1,294 research outputs found
Reconfigurable Intelligent Surface aided Massive MIMO Systems with Low-Resolution DACs
We investigate a reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multi-output (MIMO) system where low-resolution digital-analog converters (DACs) are configured at the base station (BS) in order to reduce the cost and power consumption. An approximate analytical expression for the downlink achievable rate is derived based on maximum ratio transmission (MRT) and additive quantization noise model (AQNM), and the rate maximization problem is solved by particle swarm optimization (PSO) method under both continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS. Simulation results show that the downlink sum achievable rate tends to a constant with the increase of the number of quantization bits of DACs, and four quantization bits are enough to capture a large portion of the performance of the ideal perfect DACs case
Secure Massive MIMO Communication with Low-resolution DACs
In this paper, we investigate secure transmission in a massive multiple-input
multiple-output (MIMO) system adopting low-resolution digital-to-analog
converters (DACs). Artificial noise (AN) is deliberately transmitted
simultaneously with the confidential signals to degrade the eavesdropper's
channel quality. By applying the Bussgang theorem, a DAC quantization model is
developed which facilitates the analysis of the asymptotic achievable secrecy
rate. Interestingly, for a fixed power allocation factor , low-resolution
DACs typically result in a secrecy rate loss, but in certain cases they provide
superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically,
we derive a closed-form SNR threshold which determines whether low-resolution
or high-resolution DACs are preferable for improving the secrecy rate.
Furthermore, a closed-form expression for the optimal is derived. With
AN generated in the null-space of the user channel and the optimal ,
low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for
random AN with the optimal , the secrecy rate is hardly affected by the
DAC resolution because the negative impact of the quantization noise can be
compensated for by reducing the AN power. All the derived analytical results
are verified by numerical simulations.Comment: 14 pages, 10 figure
Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
We consider the downlink of a massive multiuser (MU) multiple-input
multiple-output (MIMO) system in which the base station (BS) is equipped with
low-resolution digital-to-analog converters (DACs). In contrast to most
existing results, we assume that the system operates over a frequency-selective
wideband channel and uses orthogonal frequency division multiplexing (OFDM) to
simplify equalization at the user equipments (UEs). Furthermore, we consider
the practically relevant case of oversampling DACs. We theoretically analyze
the uncoded bit error rate (BER) performance with linear precoders (e.g., zero
forcing) and quadrature phase-shift keying using Bussgang's theorem. We also
develop a lower bound on the information-theoretic sum-rate throughput
achievable with Gaussian inputs, which can be evaluated in closed form for the
case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet
accurate, expressions for the distortion caused by low-precision DACs, which
can be used to establish lower bounds on the corresponding sum-rate throughput.
Our results demonstrate that, for a massive MU-MIMO-OFDM system with a
128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an
uncoded BER of 10^-4 with a negligible performance loss compared to the
infinite-resolution case at the cost of additional out-of-band emissions.
Furthermore, our results highlight the importance of taking into account the
inherent spatial and temporal correlations caused by low-precision DACs
A Mixed-ADC Receiver Architecture for Massive MIMO Systems
Motivated by the demand for energy-efficient communication solutions in the
next generation cellular network, a mixed-ADC receiver architecture for massive
multiple input multiple output (MIMO) systems is proposed, which differs from
previous works in that herein one-bit analog-to-digital converters (ADCs)
partially replace the conventionally assumed high-resolution ADCs. The
information-theoretic tool of generalized mutual information (GMI) is exploited
to analyze the achievable data rates of the proposed system architecture and an
array of analytical results of engineering interest are obtained. For
deterministic single input multiple output (SIMO) channels, a closed-form
expression of the GMI is derived, based on which the linear combiner is
optimized. Then, the asymptotic behaviors of the GMI in both low and high SNR
regimes are explored, and the analytical results suggest a plausible ADC
assignment scheme. Finally, the analytical framework is applied to the
multi-user access scenario, and the corresponding numerical results demonstrate
that the mixed system architecture with a relatively small number of
high-resolution ADCs is able to achieve a large fraction of the channel
capacity without output quantization.Comment: 5 pages, 5 figures, to appear in IEEE Information Theory Workshop
(ITW2015
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