Performance of the distributed antenna systems in a multi-cell environment

Roh, Wonil; Paulraj, A.

doi:10.1109/vetecs.2003.1207610

Cited by 23 publications

(12 citation statements)

References 4 publications

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“…R,x,l(n-i)=O, V1n-i>A (1 1) We let the diagonal channel matrix on the Ith path of the receive antenna be Ym,A (n) IE T MT L = sE E E bk (i) hk,l (i)t i=o k= 11 = (n+ I±1)Ts,+ T / g(t -iT-Tk,l) g ( (n±+1)Ts +T.,,A + / n (t) g* (t -nTTs +T1 ±T,x It is assumed that Rm,k,A,l (n1 i) r (n±+ ± Ts + T, A X~~g (t -iTs nTs +tm TA,> I (n±+ ± T, +TX,A n1m,A (n) = X n2+Ts,X" (12) (t -nT,-Tm,A) dt -Tm,A) dt s -Tk,)g (t -nT, Tm,x) dt (6) n1(t) g* (t -nT5 -Tm,A) dt (7)…”

Section: Receiver Modelmentioning

confidence: 99%

“…Up to now, research efforts of DAS have mainly focused on spectral efficiency, power allocation schemes [4], channel capacity [5] [6], and so on. However, in the downlink of DAS, the channel propagation delays from each transmit antenna to receive antennas may not be identical due to the unequal distances between distributed transmit antennas and co-located receive antennas.…”

Section: Introductionmentioning

confidence: 99%

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Sphere Decoding for Distributed Antenna Systems

Chen

Tang

2006

2006 6th International Conference on ITS Telecommunications

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Distributed antenna system (DAS) is one of the promising techniques for next generation wireless communication. In this paper, we firstly present a relatively efficient sphere decoding algorithm (SDA) that performs exact maximumlikelihood (ML) decoding for distributed antenna systems, which does not necessarily require that the number of receive antennas should be no less than that of transmit antennas. Some simulation results in frequency selective fading channel show that bit error rate (BER) performance is influenced by the difference of channel propagation time delays from neighboring transmit antennas to the receive antenna. The increase of difference can result in BER performance improvement.

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Section: Receiver Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sphere Decoding for Distributed Antenna Systems

Chen

Tang

2006

2006 6th International Conference on ITS Telecommunications

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“…With single-user spatial multiplexing, a single user is served on a given time-frequency resource employing all available antenna elements of the DAS. Single-user MIMO (SU-MIMO) has been shown to yield higher sum rates in DASs that collocate about as many antennas per RRU as are available at the receiver side [7]. When multi-user MIMO (MU-MIMO) is employed, on the other hand, multiple users are served in parallel over the distributed antenna array, by separating http://asp.eurasipjournals.com/content/2013/1/54 their signals in the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Single-user MIMO versus multi-user MIMO in distributed antenna systems with limited feedback

Schwarz¹,

Heath

Rupp³

2013

EURASIP J. Adv. Signal Process.

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This article investigates the performance of cellular networks employing distributed antennas in addition to the central antennas of the base station. Distributed antennas are likely to be implemented using remote radio units, which is enabled by a low latency and high bandwidth dedicated link to the base station. This facilitates coherent transmission from potentially all available antennas at the same time. Such distributed antenna system (DAS) is an effective way to deal with path loss and large-scale fading in cellular systems. DAS can apply precoding across multiple transmission points to implement single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) transmission. The throughput performance of various SU-MIMO and MU-MIMO transmission strategies is investigated in this article, employing a Long-Term evolution (LTE) standard compliant simulation framework. The previously theoretically established cell-capacity improvement of MU-MIMO in comparison to SU-MIMO in DASs is confirmed under the practical constraints imposed by the LTE standard, even under the assumption of imperfect channel state information (CSI) at the base station. Because practical systems will use quantized feedback, the performance of different CSI feedback algorithms for DASs is investigated. It is shown that significant gains in the CSI quantization accuracy and in the throughput of especially MU-MIMO systems can be achieved with relatively simple quantization codebook constructions that exploit the available temporal correlation and channel gain differences.

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“…But up to now, research efforts of DAS have mainly focused on spectral efficiency, power allocation schemes [2], channel capacity [3] and system performance [4]. Unfortunately, no method to estimate the channel impulse response is given, which is one of the most difficult tasks in the DAS.…”

Section: Introductionmentioning

confidence: 99%

A Subspace Blind Channel Estimation Method for Distributed MISO Systems

Chen

Tang

2006

2006 International Conference on Communications, Circuits and Systems

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We propose a subspace-based blind channel estimation method for distributed multi-input single-output systems, where one side of the link constitutes a largely spaced antenna array. The proposed approach does not necessarily require that the number of receive antennas should be no less than that of transmit antennas and reduces the computational complexity. Sufficient identifiability conditions are established for the distributed channel up to an ambiguity matrix. Simulation results demonstrate the channel estimation performance is influenced by the difference of channel delays for neighboring transmit antennas to the receive antenna, and the increase of difference can result in estimation performance improvement.

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Performance of the distributed antenna systems in a multi-cell environment

Cited by 23 publications

References 4 publications

Sphere Decoding for Distributed Antenna Systems

Sphere Decoding for Distributed Antenna Systems

Single-user MIMO versus multi-user MIMO in distributed antenna systems with limited feedback

A Subspace Blind Channel Estimation Method for Distributed MISO Systems

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