Multiuser Detection in Massive Spatial Modulation MIMO With Low-Resolution ADCs

Wang, Shengchu; Li, Yunzhou; Wang, Jing; Xu, Xiaodong

doi:10.1109/twc.2014.2382098

Cited by 194 publications

(159 citation statements)

References 36 publications

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“…For the case of 1-bit ADCs combined with time-multiplexed pilot transmission, it was shown recently that the ML estimate has a closed-form expression [32]. Data detection algorithms have beed discussed, e.g., in [33]- [36]. All these works, however, deal exclusively with systems operating over frequency-flat (or narrowband) fading channels.…”

Section: B Relevant Prior Artmentioning

confidence: 99%

Quantized Massive MU-MIMO-OFDM Uplink

Studer

Durisi

2016

IEEE Trans. Commun.

229

223

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Abstract-Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter (ADC) data that must be transferred from a spatially-separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a-posteriori (MAP) channel estimation and data detection, and we study the associated performance/quantization trade-offs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared to the infinite-precision case at no additional cost in terms of baseband processing complexity.Index Terms-Analog-to-digital conversion, convex optimization, forward-backward splitting (FBS), frequency-selective channels, massive multi-user multiple-input multiple-output (MU-MIMO), maximum a-posteriori (MAP) channel estimation, minimum mean-square error (MMSE) data detection, orthogonal frequency-division multiplexing (OFDM), quantization.

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Section: B Relevant Prior Artmentioning

confidence: 99%

Quantized Massive MU-MIMO-OFDM Uplink

Studer

Durisi

2016

IEEE Trans. Commun.

229

223

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“…In [112], the MP detector of Based on the system model of [112], in [113] the authors 1197 investigated the MU detection issues in more practical scenar-1198 ios, where the BS was equipped with low-resolution analog-1199 to-digital convertors (ADCs), only impose a low circuit-power 1200 consumption on the LS-MIMO system. In [113], the classic 1201 least-square channel estimator was invoked and a novel low-1202 complexity MP de-quantization detector was proposed, relying 1203 on the clustered factor graph method and the central limit the-1204 orem.…”

Section: Table VII Major Contributions On Sc-sm and Its Large-scale mentioning

confidence: 99%

“…In [113], the classic 1201 least-square channel estimator was invoked and a novel low-1202 complexity MP de-quantization detector was proposed, relying 1203 on the clustered factor graph method and the central limit the-1204 orem. The simulation results of [113] have shown that the 1205 proposed detector outperforms the existing linear detectors and 1206 can efficiently operate under realistic LS-MIMO channel con-1207 ditions, when the antennas are insufficiently far apart to avoid 1208 correlated fading.…”

Section: Table VII Major Contributions On Sc-sm and Its Large-scale mentioning

confidence: 99%

Single-Carrier SM-MIMO: A Promising Design for Broadband Large-Scale Antenna Systems

Yang

Xiao

Guan

et al. 2016

IEEE Commun. Surv. Tutorials

217

102

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“…Based on the valuesζ obtained from Eq. (13), the ABEP of the SMx-SM system can be expressed from (10) and (12). …”

Section: A the Abep Of The Proposed Smx-sm Systemmentioning

confidence: 99%

Compressed-Sensing Assisted Spatial Multiplexing Aided Spatial Modulation

Xiao

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-Spatial-Multiplexing aided Spatial Modulation (SMx-SM) is proposed, which intrinsically amalgamates the concept of Vertical Bell Labs Space-Time (V-BLAST) and Spatial Modulation (SM) to attain a high transmission rate, despite its low number of Radio Frequency (RF) chains at the transmitter. Specifically, in the SMx-SM scheme, the Transmit Antennas (TAs) are partitioned into groups and the SM technique is applied individually to each group. Furthermore, lowcomplexity threshold-aided Compressive Sensing (CS) based and Message Passing (MP) based detectors are derived for our SMx-SM system. Our simulation results show that the proposed SMx-SM system exhibits a better performance despite its lower complexity than the Conventional Generalized Spatial Modulation (C-GSM) system. More importantly, the proposed SMx-SM system is capable of providing considerable performance gains over the V-BLAST system at the same number of RF chains and throughput. Finally, an upper bound is derived for the Average Bit Error Probability (ABEP), which is confirmed by our simulation results.

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Multiuser Detection in Massive Spatial Modulation MIMO With Low-Resolution ADCs

Cited by 194 publications

References 36 publications

Quantized Massive MU-MIMO-OFDM Uplink

Quantized Massive MU-MIMO-OFDM Uplink

Single-Carrier SM-MIMO: A Promising Design for Broadband Large-Scale Antenna Systems

Compressed-Sensing Assisted Spatial Multiplexing Aided Spatial Modulation

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