High diversity with simple space time block-codes and linear receivers

Rupp, Markus; Mecklenbräuker, Christoph F.; Gritsch, G.

doi:10.1109/glocom.2003.1258250

Cited by 51 publications

(28 citation statements)

References 3 publications

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“…represents the kth diagonal element of the inverse of M, andĤ is obtained by removing the kth row of H. The associated SINR is γ k = E x /Rñ(k, k), which can be shown to be a chi-square random variable with 2(N − M + 1) degrees of freedom [4], [5]. The CDF of Y ∼ χ 2(N −M +1) , with variance 0.5 for the participating Gaussian random variables, is:…”

Section: Linear Receiversmentioning

confidence: 99%

Outage and Diversity of Linear Receivers in Flat-Fading MIMO Channels

Hedayat¹,

Nosratinia

2007

IEEE Trans. Signal Process.

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Abstract-This correspondence studies linear receivers for MIMO channels under frequency-nonselective (flat) quasi-static Rayleigh fading. The outage probability and diversity gain of MMSE and Zero Forcing (ZF) receivers are investigated. It is found that contrary to intuition, MMSE and zero-forcing receivers may not perform similarly at high SNR. Assuming M transmit and N receive antennas, the zero-forcing receiver always has diversity N −M +1, unlike the MMSE receiver, whose behavior can vary. Under separate spatial encoding, where data from each transmit antenna is separately encoded, MMSE is no better than ZF in terms of diversity. But for joint spatial encoding systems, where an encoded stream is sent from all the antennas, the MMSE receiver achieves diversity M N at low spectral efficiencies but has diversity only M − N + 1 at high spectral efficiencies.

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Section: Linear Receiversmentioning

confidence: 99%

Outage and Diversity of Linear Receivers in Flat-Fading MIMO Channels

Hedayat¹,

Nosratinia

2007

IEEE Trans. Signal Process.

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“…It is known that for i.i.d. Rayleigh channels, the diversity of the ZF receiver is L (ZF) = M − K + 1 [15], [16]. The WLZF approach described in this letter achieves a diversity of (see Appendix for the derivation)…”

Section: A Widely Linear Zero-forcing (Wlzf)mentioning

confidence: 99%

“…When H is complex, Λ contains M −K+1 diagonal elements equal to one and the rest are zero; hence,γ (ZF) k /γ follows a chi-squared distribution with 2M − 2K +2 degrees of freedom (DoF) [16]. It can be observed that because H is complex, h k corresponds to 2 column vectors of H R in (2), as the second half of its column vectors are formed by the elements of the first half of the column vectors.…”

Section: Appendix -Diversity Of Wlzfmentioning

confidence: 99%

Using Real Constellations in Fully- and Over-Loaded Large MU-MIMO Systems With Simple Detection

Ducoing

et al. 2016

IEEE Wireless Commun. Lett.

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Abstract-The aim of this letter is to exhibit some advantages of using real constellations in large multi-user (MU) MIMO systems. It is shown that a widely linear zero-forcing (WLZF) receiver with M-ASK modulation enjoys a spatial-domain diversity gain, which linearly increases with the MIMO size even in fully-and over-loaded systems. Using the decision of WLZF as the initial state, the likelihood ascent search (LAS) achieves nearoptimal BER performance in fully-loaded large MIMO systems. Interestingly, for coded systems, WLZF shows a much closer BER to that of WLZF-LAS with a gap of only 0.9-2 dB in SNR.Index Terms-Multiple-input multiple-output (MIMO), overloaded, M-ASK, widely linear receiver.

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“…Evidently, as shown in (20), the dimension of such a user pair subset is N q (τ ). Consequently, the search complexity can be significantly reduced if the number of UEs to be possibly paired is reduced, e.g., set the SNR threshold before pairing.…”

Section: A Maximize Capacity Pairing and Scheduling (Mcps) Schemementioning

confidence: 99%

“…According to (5) and (8) for SNR estimation, the addition and multiplication operations can be omitted in comparison with the operations for the multiplication and the inverse of the channel matrix in (8), which depends on the matrix dimension, i.e., O (K 2 N R ) and O (min (K, N R ) 3 ) respectively. For the exhaustive search shown in (20), the number of comparisons is determined by the number of users with a non-empty queue, i.e., N q . SNR estimation has to be carried out for each comparison.…”

Section: Computational Complexity and Overheadmentioning

confidence: 99%

Performance analysis of cooperative virtual multiple‐input–multiple‐output in small‐cell networks

et al. 2013

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With the advent of small cell networks (SCNs) to support growing wireless data volumes and thus reduced cell sizes, cooperative communications are significantly facilitated. Applicable to 3GPP LTE-A, we propose a novel channel/queue-aware user pairing and scheduling scheme in a cooperative virtual multiple-input multiple-output (VMIMO) system. The queueing performance of the VMIMO system with the scheduling scheme is analyzed based on the finite-state Markov model (FSMM), and compared with that of non-cooperative systems. Bounds on the average queuing delay of users are derived by using a semi-definite programming (SDP) approach. The presented analyses are validated through comparing the analytical and simulation results. It is found that the introduced VMIMO pairing process is able to significantly reduce service delays, bringing on a positive impact of cooperative techniques on next generation wireless systems.

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High diversity with simple space time block-codes and linear receivers

Cited by 51 publications

References 3 publications

Outage and Diversity of Linear Receivers in Flat-Fading MIMO Channels

Outage and Diversity of Linear Receivers in Flat-Fading MIMO Channels

Using Real Constellations in Fully- and Over-Loaded Large MU-MIMO Systems With Simple Detection

Performance analysis of cooperative virtual multiple‐input–multiple‐output in small‐cell networks

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