1,001 research outputs found
Random Beamforming with Heterogeneous Users and Selective Feedback: Individual Sum Rate and Individual Scaling Laws
This paper investigates three open problems in random beamforming based
communication systems: the scheduling policy with heterogeneous users, the
closed form sum rate, and the randomness of multiuser diversity with selective
feedback. By employing the cumulative distribution function based scheduling
policy, we guarantee fairness among users as well as obtain multiuser diversity
gain in the heterogeneous scenario. Under this scheduling framework, the
individual sum rate, namely the average rate for a given user multiplied by the
number of users, is of interest and analyzed under different feedback schemes.
Firstly, under the full feedback scheme, we derive the closed form individual
sum rate by employing a decomposition of the probability density function of
the selected user's signal-to-interference-plus-noise ratio. This technique is
employed to further obtain a closed form rate approximation with selective
feedback in the spatial dimension. The analysis is also extended to random
beamforming in a wideband OFDMA system with additional selective feedback in
the spectral dimension wherein only the best beams for the best-L resource
blocks are fed back. We utilize extreme value theory to examine the randomness
of multiuser diversity incurred by selective feedback. Finally, by leveraging
the tail equivalence method, the multiplicative effect of selective feedback
and random observations is observed to establish the individual rate scaling.Comment: Submitted in March 2012. To appear in IEEE Transactions on Wireless
Communications. Part of this paper builds upon the following letter: Y. Huang
and B. D. Rao, "Closed form sum rate of random beamforming", IEEE Commun.
Lett., vol. 16, no. 5, pp. 630-633, May 201
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
Multi-User Diversity vs. Accurate Channel State Information in MIMO Downlink Channels
In a multiple transmit antenna, single antenna per receiver downlink channel
with limited channel state feedback, we consider the following question: given
a constraint on the total system-wide feedback load, is it preferable to get
low-rate/coarse channel feedback from a large number of receivers or
high-rate/high-quality feedback from a smaller number of receivers? Acquiring
feedback from many receivers allows multi-user diversity to be exploited, while
high-rate feedback allows for very precise selection of beamforming directions.
We show that there is a strong preference for obtaining high-quality feedback,
and that obtaining near-perfect channel information from as many receivers as
possible provides a significantly larger sum rate than collecting a few
feedback bits from a large number of users.Comment: Submitted to IEEE Transactions on Communications, July 200
Multi-Cell Random Beamforming: Achievable Rate and Degrees of Freedom Region
Random beamforming (RBF) is a practically favourable transmission scheme for
multiuser multi-antenna downlink systems since it requires only partial channel
state information (CSI) at the transmitter. Under the conventional single-cell
setup, RBF is known to achieve the optimal sum-capacity scaling law as the
number of users goes to infinity, thanks to the multiuser diversity enabled
transmission scheduling that virtually eliminates the intra-cell interference.
In this paper, we extend the study of RBF to a more practical multi-cell
downlink system with single-antenna receivers subject to the additional
inter-cell interference (ICI). First, we consider the case of finite
signal-to-noise ratio (SNR) at each receiver. We derive a closed-form
expression of the achievable sum-rate with the multi-cell RBF, based upon which
we show an asymptotic sum-rate scaling law as the number of users goes to
infinity. Next, we consider the high-SNR regime and for tractable analysis
assume that the number of users in each cell scales in a certain order with the
per-cell SNR. Under this setup, we characterize the achievable degrees of
freedom (DoF) for the single-cell case with RBF. Then we extend the analysis to
the multi-cell RBF case by characterizing the DoF region. It is shown that the
DoF region characterization provides useful guideline on how to design a
cooperative multi-cell RBF system to achieve optimal throughput tradeoffs among
different cells. Furthermore, our results reveal that the multi-cell RBF scheme
achieves the "interference-free DoF" region upper bound for the multi-cell
system, provided that the per-cell number of users has a sufficiently large
scaling order with the SNR. Our result thus confirms the optimality of
multi-cell RBF in this regime even without the complete CSI at the transmitter,
as compared to other full-CSI requiring transmission schemes such as
interference alignment.Comment: 28 pages, 6 figures, to appear in IEEE Transactions of Signal
Processing. This work was presented in part at IEEE International Conference
on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, March
25-30, 2012. The authors are with the Department of Electrical and Computer
Engineering, National University of Singapore (emails: {hieudn, elezhang,
elehht}@nus.edu.sg
Vector Broadcast Channels: Optimality of Threshold Feedback Policies
Beamforming techniques utilizing only partial channel state information (CSI)
has gained popularity over other communication strategies requiring perfect CSI
thanks to their lower feedback requirements. The amount of feedback in
beamforming based communication systems can be further reduced through
selective feedback techniques in which only the users with channels good enough
are allowed to feed back by means of a decentralized feedback policy. In this
paper, we prove that thresholding at the receiver is the rate-wise optimal
decentralized feedback policy for feedback limited systems with prescribed
feedback constraints. This result is highly adaptable due to its distribution
independent nature, provides an analytical justification for the use of
threshold feedback policies in practical systems, and reinforces previous work
analyzing threshold feedback policies as a selective feedback technique without
proving its optimality. It is robust to selfish unilateral deviations. Finally,
it reduces the search for rate-wise optimal feedback policies subject to
feedback constraints from function spaces to a finite dimensional Euclidean
space.Comment: Submitted to IEEE International Symposium on Information Theory, St.
Petersburg, Russia, Aug 201
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