7,785 research outputs found
LQG Control Approach to Gaussian Broadcast Channels with Feedback
A code for communication over the k-receiver additive white Gaussian noise
broadcast channel with feedback is presented and analyzed using tools from the
theory of linear quadratic Gaussian optimal control. It is shown that the
performance of this code depends on the noise correlation at the receivers and
it is related to the solution of a discrete algebraic Riccati equation. For the
case of independent noises, the sum rate achieved by the proposed code,
satisfying average power constraint P, is characterized as 1/2 log (1+P*phi),
where the coefficient "phi" in the interval [1,k] quantifies the power gain due
to the presence of feedback. When specialized to the case of two receivers,
this includes a previous result by Elia and strictly improves upon the code of
Ozarow and Leung. When the noises are correlated, the pre-log of the
sum-capacity of the broadcast channel with feedback can be strictly greater
than one. It is established that for all noise covariance matrices of rank r
the pre-log of the sum capacity is at most k-r+1 and, conversely, there exists
a noise covariance matrix of rank r for which the proposed code achieves this
upper bound. This generalizes a previous result by Gastpar and Wigger for the
two-receiver broadcast channel.Comment: Submitted to IEEE Transactions on Information Theor
On the Noisy Feedback Capacity of Gaussian Broadcast Channels
It is well known that, in general, feedback may enlarge the capacity region
of Gaussian broadcast channels. This has been demonstrated even when the
feedback is noisy (or partial-but-perfect) and only from one of the receivers.
The only case known where feedback has been shown not to enlarge the capacity
region is when the channel is physically degraded (El Gamal 1978, 1981). In
this paper, we show that for a class of two-user Gaussian broadcast channels
(not necessarily physically degraded), passively feeding back the stronger
user's signal over a link corrupted by Gaussian noise does not enlarge the
capacity region if the variance of feedback noise is above a certain threshold.Comment: 5 pages, 3 figures, to appear in IEEE Information Theory Workshop
2015, Jerusale
Compressive Sensing for Feedback Reduction in MIMO Broadcast Channels
We propose a generalized feedback model and compressive sensing based
opportunistic feedback schemes for feedback resource reduction in MIMO
Broadcast Channels under the assumption that both uplink and downlink channels
undergo block Rayleigh fading. Feedback resources are shared and are
opportunistically accessed by users who are strong, i.e. users whose channel
quality information is above a certain fixed threshold. Strong users send same
feedback information on all shared channels. They are identified by the base
station via compressive sensing. Both analog and digital feedbacks are
considered. The proposed analog & digital opportunistic feedback schemes are
shown to achieve the same sum-rate throughput as that achieved by dedicated
feedback schemes, but with feedback channels growing only logarithmically with
number of users. Moreover, there is also a reduction in the feedback load. In
the analog feedback case, we show that the propose scheme reduces the feedback
noise which eventually results in better throughput, whereas in the digital
feedback case the proposed scheme in a noisy scenario achieves almost the
throughput obtained in a noiseless dedicated feedback scenario. We also show
that for a fixed given budget of feedback bits, there exist a trade-off between
the number of shared channels and thresholds accuracy of the feedback SINR.Comment: Submitted to IEEE Transactions on Wireless Communications, April 200
Degrees of Freedom of Time Correlated MISO Broadcast Channel with Delayed CSIT
We consider the time correlated multiple-input single-output (MISO) broadcast
channel where the transmitter has imperfect knowledge on the current channel
state, in addition to delayed channel state information. By representing the
quality of the current channel state information as P^-{\alpha} for the
signal-to-noise ratio P and some constant {\alpha} \geq 0, we characterize the
optimal degree of freedom region for this more general two-user MISO broadcast
correlated channel. The essential ingredients of the proposed scheme lie in the
quantization and multicasting of the overheard interferences, while
broadcasting new private messages. Our proposed scheme smoothly bridges between
the scheme recently proposed by Maddah-Ali and Tse with no current state
information and a simple zero-forcing beamforming with perfect current state
information.Comment: revised and final version, to appear in IEEE transactions on
Information Theor
Fundamental Limits in MIMO Broadcast Channels
This paper studies the fundamental limits of MIMO broadcast channels from a high level, determining the sum-rate capacity of the system as a function of system paramaters, such as the number of transmit antennas, the number of users, the number of receive antennas, and the total transmit power. The crucial role of channel state information at the transmitter is emphasized, as well as the emergence of opportunistic transmission schemes. The effects of channel estimation errors, training, and spatial correlation are studied, as well as issues related to fairness, delay and differentiated rate scheduling
Linear Beamforming for the Spatially Correlated MISO broadcast channel
A spatially correlated broadcast setting with M antennas at the base station
and M users (each with a single antenna) is considered. We assume that the
users have perfect channel information about their links and the base station
has only statistical information about each user's link. The base station
employs a linear beamforming strategy with one spatial eigen-mode allocated to
each user. The goal of this work is to understand the structure of the
beamforming vectors that maximize the ergodic sum-rate achieved by treating
interference as noise. In the M = 2 case, we first fix the beamforming vectors
and compute the ergodic sum-rate in closed-form as a function of the channel
statistics. We then show that the optimal beamforming vectors are the dominant
generalized eigenvectors of the covariance matrices of the two links. It is
difficult to obtain intuition on the structure of the optimal beamforming
vectors for M > 2 due to the complicated nature of the sum-rate expression.
Nevertheless, in the case of asymptotic M, we show that the optimal beamforming
vectors have to satisfy a set of fixed-point equations.Comment: Published in IEEE ISIT 2010, 5 page
Limited Feedback-based Block Diagonalization for the MIMO Broadcast Channel
Block diagonalization is a linear precoding technique for the multiple
antenna broadcast (downlink) channel that involves transmission of multiple
data streams to each receiver such that no multi-user interference is
experienced at any of the receivers. This low-complexity scheme operates only a
few dB away from capacity but requires very accurate channel knowledge at the
transmitter. We consider a limited feedback system where each receiver knows
its channel perfectly, but the transmitter is only provided with a finite
number of channel feedback bits from each receiver. Using a random quantization
argument, we quantify the throughput loss due to imperfect channel knowledge as
a function of the feedback level. The quality of channel knowledge must improve
proportional to the SNR in order to prevent interference-limitations, and we
show that scaling the number of feedback bits linearly with the system SNR is
sufficient to maintain a bounded rate loss. Finally, we compare our
quantization strategy to an analog feedback scheme and show the superiority of
quantized feedback.Comment: 20 pages, 4 figures, submitted to IEEE JSAC November 200
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