68,143 research outputs found
Improved Convergence Rates for Distributed Resource Allocation
In this paper, we develop a class of decentralized algorithms for solving a
convex resource allocation problem in a network of agents, where the agent
objectives are decoupled while the resource constraints are coupled. The agents
communicate over a connected undirected graph, and they want to collaboratively
determine a solution to the overall network problem, while each agent only
communicates with its neighbors. We first study the connection between the
decentralized resource allocation problem and the decentralized consensus
optimization problem. Then, using a class of algorithms for solving consensus
optimization problems, we propose a novel class of decentralized schemes for
solving resource allocation problems in a distributed manner. Specifically, we
first propose an algorithm for solving the resource allocation problem with an
convergence rate guarantee when the agents' objective functions are
generally convex (could be nondifferentiable) and per agent local convex
constraints are allowed; We then propose a gradient-based algorithm for solving
the resource allocation problem when per agent local constraints are absent and
show that such scheme can achieve geometric rate when the objective functions
are strongly convex and have Lipschitz continuous gradients. We have also
provided scalability/network dependency analysis. Based on these two
algorithms, we have further proposed a gradient projection-based algorithm
which can handle smooth objective and simple constraints more efficiently.
Numerical experiments demonstrates the viability and performance of all the
proposed algorithms
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks
We consider the problem of rate allocation among multiple simultaneous video
streams sharing multiple heterogeneous access networks. We develop and evaluate
an analytical framework for optimal rate allocation based on observed available
bit rate (ABR) and round-trip time (RTT) over each access network and video
distortion-rate (DR) characteristics. The rate allocation is formulated as a
convex optimization problem that minimizes the total expected distortion of all
video streams. We present a distributed approximation of its solution and
compare its performance against H-infinity optimal control and two heuristic
schemes based on TCP-style additive-increase-multiplicative decrease (AIMD)
principles. The various rate allocation schemes are evaluated in simulations of
multiple high-definition (HD) video streams sharing multiple access networks.
Our results demonstrate that, in comparison with heuristic AIMD-based schemes,
both media-aware allocation and H-infinity optimal control benefit from
proactive congestion avoidance and reduce the average packet loss rate from 45%
to below 2%. Improvement in average received video quality ranges between 1.5
to 10.7 dB in PSNR for various background traffic loads and video playout
deadlines. Media-aware allocation further exploits its knowledge of the video
DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure
On Resource Allocation in Fading Multiple Access Channels - An Efficient Approximate Projection Approach
We consider the problem of rate and power allocation in a multiple-access
channel. Our objective is to obtain rate and power allocation policies that
maximize a general concave utility function of average transmission rates on
the information theoretic capacity region of the multiple-access channel. Our
policies does not require queue-length information. We consider several
different scenarios. First, we address the utility maximization problem in a
nonfading channel to obtain the optimal operating rates, and present an
iterative gradient projection algorithm that uses approximate projection. By
exploiting the polymatroid structure of the capacity region, we show that the
approximate projection can be implemented in time polynomial in the number of
users. Second, we consider resource allocation in a fading channel. Optimal
rate and power allocation policies are presented for the case that power
control is possible and channel statistics are available. For the case that
transmission power is fixed and channel statistics are unknown, we propose a
greedy rate allocation policy and provide bounds on the performance difference
of this policy and the optimal policy in terms of channel variations and
structure of the utility function. We present numerical results that
demonstrate superior convergence rate performance for the greedy policy
compared to queue-length based policies. In order to reduce the computational
complexity of the greedy policy, we present approximate rate allocation
policies which track the greedy policy within a certain neighborhood that is
characterized in terms of the speed of fading.Comment: 32 pages, Submitted to IEEE Trans. on Information Theor
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
In this paper we deal with a network of agents seeking to solve in a
distributed way Mixed-Integer Linear Programs (MILPs) with a coupling
constraint (modeling a limited shared resource) and local constraints. MILPs
are NP-hard problems and several challenges arise in a distributed framework,
so that looking for suboptimal solutions is of interest. To achieve this goal,
the presence of a linear coupling calls for tailored decomposition approaches.
We propose a fully distributed algorithm based on a primal decomposition
approach and a suitable tightening of the coupling constraints. Agents
repeatedly update local allocation vectors, which converge to an optimal
resource allocation of an approximate version of the original problem. Based on
such allocation vectors, agents are able to (locally) compute a mixed-integer
solution, which is guaranteed to be feasible after a sufficiently large time.
Asymptotic and finite-time suboptimality bounds are established for the
computed solution. Numerical simulations highlight the efficacy of the proposed
methodology.Comment: 57th IEEE Conference on Decision and Contro
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