1,425 research outputs found
One-Bit Massive MIMO: Channel Estimation and High-Order Modulations
We investigate the information-theoretic throughout achievable on a fading
communication link when the receiver is equipped with one-bit analog-to-digital
converters (ADCs). The analysis is conducted for the setting where neither the
transmitter nor the receiver have a priori information on the realization of
the fading channels. This means that channel-state information needs to be
acquired at the receiver on the basis of the one-bit quantized channel outputs.
We show that least-squares (LS) channel estimation combined with joint pilot
and data processing is capacity achieving in the single-user,
single-receive-antenna case.
We also investigate the achievable uplink throughput in a massive
multiple-input multiple-output system where each element of the antenna array
at the receiver base-station feeds a one-bit ADC. We show that LS channel
estimation and maximum-ratio combining are sufficient to support both multiuser
operation and the use of high-order constellations. This holds in spite of the
severe nonlinearity introduced by the one-bit ADCs
Distributed closed-loop EO-STBC for a time-varying relay channel based on kalman tracking
This paper considers distributed closed-loop extended orthogonal space-time block coding (EO-STBC) for amplify-forward relaying over time-varying channels. In between periodically injected pilot symbols for training, the smooth variation of the fading channel coefficients is exploited by Kalman tracking. We show in this paper that the joint variation of both relay channels still motivates the use of a higher-order auto-regressive model for the a priori prediction step within a decision-feedback system, compared to a first-order standard Kalman model. Simulations results compare these two case and highlight the benefits of the proposed higher-order Kalman filter, which offer joint decoding and tracking
Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels
Massive multiple-input--multiple-output (MIMO) systems can suffer from
coherent intercell interference due to the phenomenon of pilot contamination.
This paper investigates a two-layer decoding method that mitigates both
coherent and non-coherent interference in multi-cell Massive MIMO. To this end,
each base station (BS) first estimates the channels to intra-cell users using
either minimum mean-squared error (MMSE) or element-wise MMSE (EW-MMSE)
estimation based on uplink pilots. The estimates are used for local decoding on
each BS followed by a second decoding layer where the BSs cooperate to mitigate
inter-cell interference. An uplink achievable spectral efficiency (SE)
expression is computed for arbitrary two-layer decoding schemes. A closed-form
expression is then obtained for correlated Rayleigh fading, maximum-ratio
combining, and the proposed large-scale fading decoding (LSFD) in the second
layer. We also formulate a sum SE maximization problem with both the data power
and LSFD vectors as optimization variables. Since this is an NP-hard problem,
we develop a low-complexity algorithm based on the weighted MMSE approach to
obtain a local optimum. The numerical results show that both data power control
and LSFD improves the sum SE performance over single-layer decoding multi-cell
Massive MIMO systems.Comment: 17 pages; 10 figures; Accepted for publication in IEEE Transactions
on Communication
Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
This paper investigates the joint data and pilot power optimization for
maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which
is a non-convex problem. We first propose a new optimization algorithm,
inspired by the weighted minimum mean square error (MMSE) approach, to obtain a
stationary point in polynomial time. We then use this algorithm together with
deep learning to train a convolutional neural network to perform the joint data
and pilot power control in sub-millisecond runtime, making it suitable for
online optimization in real multi-cell Massive MIMO systems. The numerical
result demonstrates that the solution obtained by the neural network is
less than the stationary point for four-cell systems, while the sum SE loss is
in a nine-cell system.Comment: 4 figures, 1 table. Accepted by ICC 2019. arXiv admin note: text
overlap with arXiv:1901.0362
- …