789 research outputs found
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
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
Processin
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
Coordinated Multicasting with Opportunistic User Selection in Multicell Wireless Systems
Physical layer multicasting with opportunistic user selection (OUS) is
examined for multicell multi-antenna wireless systems. By adopting a two-layer
encoding scheme, a rate-adaptive channel code is applied in each fading block
to enable successful decoding by a chosen subset of users (which varies over
different blocks) and an application layer erasure code is employed across
multiple blocks to ensure that every user is able to recover the message after
decoding successfully in a sufficient number of blocks. The transmit signal and
code-rate in each block determine opportunistically the subset of users that
are able to successfully decode and can be chosen to maximize the long-term
multicast efficiency. The employment of OUS not only helps avoid
rate-limitations caused by the user with the worst channel, but also helps
coordinate interference among different cells and multicast groups. In this
work, efficient algorithms are proposed for the design of the transmit
covariance matrices, the physical layer code-rates, and the target user subsets
in each block. In the single group scenario, the system parameters are
determined by maximizing the group-rate, defined as the physical layer
code-rate times the fraction of users that can successfully decode in each
block. In the multi-group scenario, the system parameters are determined by
considering a group-rate balancing optimization problem, which is solved by a
successive convex approximation (SCA) approach. To further reduce the feedback
overhead, we also consider the case where only part of the users feed back
their channel vectors in each block and propose a design based on the balancing
of the expected group-rates. In addition to SCA, a sample average approximation
technique is also introduced to handle the probabilistic terms arising in this
problem. The effectiveness of the proposed schemes is demonstrated by computer
simulations.Comment: Accepted by IEEE Transactions on Signal Processin
Joint Downlink Beamforming and Discrete Resource Allocation Using Mixed-Integer Programming
Multi-antenna processing is widely adopted as one of the key enabling technologies for current and future cellular networks. Particularly, multiuser downlink beamforming (also known as space-division multiple access), in which multiple users are simultaneously served with spatial transmit beams in the same time and frequency resource, achieves high spectral efficiency with reduced energy consumption. To harvest the potential of multiuser downlink beamforming in practical systems, optimal beamformer design shall be carried out jointly with network resource allocation. Due to the specifications of cellular standards and/or implementation constraints, resource allocation in practice naturally necessitates discrete decision makings, e.g., base station (BS) association, user scheduling and admission control, adaptive modulation and coding, and codebook-based beamforming (precoding).
This dissertation focuses on the joint optimization of multiuser downlink beamforming and discrete resource allocation in modern cellular networks. The problems studied in this thesis involve both continuous and discrete decision variables and are thus formulated as mixed-integer programs (MIPs). A systematic MIP framework is developed to address the problems. The MIP framework consists of four components: (i) MIP formulations that support the commercial solver based approach for computing the optimal solutions, (ii) analytic comparisons of the MIP formulations, (iii) customizing techniques for speeding up the MIP solvers, and (iv) low-complexity heuristic algorithms for practical applications.
We consider first joint network topology optimization and multi-cell downlink beamforming (JNOB) for coordinated multi-point transmission. The objective is to minimize the overall power consumption of all BSs while guaranteeing the quality-of-service (QoS) requirements of the mobile stations (MSs). A standard mixed-integer second-order cone program (MISOCP) formulation and an extended MISOCP formulation are developed, both of which support the branch-and-cut (BnC) method. Analysis shows that the extended formulation admits tighter continuous relaxations (and hence less computational complexity) than that of the standard formulation. Effective strategies are proposed to customize the BnC method in the MIP solver CPLEX when applying it to the JNOB problem. Low-complexity inflation and deflation procedures are devised for large-scale applications. The simulations show that our design results in sparse network topologies and partial BS cooperation.
We study next the joint optimization of discrete rate adaptation and downlink beamforming (DRAB), in which rate adaptation is carried out via modulation and coding scheme (MCS) assignment and admission control is embedded in the MCS assignment procedure. The objective is to achieve the maximum sum-rate with the minimum transmitted BS power. As in the JNOB problem, a standard and an extended MISOCP formulations are developed, and analytic comparisons of the two formulations are carried out. The analysis also leads to efficient customizing strategies for the BnC method in CPLEX. We also develop fast inflation and deflation procedures for applications in large-scale networks. Our numerical results show that the heuristic algorithms yield sum-rates that are very close to the optimal ones.
We then turn our attention to codebook-based downlink beamforming. Codebook-based beamforming is employed in the latest cellular standards, e.g., in long-term evolution advanced (LTE-A), to simplify the signaling procedure of beamformers with reduced signaling overhead. We consider first the standard codebook-based downlink beamforming (SCBF) problem, in which precoding vector assignment and power allocation are jointly optimized. The objective is to minimize the total transmitted BS power while ensuring the prescribed QoS targets of the MSs. We introduce a virtual uplink (VUL) problem, which is proved to be equivalent to the SCBF problem. A customized power iteration method is developed to solve optimally the VUL problem and hence the SCBF problem. To improve the performance of codebook-based downlink beamforming, we propose a channel predistortion mechanism that does not introduce any additional signalling overhead or require modification of the mobile receivers. The joint codebook-based downlink beamforming and channel predistortion (CBCP) problem represents a non-convex MIP. An alternating optimization algorithm and an alternating feasibility search algorithm are devised to approximately solve the CBCP problem. The simulation results confirm the efficiency of the channel predistortion scheme, e.g., achieving significant reductions of the total transmitted BS power.
We study finally the worst-case robust codebook-based downlink beamforming when only estimated channel covariance matrices are available at the BS. Similar to the DRAB problem, user admission control is embedded in the precoding vector assignment procedure. In the robust codebook-based downlink beamforming and admission control (RCBA) problem, the objective is to achieve the maximum number of admitted MSs with the minimum transmitted BS power. We develop a conservative mixed-integer linear program (MILP) approximation and an exact MISOCP formulation of the RCBA problem. We further propose a low-complexity inflation procedure. Our simulations show that the three approaches yield almost the same average number of admitted MSs, while the MILP based approach requires much more transmitted BS power than the other two to support the admitted MSs.
The MIP framework developed in this thesis can be applied to address various discrete resource allocation problems in interference limited cellular networks. Both optimal solutions, i.e., performance benchmarks, and low-complexity practical algorithms are considered in our MIP framework. Conventional approaches often did not adopt the exact discrete models and approximated the discrete variables by (quantized) continuous ones, which could lead to highly suboptimal solutions or infeasible problem instances
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