Near-Optimal Quantization for LoS MIMO with QPSK Modulation

Sezer, Ahmet Dündar; Madhow, Upamanyu

doi:10.1109/ieeeconf44664.2019.9048696

Cited by 4 publications

(3 citation statements)

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“…Our model is perhaps the simplest possible extension of this framework to MIMO systems. This paper builds on our preliminary results on quantizer design in an earlier conference paper [26]. We provide proofs and technical details, as well as more detailed insights and numerical results, for quantizer design here.…”

Section: Related Workmentioning

confidence: 93%

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All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion

Sezer¹,

Madhow²

2021

Preprint

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Line-of-sight (LoS) multi-input multi-output (MIMO) systems exhibit attractive scaling properties with increase in carrier frequency: for a fixed form factor and range, the spatial degrees of freedom increase quadratically for 2D arrays, in addition to the typically linear increase in available bandwidth.In this paper, we investigate whether modern all-digital baseband signal processing architectures can be devised for such regimes, given the difficulty of analog-to-digital conversion for large bandwidths. We propose low-precision quantizer designs and accompanying spatial demultiplexing algorithms, considering 2 × 2 LoS MIMO with QPSK for analytical insight, and 4 × 4 MIMO with QPSK and 16QAM for performance evaluation. Unlike prior work, channel state information is utilized only at the receiver (i.e., transmit precoding is not employed). We investigate quantizers with regular structure whose high-SNR mutual information approaches that of an unquantized system. We prove that amplitude-phase quantization is necessary to attain this benchmark; phase-only quantization falls short. We show that quantizers based on maximizing per-antenna output entropy perform better than standard Minimum Mean Squared Quantization Error (MMSQE) quantization. For spatial demultiplexing with severely quantized observations, we introduce the novel concept of virtual quantization which, combined with linear detection, provides reliable demodulation at significantly reduced complexity compared to maximum likelihood detection.

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Section: Related Workmentioning

confidence: 93%

“…where Q(•) is the quantizer function, whose set is defined as either (18) or (26). The optimal decision boundaries in ( 18) and ( 26) are obtained as usual, by applying the Lloyd-Max algorithm [31], [32].…”

Section: A Quantizer Designmentioning

confidence: 99%

All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion

Sezer¹,

Madhow²

2021

Preprint

Self Cite

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“…Moreover, the constant envelope of QPSK enables a better channel estimation and hence it is even better than Gaussian modulations at low SNR for the non-coherent channel (where neither the transmitter nor the receiver know the channel) [3]. QPSK was also shown to be optimal in the 1-bit ADC Rician channel case [4], and gained much popularity in practical implementations (e.g., [5], [6]). Hence, it is important to characterize the performance of systems with BPSK and QPSK modulations.…”

Section: Introductionmentioning

confidence: 99%

Lower Bounds on Mutual Information of QPSK and BPSK in General Memoryless channels

Ezri,

Bergel

2024

Preprint

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We derive novel lower bounds on the mutual information in general memoryless channels with quadrature-phase-shift-keying (QPSK) or binary-phase-shift-keying (BPSK) modulation. These novel bounds are very easy to evaluate and result in closed-form expressions. The bounds' derivation is based on the mean squared error (MSE) of the channel input given the channel output. To emphasize the usefulness of these bounds, we evaluate the bounds for two cases of interest. We consider a general additive white noise channel, and derive simple bounds on the mutual information that depend solely on the noise variance (and not on its distribution). In addition, we derive a closed-form lower bound for QPSK modulation over a Rician fading channel with 1-bit analog to digital converter (ADC) at the receiver. The bound characterizes the achievable performance as function of the SNR and the ADC phase offset. The bound is shown to be tight in the absence of phase offset, and at most 25% from the actual mutual information at any phase offset.

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