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Author

Luis M. Lopez-Ramos

Other affiliations: King Juan Carlos University
Bio: Luis M. Lopez-Ramos is an academic researcher from University of Agder. The author has contributed to research in topics: Resource allocation & Cognitive radio. The author has an hindex of 7, co-authored 26 publications receiving 235 citations. Previous affiliations of Luis M. Lopez-Ramos include King Juan Carlos University.

Papers
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Journal ArticleDOI
TL;DR: Stochastic resource allocation algorithms for both interweave and underlay cognitive radio paradigms designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints are introduced.
Abstract: Efficient design of cognitive radios (CRs) calls for secondary users implementing adaptive resource allocation schemes that exploit knowledge of the channel state information (CSI), while at the same time limiting interference to the primary system. This paper introduces stochastic resource allocation algorithms for both interweave (also known as overlay) and underlay cognitive radio paradigms. The algorithms are designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints. The latter are formulated either as short- or as long-term constraints, and guarantee that the probability of secondary transmissions interfering with primary receivers stays below a certain pre-specified level. When the resultant optimization problem is non-convex, it exhibits zero-duality gap and thus, due to a favorable structure in the dual domain, it can be solved efficiently. The optimal schemes leverage CSI of the primary and secondary networks, as well as the Lagrange multipliers associated with the constraints. Analysis and simulated tests confirm the merits of the novel algorithms in: i) accommodating time-varying settings through stochastic approximation iterations; and ii) coping with imperfect CSI.

71 citations

Journal ArticleDOI
TL;DR: Stochastic convex optimization is relied on to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport, link, and physical layers and a simple mechanism is devised to effect delay priorities among users.
Abstract: Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel- and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users.

53 citations

Journal ArticleDOI
TL;DR: An interweave cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users is investigated, designed to minimize the cost of sensing, maximize the performance of the secondary users and limit the probability of interfering with the primary users.
Abstract: Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an interweave cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering with the primary users. The joint design is addressed using nonlinear optimization and dynamic programming, which is able to leverage the time correlation in the activity of the primary network. A two-step strategy is implemented: it first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy. The two-step strategy is optimal, gives rise to intuitive optimal policies, and entails a computational complexity much lower than that required to solve the original formulation.

25 citations

Journal ArticleDOI
26 Feb 2018-Energies
TL;DR: An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS) are proposed in this paper.
Abstract: Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels.

20 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency and sparse connectivity to exploit the sparse connectivity of causality graphs.
Abstract: An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.

13 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: A taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture is provided and the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy.
Abstract: For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.

200 citations

Journal ArticleDOI
TL;DR: In this article, a modified online saddle-point (MOSP) scheme is developed, and proved to simultaneously yield sublinear dynamic regret and fit, provided that the accumulated variations of per-slot minimizers and constraints are sublinearly growing with time.
Abstract: Existing approaches to online convex optimization make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best yet fixed overall solution in hindsight . The present paper deals with online convex optimization involving adversarial loss functions and adversarial constraints, where the constraints are revealed after making decisions, and can be tolerable to instantaneous violations but must be satisfied in the long term. Performance of an online algorithm in this setting is assessed by the difference of its losses relative to the best dynamic solution with one-slot-ahead information of the loss function and the constraint (that is here termed dynamic regret ); and the accumulated amount of constraint violations (that is here termed dynamic fit ). In this context, a modified online saddle-point (MOSP) scheme is developed, and proved to simultaneously yield sublinear dynamic regret and fit, provided that the accumulated variations of per-slot minimizers and constraints are sublinearly growing with time. MOSP is also applied to the dynamic network resource allocation task, and it is compared with the well-known stochastic dual gradient method. Numerical experiments demonstrate the performance gain of MOSP relative to the state of the art.

193 citations

Journal ArticleDOI
TL;DR: An overview on robust design for power control and beamforming in cognitive radio networks (CRNs) is given, modeling methods for parametric uncertainties are analyzed, various design methodologies are introduced, and robust algorithms that have appeared in the literatures are presented.
Abstract: Traditional spectrum allocation policies may result in temporarily unused radio spectrum. Cognitive radio (CR) has emerged as a promising technology to exploit the radio spectrum in a more efficient manner by allowing spectrum sharing between secondary users (SUs) and primary users (PUs). Power control and beamforming are two key techniques in CR design used to maximize the benefits of SUs, yet to maintain the quality of service of PUs. In practice, the available system parameters (e.g., channel state information and interference power) to enable power control and beamforming could be uncertain due to various factors such as estimation error and/or measurement error, thus the robustness of the designed algorithms should be considered in order to overcome the effects of parametric uncertainties. The objective of this paper is to give an overview on robust design for power control and beamforming in cognitive radio networks (CRNs). We will analyze modeling methods for parametric uncertainties, introduce various design methodologies, and present robust algorithms that have appeared in the literatures. Finally, some potential issues and future research directions in this field will be presented.

125 citations

Journal ArticleDOI
TL;DR: This work has investigated the cooperative relaying technique in the context of the overlay spectrum access scheme aiming for allowing the PUs to transmit at a lower power and/or at a higher throughput, while at the same time enabling the CUs to communicate using the bandwidth released.
Abstract: In order to mitigate the shortage of wireless spectrum, the appealing concepts of cooperative communication techniques and cognitive radio (CR) networks have been combined for the sake of improving the spectral efficiency and hence the overall system throughput. We mainly survey the overlay spectrum access scheme in this novel cooperative CR (CCR) network context. Therefore, the interference between the licensed users/primary users (PUs) and the unlicensed users/cognitive users (CUs) can be offset by relying on some of the CUs to act as relay nodes. More specifically, we have investigated the cooperative relaying technique in the context of the overlay spectrum access scheme aiming for allowing the PUs to transmit at a lower power and/or at a higher throughput, while at the same time enabling the CUs to communicate using the bandwidth released. Additionally, gaming techniques can be employed for negotiating between the PUs and the CUs for determining the specific fraction of relaying and active transmission time. Therefore, we will consider two main schemes in the overlay spectrum access scheme based on the CCR network, which are the frequency division-based channel as well as the time-division based channel. Moreover, we have surveyed the relevant advances concerning the game-based model of the overlay-based CR network. Specifically, both the family of non-cooperative and cooperative games as well as matching games have been reviewed. Furthermore, we will review the joint design of coding, modulation, user-cooperation, and CCR techniques, which leads to significant mutual benefits for both the PUs and CUs.

114 citations