494 research outputs found
Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems with Partial CSIT: A Rate-Splitting Approach
This paper considers the Sum-Rate (SR) maximization problem in downlink
MU-MISO systems under imperfect Channel State Information at the Transmitter
(CSIT). Contrary to existing works, we consider a rather unorthodox
transmission scheme. In particular, the message intended to one of the users is
split into two parts: a common part which can be recovered by all users, and a
private part recovered by the corresponding user. On the other hand, the rest
of users receive their information through private messages. This
Rate-Splitting (RS) approach was shown to boost the achievable Degrees of
Freedom (DoF) when CSIT errors decay with increased SNR. In this work, the RS
strategy is married with linear precoder design and optimization techniques to
achieve a maximized Ergodic SR (ESR) performance over the entire range of SNRs.
Precoders are designed based on partial CSIT knowledge by solving a stochastic
rate optimization problem using means of Sample Average Approximation (SAA)
coupled with the Weighted Minimum Mean Square Error (WMMSE) approach. Numerical
results show that in addition to the ESR gains, the benefits of RS also include
relaxed CSIT quality requirements and enhanced achievable rate regions compared
to conventional transmission with NoRS.Comment: accepted to IEEE Transactions on Communication
Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach
We study multi-user massive multiple-input single-output (MISO) systems and
focus on downlink transmission, where the base station (BS) employs a large
antenna array with low-cost 1-bit digital-to-analog converters (DACs). The
direct combination of existing beamforming schemes with 1-bit DACs is shown to
lead to an error floor at medium-to-high SNR regime, due to the coarse
quantization of the DACs with limited precision. In this paper, based on the
constructive interference we consider both a quantized linear beamforming
scheme where we analytically obtain the optimal beamforming matrix, and a
non-linear mapping scheme where we directly design the transmit signal vector.
Due to the 1-bit quantization, the formulated optimization for the non-linear
mapping scheme is shown to be non-convex. To solve this problem, the non-convex
constraints of the 1-bit DACs are firstly relaxed, followed by an element-wise
normalization to satisfy the 1-bit DAC transmission. We further propose a
low-complexity symbol scaling scheme that consists of three stages, in which
the quantized transmit signal on each antenna element is selected sequentially.
Numerical results show that the proposed symbol scaling scheme achieves a
comparable performance to the optimization-based non-linear mapping approach,
while its corresponding complexity is negligible compared to that of the
non-linear scheme.Comment: 15 page
On the MISO Channel with Feedback: Can Infinitely Massive Antennas Achieve Infinite Capacity?
We consider communication over a multiple-input single-output (MISO) block
fading channel in the presence of an independent noiseless feedback link. We
assume that the transmitter and receiver have no prior knowledge of the channel
state realizations, but the transmitter and receiver can acquire the channel
state information (CSIT/CSIR) via downlink training and feedback. For this
channel, we show that increasing the number of transmit antennas to infinity
will not achieve an infinite capacity, for a finite channel coherence length
and a finite input constraint on the second or fourth moment. This insight
follows from our new capacity bounds that hold for any linear and nonlinear
coding strategies, and any channel training schemes. In addition to the channel
capacity bounds, we also provide a characterization on the beamforming gain
that is also known as array gain or power gain, at the regime with a large
number of antennas.Comment: This work has been submitted to the IEEE Transactions on Information
Theory. It was presented in part at ISIT201
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