Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM

Nedelcu, Andrei; Steiner, Fabian; Kramer, Gerhard

doi:10.3390/e24040504

Cited by 7 publications

(7 citation statements)

References 47 publications

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“…The right-hand side (RHS) of ( 10) is called a generalized mutual information (GMI) [39,40] and has been applied to problems in information theory [41], wireless communications [42][43][44][45][46][47][48][49][50][51], and fiber-optic communications [52][53][54][55][56][57][58][59][60][61]. For example, the bounds (6) and (10) are the same if s = 1 and q(y|x) = exp −|y − hx| 2 σ 2 (11) where h and σ 2 are given by (7).…”

Section: Auxiliary Modelsmentioning

confidence: 99%

Information Rates for Channels with Fading, Side Information and Adaptive Codewords

Kramer

2023

Entropy

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Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel models with additive white Gaussian noise (AWGN) and circularly-symmetric complex Gaussian inputs. One variation uses reverse channel models with minimum mean square error (MMSE) estimates that give the largest rates but are challenging to optimize. A second variation uses forward channel models with linear MMSE estimates that are easier to optimize. Both model classes are applied to channels where the receiver is unaware of the CSIT and for which adaptive codewords achieve capacity. The forward model inputs are chosen as linear functions of the adaptive codeword’s entries to simplify the analysis. For scalar channels, the maximum GMI is then achieved by a conventional codebook, where the amplitude and phase of each channel symbol are modified based on the CSIT. The GMI increases by partitioning the channel output alphabet and using a different auxiliary model for each partition subset. The partitioning also helps to determine the capacity scaling at high and low signal-to-noise ratios. A class of power control policies is described for partial CSIR, including a MMSE policy for full CSIT. Several examples of fading channels with AWGN illustrate the theory, focusing on on-off fading and Rayleigh fading. The capacity results generalize to block fading channels with in-block feedback, including capacity expressions in terms of mutual and directed information.

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Section: Auxiliary Modelsmentioning

confidence: 99%

Information Rates for Channels with Fading, Side Information and Adaptive Codewords

Kramer

2023

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“…In the original publication [ 1 ], there was a mistake in Figure 1 as published. The labels of the output signals x 1 [t]…x N [t] should appear at the output of the power amplifier as transmit waveforms.…”

Section: Error In Figurementioning

confidence: 99%

Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504

Nedelcu

Steiner

Kramer

2023

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“…A large body of literature is concerned with the performance achievable with multiuser massive MIMO systems in which the BS is equipped with low-resolution data converters. Existing works include the derivation of information-theoretic achievable rates in the ergodic scenario with Gaussian codebooks [5], [6], [7], [8], [9], [10], [11], the design of channel estimation and data-detection algorithms [12], [13], [14], [15], [16], [17], of linear and nonlinear precoders [8], [18], [19], [20], and of low-complexity equalization techniques [21]. All of these results reveal that satisfactory performance can be achieved even when using 1-bit converters at the RRHs, and that, by using 3-to-5-bit converters, one can approach closely the performance achievable in the infinite-precision case.…”

Section: A Prior Artmentioning

confidence: 99%

Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

et al. 2023

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We provide a rigorous framework for characterizing and numerically evaluating the error probability achievable in the uplink and downlink of a fully digital quantized multiuser multiple-input multiple-output (MIMO) system. We assume that the system operates over a quasi-static channel that does not change across the finite-length transmitted codewords, and only imperfect channel state information (CSI) is available at the base station (BS) and at the user equipments. The need for the novel framework developed in this paper stems from the fact that, for the quasi-static scenario, commonly used signal-to-interferenceand-distortion-ratio expressions that depend on the variance of the channel estimation error are not relatable to any rigorous information-theoretic achievable-rate bound. We use our framework to investigate how the performance of a fully digital massive MIMO system subject to a fronthaul rate constraint, which imposes a limit on the number of samples per second produced by the analog-to-digital and digital-to-analog converters (ADCs and DACs), depends on the number of BS antennas and on the precision of the ADCs and DACs. In particular, we characterize, for a given fronthaul constraint, the trade-off between the number of antennas and the resolution of the data converters, and discuss how this trade-off is influenced by the accuracy of the available CSI. Our framework captures explicitly the cost, in terms of spectral efficiency, of pilot transmission-an overhead that the outage capacity, the classic asymptotic metric used in this scenario, cannot capture. We present extensive numerical results that validate the accuracy of the proposed framework and allow us to characterize, for a given fronthaul constraint, the optimal number of antennas and the optimal resolution of the converters as a function of the transmitted power and of the available CSI.INDEX TERMS Finite blocklength, fronthaul rate constraint, low-precision converters, multi-user massive multiple-input multiple-output (MIMO), quasi-static scenario.

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Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM

Cited by 7 publications

References 47 publications

Information Rates for Channels with Fading, Side Information and Adaptive Codewords

Information Rates for Channels with Fading, Side Information and Adaptive Codewords

Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504

Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

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