Binary-Tree Encoding for Uniform Binary Sources in Index Modulation Systems

The problem of designing bit-to-pattern mappings and power allocation schemes for orthogonal frequency-division multiplexing (OFDM) systems that employ subcarrier index modulation (IM) is considered. We assume the binary source conveys a stream of independent, uniformly distributed bits to the pattern mapper, which introduces a constraint on the pattern transmission probability distribution that can be quantified using a binary tree formalism. Under this constraint, we undertake the task of maximizing the achievable rate subject to the availability of channel knowledge at the transmitter. The optimization variables are the pattern probability distribution (i.e., the bit-to-pattern mapping) and the transmit powers allocated to active subcarriers. To solve the problem, we first consider the relaxed problem where pattern probabilities are allowed to take any values in the interval [0,1] subject to a sum probability constraint. We develop (approximately) optimal solutions to the relaxed problem by using new bounds and asymptotic results, and then use a novel heuristic algorithm to project the relaxed solution onto a point in the feasible set of the constrained problem. Numerical analysis shows that this approach is capable of achieving the maximum mutual information for the relaxed problem in low and high-SNR regimes and offers noticeable benefits in terms of achievable rate relative to a conventional OFDM-IM benchmark.Comment: 18 pages, 16 figures, 2 table

DISTRIBUTION NETWORK OPERATION WITH SOLAR PHOTOVOLTAIC AND ENERGY STORAGE TECHNOLOGY

Among distributed energy resources, solar photovoltaic (PV) generation has the largest penetration in the distribution networks. Serving electric vehicles (EV) with renewable resource generation would further reduce the carbon footprint of the energy supply chain for electric vehicles. However, the integration of solar PV and EVs in the unbalanced distribution network introduces several challenges including voltage fluctuations, voltage imbalances, reverse power flow, and protection devices’ malfunctions. The uncertainties associated with solar PV integration and electric vehicles operation require significant effort to develop accurate optimization methodologies in the unbalanced distribution systems operation. In this thesis, in order to cope with the uncertainties, we first developed a two-stage optimization problem, to identify the feasible dispatch margins of photovoltaic generation considering the distribution network operation constraints. The dispatch margins of photovoltaic generation are quantified considering the worst-case realization of demand in the distribution network. The linear and the second-order cone mathematical problem formulation is procured to solve the optimal power flow problem. Second, a data-driven distributionally robust optimization framework is proposed for the operation of the unbalanced distribution network considering the uncertainties associated with the interconnected EV fleets and solar PV generation, and the proposed framework leverages the column-and-constraint generation approach. Moreover, to minimize the operation cost and improve the ramping flexibility, a continuous-time optimization problem, is developed and reformulated to a linear programming problem using Bernstein polynomials. Here, a generalized exact linear reformulation of the data-driven distributionally robust optimization is used to capture the worst-case probability distribution of the net demand uncertainties. Furthermore, in this thesis, an interconnection of multi microgrids (MGs) technology is considered a promising solution to handle the variability of the distributed renewable energy resources and improve the energy resilience in the distribution network. The coordination among the microgrids in the distribution network could improve the operation cost, reliability, and security of the distribution network. Therefore, an adaptive robust distributed optimization framework is developed for the operation of a distribution network with interconnected microgrids considering the uncertainties in demand and solar PV generation

Distributed Control with Low-Rank Coordination

A common approach to distributed control design is to impose sparsity constraints on the controller structure. Such constraints, however, may greatly complicate the control design procedure. This paper puts forward an alternative structure, which is not sparse yet might nevertheless be well suited for distributed control purposes. The structure appears as the optimal solution to a class of coordination problems arising in multi-agent applications. The controller comprises a diagonal (decentralized) part, complemented by a rank-one coordination term. Although this term relies on information about all subsystems, its implementation only requires a simple averaging operation

Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.Comment: In Press, available online, August 1, 2014. http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal Processing, 201

Distributive Network Utility Maximization (NUM) over Time-Varying Fading Channels

Distributed network utility maximization (NUM) has received an increasing intensity of interest over the past few years. Distributed solutions (e.g., the primal-dual gradient method) have been intensively investigated under fading channels. As such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the convergence behavior and tracking errors of the iterative primal-dual scaled gradient algorithm (PDSGA) with dynamic scaling matrices (DSC) for solving distributive NUM problems under time-varying fading channels. We shall also study a specific application example, namely the multi-commodity flow control and multi-carrier power allocation problem in multi-hop ad hoc networks. Our analysis shows that the PDSGA converges to a limit region rather than a single point under the finite state Markov chain (FSMC) fading channels. We also show that the order of growth of the tracking errors is given by O(T/N), where T and N are the update interval and the average sojourn time of the FSMC, respectively. Based on this analysis, we derive a low complexity distributive adaptation algorithm for determining the adaptive scaling matrices, which can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed dynamic scaling matrix algorithm over several baseline schemes, such as the regular primal-dual gradient algorithm