MIMO-capacities for COST 259 scenarios

Stege, M.; Bronzel, M.; Fettweis, F.

doi:10.1109/izsbc.2002.991772

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…For the antenna sectoring, we assume three sectors per cell, each using idealized antennas with 120 beamwidth and a 20 dB front-to-back ratio. (Note that the use of sectoring antennas may compromise the full-scattering assumption of MIMO channels [24], the corresponding data rate difference however is not to be considered in this work.) The performance is shown by the average transmission power per cell versus the requested data rate; for simplicity, we assume that the data rate requests of different users are the same, and having in mind that the proposed DCM schemes can naturally take advantage of the differentiated data rates among users.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Finally, since for any R j > 0, it results in Ã > 0; the equality in Equation (16) holds from Equation (24). & Channel Allocation Parameters k,s Are Redundant in Distributed DCM Problem Equation (6) Without loss of generality, we focus on the onedimensional DCM problem in Equation (11).…”

Section: Appendixmentioning

confidence: 99%

“…where , k , k and k are non-negative Lagrangian multipliers. The full set KKT conditions consist of those in Equations(21)(22)(23)(24)(25), with I Ã…”

mentioning

confidence: 99%

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Dynamic channel management in MIMO OFDM cellular systems

Wang

Gitlin

et al. 2004

Wireless Communications

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We consider dynamic channel management (DCM), more specifically power control and channel allocation, in multi-carrier multi-antenna wireless cellular systems. A quasi-static channel model is assumed such that the channels remain approximately time-invariant within each packet. On a packet-by-packet basis, the mobile station (MS) receivers feed back the DCM parameters to the corresponding base station (BS) transmitters in order to minimize the transmission power and strive to satisfy every individual user's data-rate request. While optimum, centralized DCM for all active cells typically requires solution of a non-convex optimization problem (which is subject to local minima) and requires intense channel feedback to a central controller. We instead consider suboptimal distributed DCM, where each individual cell locally optimizes its own performance based on the local channel information. In particular, we compare three distributed DCM schemes, namely the two-dimensional DCM, which jointly adjusts both spatial and frequency sub-channels of each BS-MS link, the one-dimensional DCM, which adjusts only the frequency sub-channels and the scalar DCM, which adjusts each BS-MS link by one scalar power coefficient. These DCM schemes are investigated in multi-cell environments with realistic parameters; and it is seen that due to the multiple antennas, the maximum supportable rate (i.e. the rate supported by practically finite transmission power) is significantly increased, for example by a factor of two for users using four antennas and close to cell boundary.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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Dynamic channel management in MIMO OFDM cellular systems

Wang

Gitlin

et al. 2004

Wireless Communications

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“…This kind of channel matrix corresponds to a Rayleigh fading channel where the scattered signal parts arrive from a great number of different directions at the receiver so that a negligible amount of correlation between the various signal components can be assumed. To vindicate this assumption an antenna-spacing of at least about half the wavelength must be chosen and to get small antenna arrangements the spacing is commonly minimized [3,4,5]. However in many practical cases (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…As for a small antenna-spacing all the signal components arrive roughly with identical amplitude and phase-angle at each receiver array element the rank of the channel transfer matrix degenerates approximately to rank-one and the capacity not longer grows linearly but only logarithmically with the number of antenna elements. Due to this a strong LOS-component is often treated harmful to the channel capacity [4,5].…”

Section: Introductionmentioning

confidence: 99%

On the boundaries of the MIMO-channel-capacity with a focus on line-of-sight-connections

2004

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In this paper we show that geometric optimization is the most efficient way to reach maximum channel capacity when the channel is characterized by a powerful Line-Of-Sight signal component but it will result in larger antenna spacings than they are found customary. To have a quantitative criterion for comparison we firstly derive boundaries for the channel capacity when considering a pure Line-Of-Sight signal component. For finding the upper and lower bound we take into account signal pre-processing strategies in conjunction with systematic power allocation schemes. Having detected these bounds we calculate the capacity in the case of a linear antenna array spaced half-wavelength at both the transmitter as well as the receiver as it is commonly chosen in mobile communications. The capacity turns out adopting values close to the lower bound even if it is optimized best by means of signal processing and power allocation. On the contrary we present an examplary geometrically optimized set of antenna elements, which is dedicated to reach the upper bound of the channel capacity.

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Optimal IDM-MISO Transmit Strategy with Partial CSI at Transmitter

Wang

et al. 2006

2006 40th Annual Conference on Information Sciences and Systems

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MIMO-capacities for COST 259 scenarios

Cited by 10 publications

References 15 publications

Dynamic channel management in MIMO OFDM cellular systems

Dynamic channel management in MIMO OFDM cellular systems

On the boundaries of the MIMO-channel-capacity with a focus on line-of-sight-connections

Optimal IDM-MISO Transmit Strategy with Partial CSI at Transmitter

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