263 research outputs found
Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs
With the congestion of the sub-6 GHz spectrum, the interest in massive
multiple-input multiple-output (MIMO) systems operating on millimeter wave
spectrum grows. In order to reduce the power consumption of such massive MIMO
systems, hybrid analog/digital transceivers and application of low-resolution
digital-to-analog/analog-to-digital converters have been recently proposed. In
this work, we investigate the energy efficiency of quantized hybrid
transmitters equipped with a fully/partially-connected phase-shifting network
composed of active/passive phase-shifters and compare it to that of quantized
digital precoders. We introduce a quantized single-user MIMO system model based
on an additive quantization noise approximation considering realistic power
consumption and loss models to evaluate the spectral and energy efficiencies of
the transmit precoding methods. Simulation results show that
partially-connected hybrid precoders can be more energy-efficient compared to
digital precoders, while fully-connected hybrid precoders exhibit poor energy
efficiency in general. Also, the topology of phase-shifting components offers
an energy-spectral efficiency trade-off: active phase-shifters provide higher
data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin
Self-Supervised and Invariant Representations for Wireless Localization
In this work, we present a wireless localization method that operates on
self-supervised and unlabeled channel estimates. Our self-supervising method
learns general-purpose channel features robust to fading and system
impairments. Learned representations are easily transferable to new
environments and ready to use for other wireless downstream tasks. To the best
of our knowledge, the proposed method is the first joint-embedding
self-supervised approach to forsake the dependency on contrastive channel
estimates. Our approach outperforms fully-supervised techniques in small data
regimes under fine-tuning and, in some cases, linear evaluation. We assess the
performance in centralized and distributed massive MIMO systems for multiple
datasets. Moreover, our method works indoors and outdoors without additional
assumptions or design changes
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