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Singh A.,Eurecom | Elia P.,Eurecom | Jalden J.,Linnaeus Center
IEEE International Symposium on Information Theory - Proceedings | Year: 2013

Recent work in [1]-[3] quantified, in the form of a complexity exponent, the computational resources required for ML and lattice sphere decoding to achieve a certain diversity-multiplexing performance. For a specific family of layered lattice designs, and a specific set of decoding orderings, this complexity was shown to be an exponential function in the number of codeword bits, and was shown to meet a universal upper bound on complexity exponents. The same results raised the question of whether complexity reductions away from the universal upper bound are feasible, for example, with a proper choice of decoder (ML vs lattice), or with a proper choice of lattice codes and decoding ordering policies. The current work addresses this question by first showing that for almost any full-rate DMT optimal lattice code, there exists no decoding ordering policy that can reduce the complexity exponent of ML or lattice based sphere decoding away from the universal upper bound, i.e., that a randomly picked lattice code (randomly and uniformly drawn from an ensemble of DMT optimal lattice designs) will almost surely be such that no decoding ordering policy can provide exponential complexity reductions away from the universal upper bound. As a byproduct of this, the current work proves the fact that ML and (MMSE-preprocessed) lattice decoding share the same complexity exponent for a very broad setting, which now includes almost any DMT optimal code (again randomly drawn) and all decoding order policies. Under a basic richness of codes assumption, this is in fact further extended to hold, with probability one, over all full-rate codes. Under the same assumption, the result allows for a meaningful rate-reliability-complexity tradeoff that holds, almost surely in the random choice of the full-rate lattice design, and which holds irrespective of the decoding ordering policy. This tradeoff can be used to, for example, describe the optimal achievable diversity gain of ML or lattice sphere decoding in the presence of limited computational resources. © 2013 IEEE.

Hadjicostis C.N.,University of Cyprus | Hadjicostis C.N.,University of Illinois at Urbana - Champaign | Charalambous T.,Linnaeus Center
IEEE Transactions on Automatic Control | Year: 2014

Classical distributed algorithms for asymptotic average consensus typically assume timely and reliable exchange of information between neighboring components of a given multi-component system. These assumptions are not necessarily valid in practice due to varying delays that might affect computations at different nodes and/or transmissions at different links. In this work, we propose a protocol that overcomes this limitation and, unlike existing consensus protocols in the presence of delays, ensures asymptotic consensus to the exact average, despite the presence of arbitrary (but bounded) delays in the communication links. The protocol requires that each component has knowledge of the number of its out-neighbors (i.e., the number of components to which it can send information) and its proof of correctness relies on the weak convergence of a backward product of column stochastic matrices. The proposed algorithm is demonstrated via illustrative examples. © 2014 IEEE.

Shi G.,Linnaeus Center | Johansson K.H.,Linnaeus Center
Automatica | Year: 2012

In this paper, we formulate and solve a randomized optimal consensus problem for multi-agent systems with stochastically time-varying interconnection topology. The considered multi-agent system with a simple randomized iterating rule achieves an almost sure consensus meanwhile solving the optimization problem minz∈Rd∑i=1nfi(z), in which the optimal solution set of objective function fi can only be observed by agent i itself. At each time step, simply determined by a Bernoulli trial, each agent independently and randomly chooses either taking an average among its neighbor set, or projecting onto the optimal solution set of its own optimization component. Both directed and bidirectional communication graphs are studied. Connectivity conditions are proposed to guarantee an optimal consensus almost surely with proper convexity and intersection assumptions. The convergence analysis is carried out using convex analysis. We compare the randomized algorithm with the deterministic one via a numerical example. The results illustrate that a group of autonomous agents can reach an optimal opinion by each node simply making a randomized trade-off between following its neighbors or sticking to its own opinion at each time step. © 2012 Elsevier Ltd. All rights reserved.

Parisio A.,Linnaeus Center | Rikos E.,Center for Renewable Energy Sources, Greece | Glielmo L.,Universitadegli Studi Del Sannio
IEEE Transactions on Control Systems Technology | Year: 2014

Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach. © 1993-2012 IEEE.

Pacifici V.,Linnaeus Center | Dan G.,Linnaeus Center
IEEE Journal on Selected Areas in Communications | Year: 2012

As a model of distributed resource allocation in networked systems, we consider resource allocation games played over a influence graph. The influence graph models limited interaction between the players due to, e.g., the network topology: the payoff that an allocated resource yields to a player depends only on the resources allocated by her neighbors on the graph. We prove that pure strategy Nash equilibria (NE) always exist in graphical resource allocation games and we provide a linear time algorithm to compute equilibria. We show that these games do not admit a potential function: if there are closed paths in the influence graph then there can be best reply cycles. Nevertheless, we show that from any initial allocation of a resource allocation game it is possible to reach a NE by playing best replies and we provide a bound on the maximal number of update steps required. Furthermore we give sufficient conditions in terms of the influence graph topology and the utility structure under which best reply cycles do not exist. Finally we propose an efficient distributed algorithm to reach an equilibrium over an arbitrary graph and we illustrate its performance on different random graph topologies. © 2012 IEEE.

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