and shadowing,” IEEE Trans. Veh. Technol.
vol. 43, pp. 439–446, Aug. 1994.
[27] M. Morelli, L. Sanguinetti, and U. Mengali, “Channel estimation and tracking for MCCDMA
signals,” Eur. Trans. Telecommuns., pp. 249–258, May–June 2004.
[28] M. M´ezard, G. Parisi, R. Zecchina, “Analytic and algorithmic solution of random satisfiability
problems,” Science, vol. 297 pp. 812–815, Aug. 2002.
[29] P. Billingsley, Probability and Measure, 3rd edition, Wiley, 1995.
[30] J. E. Marsden, and M. J. Hoffman, Elementary Classical Analysis, 2nd edition, W. H.
Freeman, 1993.
[31] J. G¨artner, “On large deviations from the invariant measure,” Theory Probab. Appl. 22,
pp. 24–39, 1977.
[32] R. S. Ellis, “Large deviations for a general class of random vectors,” Ann. Probab. 12, pp.
1–12, 1984.
[33] G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment
when using multiple antennas,” Kluwer Academic Publishers-Wireless Personal
Communication, pp. 311-335, Jan. 1998.
111
[34] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-time codes for high data rate wireless
communication: performance criterion and code construction,” IEEE Trans. Information
Theory, vol. 44, pp. 744-765, Mar. 1998.
[35] G. J. Foschini, “Layered space-time architecture for wireless communication in a fading
environment when using multi-element antennas,” Bell Labs Technical Journal, pp. 41-59,
Autumn 1996.
[36] A. Lozano, and C. Papadias, “Layered space-time receivers for frequency-selective wireless
channels,” IEEE Trans. Commun., vol. 50, pp. 65-73, Jan. 2002.
[37] Y. Liu, M. P. Fitz, and O. Y. Takeshita, “Space-time codes performance criterion and
design for frequency selective fading channel,” Proc. IEEE ICC, pp. 2800-2804, Jun. 2001.
[38] G. G. Raleigh and J. M. Cioffi, “Spatio-temporal coding for wireless communication,” IEEE
Trans. Communication, vol. 46, pp. 357-366, Mar. 1998.
[39] C. K. Wen, Y. C. Wang, and J. T. Chen, “An adaptive spatio-temporal coding scheme
for Indoor-wireless communication,” IEEE Journal on Selected Areas in Communication
Special Issue on Wireless LANs and Home Networks, vol. 21, pp. 161-170, Feb. 2003.
[40] J. R. Bunch, C. P. Nielsen, and D. C. Sorensen, “Rank-one modification of the symmetric
eigenproblem,” Numerical Math., vol. 31, pp. 31-48, 1978.
[41] E. G. Kogbetliantz, “Solution of linear equations by diagonalization of coefficient matrix,”
Quart. Appl. Math. , vol. 13, pp. 123-132, 1955
[42] John Terry and Juha Heiskala, OFDM Wireless LANs: A Theoretical and Practical Guide.
Sams Publishing. Dec. 2001.
[43] Q. H. Spencer, B. D. Jeffs, M. A. Jensen, and A. L. Swindlehurst, “Modeling the statistical
time and angle of arrival characteristic of an indoor multipath channel,” IEEE Journal on
Selected Areas in Communication, vol. 18, pp. 347-360, Mar. 2000.
[44] M. Steinbauer, A. F. Molisch, and E. Bonek, “The double-directional radio channel,” IEEE
Antennas and Propagation Magazine, vol. 43, pp. 51–63, Aug. 2001.
[45] H. Xu, V. Kukshya, and T. S. Rappaport, “Spatial and temporal characteristics of 60-GHz
indoor channels,” IEEE Journal on Selected Areas in Communication, vol. 20, pp. 620-630,
Apr. 2002.
112
[46] D. P. McNamara, M. A. Beach, P. N. Fletcher, and P. Karlsson, “Temporal variation of
multiple-input multiple-output (MIMO) channels in indoor environments,” 11th International
Conference on Antennas and Propagation, pp. 17-20, Apr. 2001.
[47] J. T. Chen and Y. C. Wang, “Performance analysis of the parametric channel estimation
for MLSE equalization in multipath channels with AWGN,” IEEE Trans. Communication,
vol. 49, pp. 393-396, Mar. 2001.
[48] Y. Y. Wang and J. T. Chen, “TST-MUSIC for DOA-delay joint estimation,” IEEE Trans.
Signal Process, Jul. 2000.
[49] M. C. Vanderveen, C. B. Papadias and A. Paulraj, “Joint angle and delay estimation
(JADE) for multipath signals arriving at an antenna array,” IEEE Communicatons Letters,
Jan. 1997.
[50] G. H. Golub, “Some modified eigenvalue problems,” SIAM, Rev., 15, pp. 318-334, 1973.
[51] P. Comon and G. H. Golub, “Taking a few extreme singular values and vectors in signal
processing,” Proc. IEEE, vol. 78, pp. 1327-1343, Aug. 1990.
[52] J. R. Bunch and C. P. Nielsen, “Updating the singular value decomposition,” Numerical
Math., vol. 31, pp. 111-129, 1978.
[53] M. Moonen, P. V. Dooren and J. Vandewalle, “A singular value decomposition updating
algorithm for subspace tracking,” SIAM Journal Matrix Anal., Appl. vol. 13. No. 4, pp.
1015-1038, Oct. 1992
[54] G. H. Golub and C. F. Van Loan, Matrix Computation. third edition, The Johns Hopkins
University Press, 1996.
[55] 3GPP/TS 25.214, Physical Layer Procedure. Ver. 3.10, 2002
[56] F. T. Luk, “A triangular processor array for computing singular values,” Linear Algebra
Appl., Appl. vol. 77, pp. 259-273, 1986
[57] S. Starr, J. M. Cioffi, and P. Silverman, Understanding digital subscriber line technology.
Prentice Hall, 1999.
[58] M. M´edard, “The effect upon channel capacity in wireless communications of perfect and
imperfect knowledge of the channel,” IEEE Trans. Information Theory, vol. 46, pp. 933-946,
May 2000.
113
[59] T. M. Cover and J. A. Tomas, Elements of Information Theory. New York: Wiley, 1991.
[60] G. L. St¨uber, Principle of Mobile Communication. Kluwer Academic Publishers, 1996.
[61] ˙I. E. Telatar, “Capacity of multi-antenna Gaussian channel,” Euro. Trans. Telecom., vol.
10, pp. 585–595, 1999.
[62] G. J. Foschini, and M. J. Gans, “On limits of wireless communications in a fading environment
when using multiple antennas,” Kluwer Academic Publishers-Wireless Personal
Communication, pp. 311–335, Jun. 1998.
[63] K. K. Mukkavilli, A. Sabharwal, E. Erkip, and B. Aazhang, “On beamforming with finite
rate feedback in multiple-antenna systems,” IEEE Trans. Info. Theory, vol. 49, pp. 2562–
2579, Oct. 2003.
[64] D. J. Love, R. W. Heath, Jr., and T. Strohmer, “Grassmannian beamforming for multipleinput
multiple-output wireless systems,” IEEE Trans. Info. Theory, vol. 49, pp. 2735–2747,
Oct. 2003.
[65] S. ten Brink, G. Kramer, and A. Ashikhmin, “Design of low-density parity-check codes for
modulation and detection,” IEEE Trans. Comm., vol. 52, pp. 670–678, Apr. 2004.
[66] M. K. Varanasi and T. Guess, “Optimum decision feedback multiuser equalization and
successive decoding achieves the total capacity of the Gaussian multiple-access channel,”
in Proc. Asilomar Conf. Signals, Systems and Computers, 1997.
[67] D. Tse and S. Hanly “Linear multiuser receivers: effective interference, effective bandwidth
and user capacity,” IEEE Trans. Inform. Theory, vol. 45, pp. 641–657, Mar. 1999.
[68] A. Lozano, “Capacity-approaching rate function for layered multiantenna architectures,”
IEEE Trans. Wireless Comm., vol. 2, pp. 616–620, May 2003.
[69] J. Wang and B. Daneshrad, “Performance of linear interpolation-based MIMO detection for
MIMO-OFDM systems,” 2004 IEEE Wireless Communications and Networking Conference
(WCNC 2004), Atlanta, GA, pp. 981–986, Mar. 2004.
[70] V. Lau, Y. Liu, and T. A. Chen, “On the design of MIMO block-fading channels with
feedback-link capacity constraint,” IEEE Trans. Commun., vol. 52, pp. 62–70, Jan. 2004.
114
[71] J. C. Roh and B. D. Rao, “An efficient feedback method for MIMO systems with slowly
time-varying channels,” 2004 IEEE Wireless Communications and Networking Conference
(WCNC 2004), Atlanta, GA, pp. 760–764, Mar. 2004.
[72] D. Samardzija, N. Mandayam, “Unquantized and uncoded channel state information feedback
on wireless channels,” 2005 IEEE Wireless Communications and Networking Conference
(WCNC 2005), New Orleans, LA, pp. 1059–1065, Mar. 2005.
[73] H. B¨olckei, D. Gesbert, and A. J. Paulraj, “On the capacity of OFDM-based spatial multiplexing
systems,” IEEE Trans. Commun., vol. 50, pp. 225–234, Feb. 2002.
[74] J. Choi, B. Mondal, and R. W. Heath, Jr., “Interpolation based unitary precoding for
spatial multiplexing MIMO-OFDM with limited feedback,” to apper in IEEE Trans. on
Signal Processing, 2006.
[75] M. K. Varanasi and T. Guess, “Optimum decision feedback multiuser equalization and
successive decoding achieves the total capacity of the Gaussian multiple-access channel,”
in Proc. Asilomar Conf. Signals, Systems and Computers, 1997.
[76] M. Ikram, E. Onggosanusi, et. al., “Close loop MIMO pre-coding using Givens rotations,”
Contribution IEEE 802.16e, Jan. 2005.
[77] A. Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications,
Cambridge University Press, 2003.
[78] J. M. Cioffi, Lecture Note of Digital Communication, Stanford University.
[79] V. Erceg, K. V. Hari, M. S. Smith, D. S. Baum et. al., “Channel model for fixed wireless
applications,” Contribution IEEE 802.16a, Jun. 2003.
[80] G. L. St¨uber, Principle of Mobile Communication. Kluwer Academic Publishers, 1996.
[81] J. Proakis, Digital Communications, McGraw-Hill, 2000.
[82] W. Rhee and J. M. Cioffi, “On the capacity of multiuser wireless channels with multiple
antennas,” IEEE Trans. Infom. Theory, vol. 49, pp. 2580-2595, Oct. 2003.
[83] W. Yu and J. M. Cioffi, “Trellis Precoding for the Broadcast Channel,” Globecom, 2001.
[84] G. Caire and S. Shamai, “On the achievable throughput of a multi-antenna Gaussian broadcast
channel,” IEEE Trans. Infom. Theory, vol. 49, pp. 1691-1706, July 2003.
115
[85] S. Vishwanath, N. Jindal and A. Goldsmith, “Duality, achievable rates, and sum-rate capacity
of Gaussian MIMO broadcast channels ,” IEEE Trans. Infom. Theory, vol. 49, pp.
2658-2668, Oct. 2003.
[86] C. K. Wen and J. T. Chen “A low complexity space-time OFDM multi-user system,”
submitted to IEEE. Trans. Wireless Commun., 2003.
[87] L. M. Correia “Wireless flexible personalised communication: COST 259,European cooperation
in mobile radio research” John Wiley and Sons, 2001.
[88] G. Wang and N. Ansari, “Optimal broadcast scheduling in packet radio networks using
mean field annealing,” IEEE Journal on Selected Arreas in Communications, vol. 15, pp.
250-259, Feb. 1997.
[89] D. Bertsekas and R. Gallager, Data Networks. Englewood Cliffs, NJ: Prentice-Hall, 1987.