Performance Analysis of Macrodiversity MIMO Systems with MMSE and ZF Receivers in Flat Rayleigh Fading

Smith, Peter J.; Martin, Philippa A.

doi:10.1109/twc.2013.032113.120798

Cited by 39 publications

(52 citation statements)

References 30 publications

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“…Third, our proposed approximations are seen to remain extremely tight for the entire SNR range for all cases. The analytical expressions are also seen to remain tight for the special case presented in (19), where each terminal undergoes Rayleigh fading with unequal correlation matrices. Furthermore, the expected SINR in each case is seen to saturate with growing SNR, due to the inability of the MRC to mitigate inter-terminal interference.…”

Section: (36)mentioning

confidence: 84%

Uplink analysis of large MU-MIMO systems with space-constrained arrays in Ricean fading

Tataria

Smith

Matthaiou

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-Closed-form approximations to the expected perterminal signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency of a large multiuser multipleinput multiple-output system are presented. Our analysis assumes correlated Ricean fading with maximum ratio combining on the uplink, where the base station (BS) is equipped with a uniform linear array (ULA) with physical size restrictions. Unlike previous studies, our model caters for the presence of unequal correlation matrices and unequal Rice factors for each terminal. As the number of BS antennas grows without bound, with a finite number of terminals, we derive the limiting expected perterminal SINR and ergodic sum spectral efficiency of the system. Our findings suggest that with restrictions on the size of the ULA, the expected SINR saturates with increasing operating signal-to-noise-ratio (SNR) and BS antennas. Whilst unequal correlation matrices result in higher performance, the presence of strong line-of-sight (LoS) has an opposite effect. Our analysis accommodates changes in system dimensions, SNR, LoS levels, spatial correlation levels and variations in fixed physical spacings of the BS array.

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Section: (36)mentioning

confidence: 84%

Uplink analysis of large MU-MIMO systems with space-constrained arrays in Ricean fading

Tataria

Smith

Matthaiou

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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“…From (32), it follows that the following condition must be satisfied: 1 − c S − c(B + 1) > 0 or, equivalently,…”

Section: B Zero Forcingmentioning

confidence: 99%

Interference Management in 5G Reverse TDD HetNets With Wireless Backhaul: A Large System Analysis

Sanguinetti

Moustakas

Debbah

2015

IEEE J. Select. Areas Commun.

122

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This work analyzes a heterogeneous network (Het-Net), which comprises a macro base station (BS) equipped with a large number of antennas and an overlaid dense tier of small cell access points (SCAs) using a wireless backhaul for data traffic. The static and low mobility user equipment terminals (UEs) are associated with the SCAs while those with mediumto-high mobility are served by the macro BS. A reverse time division duplexing (TDD) protocol is used by the two tiers, which allows the BS to locally estimate both the intra-tier and inter-tier channels. This knowledge is then used at the BS either in the uplink (UL) or in the downlink (DL) to simultaneously serve the macro UEs (MUEs) and to provide the wireless backhaul to SCAs. A concatenated linear precoding technique employing either zeroforcing (ZF) or regularized ZF is used at the BS to simultaneously serve MUEs and SCAs in DL while nulling interference toward those SCAs in UL. We evaluate and characterize the performance of the system through the power consumption of UL and DL transmissions under the assumption that target rates must be satisfied and imperfect channel state information is available for MUEs. The analysis is conducted in the asymptotic regime where the number of BS antennas and the network size (MUEs and SCAs) grow large with fixed ratios. Results from large system analysis are used to provide concise formulae for the asymptotic UL and DL transmit powers and precoding vectors under the above assumptions. Numerical results are used to validate the analysis in different settings and to make comparisons with alternative network architectures.

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“…The constant g is defined to capture the average link power drop between transmit antennas and receive antennas due to shadowing and path loss. It is assumed here that both transmit and receive antennas are co-located so that the average link SNR, g of each constituent link is the same in contrast to macrodiversity MIMO [4]. The average transmit power of the data signal x x x is set to E s , and n n n denotes the C n R ×1 AWGN vector.…”

Section: System Modelmentioning

confidence: 99%

“…Multi element antennas are used in many scenarios such as point-to-point links [1], [2], multiuser links [3], and macrodiversity links [4]. The capability of MIMO for achieving higher throughput is due to the fact that multiple independent spatial data streams can be transmitted in the same time and frequency resource.…”

Section: Introductionmentioning

confidence: 99%

Spatial modulation for massive MIMO

Haas

2015

2015 IEEE International Conference on Communications (ICC)

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Multiple-input multiple-output (MIMO) systems can increase wireless link capacity without requiring additional bandwidth and power. On the one hand, MIMO systems heavily depend on channel state information (CSI) at the receiver. It has also been shown that CSI at the transmitter (CSIT) can improve the link capacity and reliability considerably but with the disadvantage of extra feedback and computational costs. On the other hand, energy efficiency of MIMO systems could adversely increase with the number of antennas in the link. Therefore, there are considerable interests in energy efficient practical transmission schemes for MIMO links without CSIT. In this study, an emerging wireless communication concept which is termed as spatial modulation (SM) is considered for large scale MIMO. The results show that in information-theoretic viewpoint, SM achieves capacity comparable to the open-loop MIMO capacity even though a subset of transmit antennas is activated in every channel use. The reason is both the channel coefficients and input symbols carry information in SM. As a result, SM regains (compensates) the loss of information capacity due to activating a subset of antennas by modulating information in the antenna index. This means that the sum information rate remains high.

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Performance Analysis of Macrodiversity MIMO Systems with MMSE and ZF Receivers in Flat Rayleigh Fading

Cited by 39 publications

References 30 publications

Uplink analysis of large MU-MIMO systems with space-constrained arrays in Ricean fading

Uplink analysis of large MU-MIMO systems with space-constrained arrays in Ricean fading

Interference Management in 5G Reverse TDD HetNets With Wireless Backhaul: A Large System Analysis

Spatial modulation for massive MIMO

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