8,497 research outputs found
Modulation-mode Assignment for SVD-assisted Multiuser MIMO Systems with Correlation.
Multiuser multiple-input multiple-output (MIMO) downlink (DL) transmission schemes experience both multiuser interference as well as inter-antenna interference. The singular value decomposition provides an appropriate mean to process channel information and allows us to take the individual user’s channel characteristics into account rather than treating all users channels jointly as in zero-forcing (ZF) multiuser transmission techniques. However, uncorrelated MIMO channels has attracted a lot of attention and reached a state of maturity. By contrast, the performance analysis in the presence of antenna fading correlation, which decreases the channel capacity, requires substantial further research. The joint optimization of the number of activated MIMO layers and the number of bits per symbol along with the appropriate allocation of the transmit power shows that not necessarily all user-specific MIMO layers has to be activated in order to minimize the overall BER under the constraint of a given fixed data throughput
Sum rate maximization of MIMO broadcast channels with coordination of base stations
Abstract-We consider cooperative downlink transmission in multiuser, multi-cell and multiple-antenna cellular networks. Recently, it has been shown that multi-base coordinated transmission has significant spectral efficiency gains over that without coordination. The capacity limits can be achieved using a non-linear precoding technique known as dirty paper coding, which is still infeasible to implement in practice. This motivates investigation of a simpler linear precoding technique based on generalized zero-forcing known as block diagonalization (BD). In this paper, an enhanced form of BD is proposed for multiple-input multiple-output (MIMO) multi-base coordinated network. It involves optimizing the precoding over the entire null space of other users' transmissions. The performance limits of the multiple-antenna downlink with multi-base coordination are studied using duality of MIMO broadcast channels (BC) and MIMO multiple-access channels (MAC) under per-antenna power constraint, which has been established recently
On the MIMO Capacity with Multiple Linear Transmit Covariance Constraints
This paper presents an efficient approach to computing the capacity of multiple-input multiple-output (MIMO) channels under multiple linear transmit covariance constraints (LTCCs). LTCCs are general enough to include several special types of power constraints as special cases such as the sum power constraint (SPC), per-antenna power constraint (PAPC), or a combination thereof. Despite its importance and generality, most of the existing literature considers either SPC or PAPC independently. Efficient solutions to the computation of the MIMO capacity with a combination of SPC and PAPC have been recently reported, but were only dedicated to multipleinput single-output (MISO) systems. For the general case of LTCCs, we propose a low-complexity semi-closed-form approach tothecomputationoftheMIMOcapacity.Specifically,amodified minimax duality is first invoked to transform the considered problem in the broadcast channel into an equivalent minimax problem in the dual multiple access channel. Then alternating optimization and concave-convex procedure are utilized to derive water-filling-based algorithms to find a saddle point of the minimax problem. This is different from the state-of-the-art solutions to the considered problem, which are based on interiorpoint or subgradient methods. Analytical and numerical results are provided to demonstrate the effectiveness of the proposed low-complexity solution under various MIMO scenarios
Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint
We propose an iterative mode-dropping algorithm that optimizes input signals
to achieve the sum capacity of the MIMO-MAC with per-antenna power constraint.
The algorithm successively optimizes each user's input covariance matrix by
applying mode-dropping to the equivalent single-user MIMO rate maximization
problem. Both analysis and simulation show fast convergence. We then use the
algorithm to briefly highlight the difference in MIMO-MAC capacities under sum
and per-antenna power constraints.Comment: 6 pages double-column, 5 figure
MISO Capacity with Per-Antenna Power Constraint
We establish in closed-form the capacity and the optimal signaling scheme for
a MISO channel with per-antenna power constraint. Two cases of channel state
information are considered: constant channel known at both the transmitter and
receiver, and Rayleigh fading channel known only at the receiver. For the first
case, the optimal signaling scheme is beamforming with the phases of the beam
weights matched to the phases of the channel coefficients, but the amplitudes
independent of the channel coefficients and dependent only on the constrained
powers. For the second case, the optimal scheme is to send independent signals
from the antennas with the constrained powers. In both cases, the capacity with
per-antenna power constraint is usually less than that with sum power
constraint.Comment: 7 pages double-column, 3 figure
Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks
In cognitive radio (CR) networks, there are scenarios where the secondary
(lower priority) users intend to communicate with each other by
opportunistically utilizing the transmit spectrum originally allocated to the
existing primary (higher priority) users. For such a scenario, a secondary user
usually has to trade off between two conflicting goals at the same time: one is
to maximize its own transmit throughput; and the other is to minimize the
amount of interference it produces at each primary receiver. In this paper, we
study this fundamental tradeoff from an information-theoretic perspective by
characterizing the secondary user's channel capacity under both its own
transmit-power constraint as well as a set of interference-power constraints
each imposed at one of the primary receivers. In particular, this paper
exploits multi-antennas at the secondary transmitter to effectively balance
between spatial multiplexing for the secondary transmission and interference
avoidance at the primary receivers. Convex optimization techniques are used to
design algorithms for the optimal secondary transmit spatial spectrum that
achieves the capacity of the secondary transmission. Suboptimal solutions for
ease of implementation are also presented and their performances are compared
with the optimal solution. Furthermore, algorithms developed for the
single-channel transmission are also extended to the case of multi-channel
transmission whereby the secondary user is able to achieve opportunistic
spectrum sharing via transmit adaptations not only in space, but in time and
frequency domains as well.Comment: Extension of IEEE PIMRC 2007. 35 pages, 6 figures. Submitted to IEEE
Journal of Special Topics in Signal Processing, special issue on Signal
Processing and Networking for Dynamic Spectrum Acces
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