392 research outputs found
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Linear Precoding in Cooperative MIMO Cellular Networks with Limited Coordination Clusters
In a cooperative multiple-antenna downlink cellular network, maximization of
a concave function of user rates is considered. A new linear precoding
technique called soft interference nulling (SIN) is proposed, which performs at
least as well as zero-forcing (ZF) beamforming. All base stations share channel
state information, but each user's message is only routed to those that
participate in the user's coordination cluster. SIN precoding is particularly
useful when clusters of limited sizes overlap in the network, in which case
traditional techniques such as dirty paper coding or ZF do not directly apply.
The SIN precoder is computed by solving a sequence of convex optimization
problems. SIN under partial network coordination can outperform ZF under full
network coordination at moderate SNRs. Under overlapping coordination clusters,
SIN precoding achieves considerably higher throughput compared to myopic ZF,
especially when the clusters are large.Comment: 13 pages, 5 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
Coordinated Beamforming with Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control
In this paper, coordinated beamforming based on relaxed zero forcing (RZF)
for K transmitter-receiver pair multiple-input single-output (MISO) and
multiple-input multiple-output (MIMO) interference channels is considered. In
the RZF coordinated beamforming, conventional zero-forcing interference leakage
constraints are relaxed so that some predetermined interference leakage to
undesired receivers is allowed in order to increase the beam design space for
larger rates than those of the zero-forcing (ZF) scheme or to make beam design
feasible when ZF is impossible. In the MISO case, it is shown that the
rate-maximizing beam vector under the RZF framework for a given set of
interference leakage levels can be obtained by sequential orthogonal projection
combining (SOPC). Based on this, exact and approximate closed-form solutions
are provided in two-user and three-user cases, respectively, and an efficient
beam design algorithm for RZF coordinated beamforming is provided in general
cases. Furthermore, the rate control problem under the RZF framework is
considered. A centralized approach and a distributed heuristic approach are
proposed to control the position of the designed rate-tuple in the achievable
rate region. Finally, the RZF framework is extended to MIMO interference
channels by deriving a new lower bound on the rate of each user.Comment: Lemma 1 proof corrected; a new SOPC algorithm invented; K > N case
considere
Robust Beamforming for Secrecy Rate in Cooperative Cognitive Radio Multicast Communications
In this paper, we propose a cooperative approach to improve the security of
both primary and secondary systems in cognitive radio multicast communications.
During their access to the frequency spectrum licensed to the primary users,
the secondary unlicensed users assist the primary system in fortifying security
by sending a jamming noise to the eavesdroppers, while simultaneously protect
themselves from eavesdropping. The main objective of this work is to maximize
the secrecy rate of the secondary system, while adhering to all individual
primary users' secrecy rate constraints. In the case of passive eavesdroppers
and imperfect channel state information knowledge at the transceivers, the
utility function of interest is nonconcave and involved constraints are
nonconvex, and thus, the optimal solutions are troublesome. To address this
problem, we propose an iterative algorithm to arrive at a local optimum of the
considered problem. The proposed iterative algorithm is guaranteed to achieve a
Karush-Kuhn-Tucker solution.Comment: 6 pages, 4 figures, IEEE ICC 201
Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications
The present work focuses on the forward link of a broadband multibeam
satellite system that aggressively reuses the user link frequency resources.
Two fundamental practical challenges, namely the need to frame multiple users
per transmission and the per-antenna transmit power limitations, are addressed.
To this end, the so-called frame-based precoding problem is optimally solved
using the principles of physical layer multicasting to multiple co-channel
groups under per-antenna constraints. In this context, a novel optimization
problem that aims at maximizing the system sum rate under individual power
constraints is proposed. Added to that, the formulation is further extended to
include availability constraints. As a result, the high gains of the sum rate
optimal design are traded off to satisfy the stringent availability
requirements of satellite systems. Moreover, the throughput maximization with a
granular spectral efficiency versus SINR function, is formulated and solved.
Finally, a multicast-aware user scheduling policy, based on the channel state
information, is developed. Thus, substantial multiuser diversity gains are
gleaned. Numerical results over a realistic simulation environment exhibit as
much as 30% gains over conventional systems, even for 7 users per frame,
without modifying the framing structure of legacy communication standards.Comment: Accepted for publication to the IEEE Transactions on Wireless
Communications, 201
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