Increased capacity per unit-cost by oversampling

Koch, Tobias; Lapidoth, Amos

doi:10.1109/eeei.2010.5662127

Cited by 73 publications

(88 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capacity (in bits per second) of the AWGN channel with Nyquist sampling and 1-bit output quantization is given by (49) where is the capacity (7) of the discrete-time channel in nats per channel use. Note, however, that when the channel output is quantized, sampling at the Nyquist rate need not be optimal with respect to capacity: see, e.g., [6]- [9] for scenarios where sampling the quantizer's output above the Nyquist rate provides capacity gains. Consequently, is, in general, a lower bound on the capacity of the AWGN channel with 1-bit output quantization.…”

Section: Spectral Efficiencymentioning

confidence: 99%

“…Extending the proof of Theorem 2 in [11] to account for the additional maximization over all possible quantizers, we obtain (13) which, by (6), can be expressed as (14) Exchanging the order of the suprema and applying (10) …”

Section: Channel Model and Capacitymentioning

confidence: 99%

“…This will prove (84). 6 To prove that the extreme points of are indeed the channel laws corresponding to quantizers of the form (85) (90) where (91) We rewrite (90) as (92) where (93) The integral on the RHS of (92) is maximized when is the set (94)…”

Section: B Quantizers For Three-mass-point Input Distributionsmentioning

confidence: 99%

“…While sampling the output at Nyquist rate incurs no loss in capacity for the additive white Gaussian noise (AWGN) channel [4], [5], it is not necessarily optimal (with respect to capacity) when the channel output is first quantized using a 1-bit quantizer. In fact, when a symmetric threshold quantizer is employed, sampling the output above the Nyquist rate increases the low-SNR asymptotic capacity [6], [7] and it increases the capacity in the noiseless case [8], [9].…”

mentioning

confidence: 99%

See 3 more Smart Citations

At Low SNR, Asymmetric Quantizers are Better

Koch

Lapidoth

2013

IEEE Trans. Inform. Theory

Self Cite

View full text Add to dashboard Cite

Abstract-We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of , i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level.Index Terms-Capacity per unit energy, channel capacity, Gaussian channel, low signal-to-noise ratio (SNR), quantization.

show abstract

Section: Spectral Efficiencymentioning

confidence: 99%

Section: Channel Model and Capacitymentioning

confidence: 99%

Section: B Quantizers For Three-mass-point Input Distributionsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

At Low SNR, Asymmetric Quantizers are Better

Koch

Lapidoth

2013

IEEE Trans. Inform. Theory

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sampling faster than the Nyquist rate may be beneficial when using low-precision ADCs (see, e.g., [21], [22] and [23,Sec. VI]).…”

Section: B Relevant Prior Artmentioning

confidence: 99%

Quantized Massive MU-MIMO-OFDM Uplink

Studer

Durisi

2016

IEEE Trans. Commun.

232

223

View full text Add to dashboard Cite

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.

show abstract