244 research outputs found
Sum Rates, Rate Allocation, and User Scheduling for Multi-User MIMO Vector Perturbation Precoding
This paper considers the multiuser multiple-input multiple-output (MIMO)
broadcast channel. We consider the case where the multiple transmit antennas
are used to deliver independent data streams to multiple users via vector
perturbation. We derive expressions for the sum rate in terms of the average
energy of the precoded vector, and use this to derive a high signal-to-noise
ratio (SNR) closed-form upper bound, which we show to be tight via simulation.
We also propose a modification to vector perturbation where different rates can
be allocated to different users. We conclude that for vector perturbation
precoding most of the sum rate gains can be achieved by reducing the rate
allocation problem to the user selection problem. We then propose a
low-complexity user selection algorithm that attempts to maximize the high-SNR
sum rate upper bound. Simulations show that the algorithm outperforms other
user selection algorithms of similar complexity.Comment: 27 pages with 6 figures and 2 tables. Accepted for publication in
IEEE Trans. Wireless Comm
Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding
In this paper, we propose a linear precoder for the downlink of a multi-user
MIMO system with multiple users that potentially act as eavesdroppers. The
proposed precoder is based on regularized channel inversion (RCI) with a
regularization parameter and power allocation vector chosen in such a
way that the achievable secrecy sum-rate is maximized. We consider the
worst-case scenario for the multi-user MIMO system, where the transmitter
assumes users cooperate to eavesdrop on other users. We derive the achievable
secrecy sum-rate and obtain the closed-form expression for the optimal
regularization parameter of the precoder using
large-system analysis. We show that the RCI precoder with
outperforms several other linear precoding schemes, and
it achieves a secrecy sum-rate that has same scaling factor as the sum-rate
achieved by the optimum RCI precoder without secrecy requirements. We propose a
power allocation algorithm to maximize the secrecy sum-rate for fixed .
We then extend our algorithm to maximize the secrecy sum-rate by jointly
optimizing and the power allocation vector. The jointly optimized
precoder outperforms RCI with and equal power allocation
by up to 20 percent at practical values of the signal-to-noise ratio and for 4
users and 4 transmit antennas.Comment: IEEE Transactions on Communications, accepted for publicatio
On the Throughput of Large-but-Finite MIMO Networks using Schedulers
This paper studies the sum throughput of the {multi-user}
multiple-input-single-output (MISO) networks in the cases with large but finite
number of transmit antennas and users. Considering continuous and bursty
communication scenarios with different users' data request probabilities, we
derive quasi-closed-form expressions for the maximum achievable throughput of
the networks using optimal schedulers. The results are obtained in various
cases with different levels of interference cancellation. Also, we develop an
efficient scheduling scheme using genetic algorithms (GAs), and evaluate the
effect of different parameters, such as channel/precoding models, number of
antennas/users, scheduling costs and power amplifiers' efficiency, on the
system performance. Finally, we use the recent results on the achievable rates
of finite block-length codes to analyze the system performance in the cases
with short packets. As demonstrated, the proposed GA-based scheduler reaches
(almost) the same throughput as in the exhaustive search-based optimal
scheduler, with substantially less implementation complexity. Moreover, the
power amplifiers' inefficiency and the scheduling delay affect the performance
of the scheduling-based systems significantly
Optimality Properties, Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission
The throughput of multicell systems is inherently limited by interference and
the available communication resources. Coordinated resource allocation is the
key to efficient performance, but the demand on backhaul signaling and
computational resources grows rapidly with number of cells, terminals, and
subcarriers. To handle this, we propose a novel multicell framework with
dynamic cooperation clusters where each terminal is jointly served by a small
set of base stations. Each base station coordinates interference to neighboring
terminals only, thus limiting backhaul signalling and making the framework
scalable. This framework can describe anything from interference channels to
ideal joint multicell transmission.
The resource allocation (i.e., precoding and scheduling) is formulated as an
optimization problem (P1) with performance described by arbitrary monotonic
functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary
linear power constraints. Although (P1) is non-convex and difficult to solve
optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2)
Conditions for full power usage; and 3) A precoding parametrization based on a
few parameters between zero and one. These optimality properties are used to
propose low-complexity strategies: both a centralized scheme and a distributed
version that only requires local channel knowledge and processing. We evaluate
the performance on measured multicell channels and observe that the proposed
strategies achieve close-to-optimal performance among centralized and
distributed solutions, respectively. In addition, we show that multicell
interference coordination can give substantial improvements in sum performance,
but that joint transmission is very sensitive to synchronization errors and
that some terminals can experience performance degradations.Comment: Published in IEEE Transactions on Signal Processing, 15 pages, 7
figures. This version corrects typos related to Eq. (4) and Eq. (28
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