596 research outputs found
Achieving Global Optimality for Weighted Sum-Rate Maximization in the K-User Gaussian Interference Channel with Multiple Antennas
Characterizing the global maximum of weighted sum-rate (WSR) for the K-user
Gaussian interference channel (GIC), with the interference treated as Gaussian
noise, is a key problem in wireless communication. However, due to the users'
mutual interference, this problem is in general non-convex and thus cannot be
solved directly by conventional convex optimization techniques. In this paper,
by jointly utilizing the monotonic optimization and rate profile techniques, we
develop a new framework to obtain the globally optimal power control and/or
beamforming solutions to the WSR maximization problems for the GICs with
single-antenna transmitters and single-antenna receivers (SISO), single-antenna
transmitters and multi-antenna receivers (SIMO), or multi-antenna transmitters
and single-antenna receivers (MISO). Different from prior work, this paper
proposes to maximize the WSR in the achievable rate region of the GIC directly
by exploiting the facts that the achievable rate region is a "normal" set and
the users' WSR is a "strictly increasing" function over the rate region.
Consequently, the WSR maximization is shown to be in the form of monotonic
optimization over a normal set and thus can be solved globally optimally by the
existing outer polyblock approximation algorithm. However, an essential step in
the algorithm hinges on how to efficiently characterize the intersection point
on the Pareto boundary of the achievable rate region with any prescribed "rate
profile" vector. This paper shows that such a problem can be transformed into a
sequence of signal-to-interference-plus-noise ratio (SINR) feasibility
problems, which can be solved efficiently by existing techniques. Numerical
results validate that the proposed algorithms can achieve the global WSR
maximum for the SISO, SIMO or MISO GIC.Comment: This is the longer version of a paper to appear in IEEE Transactions
on Wireless Communication
Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and l0-Approximation Methods
We study downlink of multiantenna cloud radio access networks (C-RANs) with
finite-capacity fronthaul links. The aim is to propose joint designs of
beamforming and remote radio head (RRH)-user association, subject to
constraints on users' quality-of-service, limited capacity of fronthaul links
and transmit power, to maximize the system energy efficiency. To cope with the
limited-capacity fronthaul we consider the problem of RRH-user association to
select a subset of users that can be served by each RRH. Moreover, different to
the conventional power consumption models, we take into account the dependence
of baseband signal processing power on the data rate, as well as the dynamics
of the efficiency of power amplifiers. The considered problem leads to a mixed
binary integer program (MBIP) which is difficult to solve. Our first
contribution is to derive a globally optimal solution for the considered
problem by customizing a discrete branch-reduce-and-bound (DBRB) approach.
Since the global optimization method requires a high computational effort, we
further propose two suboptimal solutions able to achieve the near optimal
performance but with much reduced complexity. To this end, we transform the
design problem into continuous (but inherently nonconvex) programs by two
approaches: penalty and \ell_{0}-approximation methods. These resulting
continuous nonconvex problems are then solved by the successive convex
approximation framework. Numerical results are provided to evaluate the
effectiveness of the proposed approaches.Comment: IEEE Transaction on Signal Processing, September 2018 (15 pages, 12
figures
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
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