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Asymptotic Performance of MMSE MIMO Systems Using Adaptive Modulation
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Communication Systems, CoS.
KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Communication Systems, CoS.
(English)In: IEEE Communications Letters, ISSN 1089-7798Article in journal (Other academic) Submitted
Abstract [en]

This letter investigates the asymptotic performance of minimum-mean-square error (MMSE) in MIMO systemsusing adaptive modulation. The asymptotic average spectral efficiency and bit-error rate (BER) are derived basedon three approximations of the probability density function (p.d.f.) of effective SNR. Simulations suggest that themost accurate estimation of average spectral efficiency is obtained by using Gamma distribution.

National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-9797OAI: oai:DiVA.org:kth-9797DiVA: diva2:133049
Note
QS 20120327Available from: 2009-01-07 Created: 2009-01-07 Last updated: 2012-03-27Bibliographically approved
In thesis
1. Adaptive MIMO Systems with Channel State Information at Transmitter
Open this publication in new window or tab >>Adaptive MIMO Systems with Channel State Information at Transmitter
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

 This dissertation presents adaptation techniques that can achieve high spectral efficiency for single user multiple-input multiple-output (MIMO) systems. Two types of adaptation techniques, adaptive modulation and adaptive powe allocation, are employed to adapt the rate and the transmit power to fading channels. We start by investigating the adaptive modulation subject to a certain bit-error-ratio (BER) constraint, either instantaneous BER constraint or average BER constraint. The resulting average spectral efficiencies are obtained in closed-form expressions. It turns out that, by employing the average BER constraint, we can achieve the optimal average spectra efficiency at the cost of prohibitive computational complexity. On the other hand, instantaneous BER constraint leads to inferior performance with little computational complexity. In order to achieve comparable performance to the average BER constraint with limited complexity, a non-linear optimization method is proposed. To further enhance the average spectra efficiency, adaptive power allocation schemes are considered to adjust the transmit power across the temporal domain or the spatial domain, depending on the specific situation. Provided the closed-form expressions of the average spectral efficiency, the optimal MIMO coding scheme that offers the highest average spectral efficiency under the same circumstances can be identified. As we take into account the effect of imperfect channel estimation, the adaptation techniques are revised to tolerate interference introduced by the channel estimation errors. As a result, the degradation with respect to the average spectral efficiency is in proportion to signal-to-noise ratio (SNR). In order to facilitate fast development and verification of the adaptation schemes proposed for various MIMO systems, a reconfigurable Link Layer Simulator (LiLaS) which accommodates a variety of wireless/wireline applications is designed in the environment of MATLAB/OCTAVE. The idea of the simulator is originated from Software Defined Radio (SDR) and evolved to suit Cognitive Radio (CR) applications. For the convenience of modification and reconfiguration, LiLaS is functionally divided into generic blocks and all blocks are parameterized.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. xvii, 79 p.
Series
Trita-ICT-COS, ISSN 1653-6347 ; 0804
Keyword
adaptive modulation, adaptive power allocation, average spectral efficiency, MIMO, BER.
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-9777 (URN)978-91-7415-188-6 (ISBN)
Public defence
2009-01-30, Sal E, Forum IT, Kista, 14:00 (English)
Opponent
Supervisors
Note
QC 20100812Available from: 2009-01-07 Created: 2008-12-13 Last updated: 2010-08-12Bibliographically approved

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