On the Feasibility of 5G Slice Resource Allocation With Spectral Efficiency: A Probabilistic Characterization

An important concern that 5G networks face is supporting a wide range of services and use cases with heterogeneous requirements. Radio access network (RAN) slices, understood as isolated virtual networks that share a common infrastructure, are a possible answer to this very demanding scenario and enable virtual operators to provide differentiated services over independent logical entities. This article addresses the feasibility of forming 5G slices, answering the question of whether the available capacity (resources) is sufficient to satisfy slice requirements. As spectral efficiency is one of the key metrics in 5G networks, we introduce the minislot-based slicing allocation (MISA) model, a novel 5G slice resource allocation approach that combines the utilization of both complete slots (or physical resource blocks) and mini-slots with the adequate physical layer design and service requirement constraints. We advocate for a probabilistic characterization that allows to estimate feasibility and characterize the behavior of the constraints, while an exhaustive search is very computationally demanding and the methods to check feasibility provide no information on the constraints. In such a characterization, the concept of phase transition allows for the identification of a clear frontier between the feasible and infeasible regions. Our method relies on an adaptation of the Wang-Landau algorithm to determine the existence of, at least, one solution to the problem. The conducted simulations show a significant improvement in spectral efficiency and feasibility of the MISA approach compared to the slot-based formulation, the identification of the phase transition, and valuable results to characterize the satisfiability of the constraints.The work of J. J. Escudero-Garzás was supported in part by the Spanish National Project TERESA-ADA (MINECO/AEI/FEDER, UE) under Grant TEC2017-90093-C3-2-R, and in part by the National Spectrum Consortium, USA, under Project NSC-16-0140

An energy-efficient adaptive modulation suitable for wireless sensor networks with SER and throughput constraints

We consider the problem of minimizing transmission energy in wireless sensor networks by taking into account that every sensor may require a different bit rate and reliability according to its particular application. We propose a cross-layer approach to tackle such a minimization in centralized networks for the total transmission energy consumption of the network: in the physical layer, for each sensor the sink estimates the channel gain and adaptively selects a modulation scheme; in the MAC layer, each sensor is correspondingly assigned a number of time slots. The modulation level and the number of allocated time slots for every sensor are constrained to attain their applications bit rates in a global energy-efficient manner. The signal-to-noise ratio gap approximation is used in our exposition in order to jointly handle required bit rates, transmission energies, and symbol error rates.This work has been partially funded by CRUISE NoE (IST-4-027738), MAMBO2 (CCG06-UC3M/TIC-0698) and MACAWI (TEC- 2005-07477-C02-02) projects.Publicad

Fairness-Adaptive Goodput-Based Resource Allocation in OFDMA Downlink with ARQ

We present a cross-layer resource-allocation (RA) scheme for the downlink in orthogonal frequency-division multiple-access (OFDMA) systems with fairness control among the users, where the resources to be allocated are power, bits per symbol, and subchannels. The use of subchannels, which are defined as group of subcarriers, leads to reducing the complexity of the bandwidth allocation compared with the commonly adopted subcarrier allocation. A goodput-based optimization function, which is derived by combining automatic repeat request (ARQ) and physical (PHY)-layer parameters, is used to perform RA for applications that demand error-free transmissions. Two transmission strategies are considered, with and without concatenation of subchannels, for which two different RA methods are developed, respectively. We also propose an algorithm that improves the complexity associated to both concatenation and nonconcatenation schemes, without appreciable performance loss.The work was supported by the GRE3N Project under Grant TEC2011-29006-C03-03.Publicad

Interference pricing mechanism for downlink multicell coordinated beamforming

We consider the downlink coordinated beamforming problem in a cellular network in which the base stations (BSs) are equipped with multiple antennas and each user is equipped with a single antenna. The BSs cooperate in sharing their local interference information, and they aim to maximize the sum-rate of the users in the network. A decentralized interference pricing beamforming (IPBF) algorithm is proposed to identify the coordinated beamformer, where a BS is penalized according to the interference it creates to its peers. We show that the decentralized pricing mechanism converges to an interference equilibrium, which is a KKT point of the sum-rate maximization problem. The proofs rely on the identification of rank-1 solutions of each BSs' interference-penalized rate maximization problem. Numerical results show that the proposed iterative mechanism reduces significantly the exchanged information with respect to other state-of-the-art beamforming algorithms with very little sum-rate loss. The version of the algorithm that limits the coordination to a cluster of base stations (IPBF-L) is shown to have very small sum-rate loss with respect to the full coordinated algorithm with much less backhaul information exchange.The work was partially supported by NSF grant CCF-1017982 and SICCNALS project (TEC2011-28219). The work of A. García was partially supported by NSF grant CCF-1017982. A. García-Armada’s work has been partially funded by research projects COMONSENS (CSD2008-00010) and GRE3N (TEC2011-29006-C03-02)Publicad

PAPR Reduction via Constellation Extension in OFDM Systems Using Generalized Benders Decomposition and Branch-and-Bound Techniques

In this paper, a novel constellation extension (CE)-based approach is presented to address the high peak-to-average power ratio (PAPR) problem at the transmitter side, which is an important drawback of orthogonal frequency-division multiplexing (OFDM) systems. This new proposal is formulated as a mixed-integer nonlinear programming optimization problem, which employs generalized Benders decomposition (GBD) and branch-and-bound (BB) methods to determine the most adequate extension factor and the optimum set of input symbols to be extended within a proper quarter plane of the constellation. The optimum technique based on GBD, which is denoted as GBD for constellation extension (GBDCE), provides a bound with relevant improvement in terms of PAPR reduction compared with other CE techniques, although it may exhibit slow convergence. To avoid excessive processing time in practical systems, the suboptimum BB for constellation extension (BBCE) scheme is proposed. Simulation results show that BBCE achieves a significant PAPR reduction, providing a good tradeoff between complexity and performance. We also show that the BBCE scheme performs satisfactorily in terms of power spectral density and bit error rate in the presence of a nonlinear high-power amplifier

On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem

Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here, we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework gives some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.This work was supported in part by the Universidad Carlos III de Madrid under Strategic Action 2013/00199/002

Fair design of plug-in electric vehicles aggregator for V2G regulation

Plug-in electric vehicles (PEVs) have recently attracted much attention due to their potential to reduce CO2 emissions and transportation costs and can be grouped into entities (aggregators) to provide ancillary services such as frequency regulation. In this paper, the application of aggregators to frequency regulation by making fair use of their energy storage capacity is addressed. When the power grid requires frequency regulation service to the aggregator to adjust the grid frequency, the PEVs participating in providing the service can either draw energy (as it is usually done to charge the vehicle) or deliver energy to the grid by means of the vehicle-to-grid (V2G) interface. Under the general framework of optimizing the aggregator profit, different methods, such as state-dependent allocation and the water-filling approach, are proposed to achieve a final state of charge (SOC) of the PEVs that satisfy the desired fairness criteria once the regulation service has been carried out.The work of J. J. Escudero-Garzás and G. Seco-Granados was supported by the Grant of Spanish Government TEC2011-28219. The work of A. García-Armada was supported by the GRE3N TEC2011-29006-C03-03 and COMONSENS CSD2008-00010 projects