MU-MIMO in LTE Systems

Duplicy, Jonathan; Badic, Biljana; Balraj, Rajarajan; Ghaffar, Rizwan; Horváth, Péter; Kaltenberger, Florian; Knopp, Raymond; Kovács, István Z.; Nguyen, Hung Tuan; Tandur, Deepaknath; Vivier, Guillaume

doi:10.1155/2011/496763

Cited by 72 publications

(27 citation statements)

References 20 publications

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“…We emphasise that SM requires only a single radio frequency (RF) chain at the transmit side since only one is active at any particular instance. Requiring only a single RF chain at the transmitter means that multi-user SM is not comparable in terms of cost to the more complicated spatial-multiplexing multi-user systems analysed in [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Multiple access spatial modulation

Serafimovski

Sinanović

Renzo

et al. 2012

J Wireless Com Network

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In this study, we seek to characterise the behaviour of Spatial modulation (SM) in the multiple access scenario. By only activating a single transmit antenna for any transmission, SM entirely avoids inter-channel interference, requires no synchronisation between the transmit antennas and a single radio frequency chain at the transmitter. Most contributions thus far have only addressed aspects of SM for a point-to-point communication system. We propose a maximum-likelihood (ML) detector which can successfully decode incoming data from multiple simultaneous transmissions and does not suffer from the near-far problem. We analyse the performance of the interference-unaware and interference-aware detectors. We look at the behaviour of SM as the signal-to-interference-plus-noise ratio goes to infinity and compare it to the complexity and cost equivalent single-input-multiple-output (SIMO) system. Two systems are considered to be equivalent in terms of complexity if their respective detection algorithms are of the same order in O(·) notation. Simulation results show that the interference-aware SM detector performs better than the complexity equivalent multi-user ML-SIMO detector by at least 3 dB at an average bit-error-ratio of 10 −3 .

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Section: Introductionmentioning

confidence: 99%

Multiple access spatial modulation

Serafimovski

Sinanović

Renzo

et al. 2012

J Wireless Com Network

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“…is the same, we only need to study the indifference curve of 3,6,9,12 SNR dB. From figure 2 we can see that the negative utility function is reduced with SNR increasing.…”

Section: A Overall Simulation Designmentioning

confidence: 99%

Joint Resource Allocation for Uplink Virtual MIMO Systems under Elastic MSE Constraint

Qiu

et al. 2015

2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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Virtual MIMO, also known as Multiuser-MIMO, has been widely investigated to improve data transmission rate of various wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference.To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average MSE performance in uplink SC-FDMA systems. Based on linear MIMO detection, we first develop MSEoriented user grouping criterion for evaluation of transmission performance, then propose a dynamic user grouping and optimal resource allocation algorithm with elastic average MSE constraint. We evaluate the proposed joint resource allocation algorithms in LTE uplink scenarios and the results show that it achieves alterable trade-off between system throughput and average MSE.

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“…Multi-user MIMO (MU-MIMO) has been proposed as a method for increasing the capacity of wireless networks [54], [55]. In MU-MIMO, multiple users are scheduled on the same physical resource blocks (PRBs).…”

Section: Application To Mu-mimo Detectionmentioning

confidence: 99%

Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

Mansour

Jalloul

2015

IEEE Trans. Signal Process.

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This paper presents an optimized low-complexity and high-throughput multiple-input multiple-output ( MIMO) signal detector core for detecting spatially-multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, as well as configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori (MAP) MIMO detector are optimized to eliminate the use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied as well to design an optimal multi-user MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90 nm CMOS.

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MU-MIMO in LTE Systems

Cited by 72 publications

References 20 publications

Multiple access spatial modulation

Multiple access spatial modulation

Joint Resource Allocation for Uplink Virtual MIMO Systems under Elastic MSE Constraint

Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

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