3,384 research outputs found
Radio resource allocation for uplink OFDMA systems with finite symbol alphabet inputs
In this paper, we consider the radio resource allocation problem for uplink orthogonal frequency-division multiple-access (OFDMA) systems. The existing algorithms have been derived under the assumption of Gaussian inputs due to its closed-form expression of mutual information. For the sake of practicality, we consider the system with finite symbol alphabet (FSA) inputs and solve the problem by capitalizing on the recently revealed relationship between mutual information and minimum mean square error (MMSE). We first relax the problem to formulate it as a convex optimization problem, and then, we derive the optimal solution via decomposition methods. The optimal solution serves as an upper bound on the system performance. Due to the complexity of the optimal solution, a low-complexity suboptimal algorithm is proposed. Numerical results show that the presented suboptimal algorithm can achieve performance very close to the optimal solution and that it outperforms the existing suboptimal algorithms. Furthermore, using our proposed algorithm, significant power saving can be achieved in comparison to the case when a Gaussian input is assumed
Distributed Big-Data Optimization via Block Communications
We study distributed multi-agent large-scale optimization problems, wherein
the cost function is composed of a smooth possibly nonconvex sum-utility plus a
DC (Difference-of-Convex) regularizer. We consider the scenario where the
dimension of the optimization variables is so large that optimizing and/or
transmitting the entire set of variables could cause unaffordable computation
and communication overhead. To address this issue, we propose the first
distributed algorithm whereby agents optimize and communicate only a portion of
their local variables. The scheme hinges on successive convex approximation
(SCA) to handle the nonconvexity of the objective function, coupled with a
novel block-signal tracking scheme, aiming at locally estimating the average of
the agents' gradients. Asymptotic convergence to stationary solutions of the
nonconvex problem is established. Numerical results on a sparse regression
problem show the effectiveness of the proposed algorithm and the impact of the
block size on its practical convergence speed and communication cost
Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks
The performance of computer networks relies on how bandwidth is shared among
different flows. Fair resource allocation is a challenging problem particularly
when the flows evolve over time. To address this issue, bandwidth sharing
techniques that quickly react to the traffic fluctuations are of interest,
especially in large scale settings with hundreds of nodes and thousands of
flows. In this context, we propose a distributed algorithm based on the
Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path
fair resource allocation problem in a distributed SDN control architecture. Our
ADMM-based algorithm continuously generates a sequence of resource allocation
solutions converging to the fair allocation while always remaining feasible, a
property that standard primal-dual decomposition methods often lack. Thanks to
the distribution of all computer intensive operations, we demonstrate that we
can handle large instances at scale
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