16 research outputs found
Joint power and admission control via p norm minimization deflation
In an interference network, joint power and admission control aims to support
a maximum number of links at their specified signal to interference plus noise
ratio (SINR) targets while using a minimum total transmission power. In our
previous work, we formulated the joint control problem as a sparse
-minimization problem and relaxed it to a -minimization
problem. In this work, we propose to approximate the -optimization
problem to a p norm minimization problem where , since intuitively p
norm will approximate 0 norm better than 1 norm. We first show that the
-minimization problem is strongly NP-hard and then derive a
reformulation of it such that the well developed interior-point algorithms can
be applied to solve it. The solution to the -minimization problem can
efficiently guide the link's removals (deflation). Numerical simulations show
the proposed heuristic outperforms the existing algorithms.Comment: 2013 IEEE International Conference on Acoustics, Speech, and Signal
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A Decentralized Method for Joint Admission Control and Beamforming in Coordinated Multicell Downlink
In cellular networks, admission control and beamforming optimization are
intertwined problems. While beamforming optimization aims at satisfying users'
quality-of-service (QoS) requirements or improving the QoS levels, admission
control looks at how a subset of users should be selected so that the
beamforming optimization problem can yield a reasonable solution in terms of
the QoS levels provided. However, in order to simplify the design, the two
problems are usually seen as separate problems. This paper considers joint
admission control and beamforming (JACoB) under a coordinated multicell MISO
downlink scenario. We formulate JACoB as a user number maximization problem,
where selected users are guaranteed to receive the QoS levels they requested.
The formulated problem is combinatorial and hard, and we derive a convex
approximation to the problem. A merit of our convex approximation formulation
is that it can be easily decomposed for per-base-station decentralized
optimization, namely, via block coordinate decent. The efficacy of the proposed
decentralized method is demonstrated by simulation results.Comment: 2012 IEEE Asilomar Conference on Signals, Systems, and Computer
Sample Approximation-Based Deflation Approaches for Chance SINR Constrained Joint Power and Admission Control
Consider the joint power and admission control (JPAC) problem for a
multi-user single-input single-output (SISO) interference channel. Most
existing works on JPAC assume the perfect instantaneous channel state
information (CSI). In this paper, we consider the JPAC problem with the
imperfect CSI, that is, we assume that only the channel distribution
information (CDI) is available. We formulate the JPAC problem into a chance
(probabilistic) constrained program, where each link's SINR outage probability
is enforced to be less than or equal to a specified tolerance. To circumvent
the computational difficulty of the chance SINR constraints, we propose to use
the sample (scenario) approximation scheme to convert them into finitely many
simple linear constraints. Furthermore, we reformulate the sample approximation
of the chance SINR constrained JPAC problem as a composite group sparse
minimization problem and then approximate it by a second-order cone program
(SOCP). The solution of the SOCP approximation can be used to check the
simultaneous supportability of all links in the network and to guide an
iterative link removal procedure (the deflation approach). We exploit the
special structure of the SOCP approximation and custom-design an efficient
algorithm for solving it. Finally, we illustrate the effectiveness and
efficiency of the proposed sample approximation-based deflation approaches by
simulations.Comment: The paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Semidefinite approximation for mixed binary quadratically constrained quadratic programs
Motivated by applications in wireless communications, this paper develops
semidefinite programming (SDP) relaxation techniques for some mixed binary
quadratically constrained quadratic programs (MBQCQP) and analyzes their
approximation performance. We consider both a minimization and a maximization
model of this problem. For the minimization model, the objective is to find a
minimum norm vector in -dimensional real or complex Euclidean space, such
that concave quadratic constraints and a cardinality constraint are
satisfied with both binary and continuous variables. {\color{blue}By employing
a special randomized rounding procedure, we show that the ratio between the
norm of the optimal solution of the minimization model and its SDP relaxation
is upper bounded by \cO(Q^2(M-Q+1)+M^2) in the real case and by
\cO(M(M-Q+1)) in the complex case.} For the maximization model, the goal is
to find a maximum norm vector subject to a set of quadratic constraints and a
cardinality constraint with both binary and continuous variables. We show that
in this case the approximation ratio is bounded from below by
\cO(\epsilon/\ln(M)) for both the real and the complex cases. Moreover, this
ratio is tight up to a constant factor
Learning to optimize: Training deep neural networks for wireless resource management
For decades, optimization has played a central role in addressing wireless resource management problems such as power control and beamformer design. However, these algorithms often require a considerable number of iterations for convergence, which poses challenges for real-time processing. In this work, we propose a new learning-based approach for wireless resource management. The key idea is to treat the input and output of a resource allocation algorithm as an unknown non-linear mapping and to use a deep neural network (DNN) to approximate it. If the non-linear mapping can be learned accurately and effectively by a DNN of moderate size, then such DNN can be used for resource allocation in almost real time, since passing the input through a DNN to get the output only requires a small number of simple operations. In this work, we first characterize a class of `learnable algorithms\u27 and then design DNNs to approximate some algorithms of interest in wireless communications. We use extensive numerical simulations to demonstrate the superior ability of DNNs for approximating two considerably complex algorithms that are designed for power allocation in wireless transmit signal design, while giving orders of magnitude speedup in computational time