Data Mining - Concepts and Techniques.

The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.

Ultra-Dense Networks: A Survey

We study resource allocation for energy-efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs). We formulate the resource allocation problem for joint BS zero-forcing beamforming (ZFBF) transmission as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a low-complexity suboptimal semi-orthogonal user selection policy is computed. Besides, by using the concept of perturbation function, we show that in the considered systems under some general conditions, the duality gap with respect to the power optimization variables is zero despite the non-convexity of the primal problem. Thus, dual decomposition can be used in each iteration to derive an efficient closed-form power allocation solution for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency, network capacity, and backhaul capacity: (1) In the low transmit power regime, an algorithm which achieves the maximum spectral efficiency may also achieve the maximum energy efficiency; (2) a high spectral efficiency does not necessarily result in a high energy efficiency; (3) spectral efficiency is always limited by the backhaul capacity; (4) energy efficiency increases with the backhaul capacity only until the maximum energy efficiency is achieved.

Energy-Efficient Resource Allocation in Multi-Cell OFDMA Systems with Limited Backhaul Capacity

Android Open Source Projectを対象としたパッチレビュー活動の調査

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity

Existing base station (BS) assignment methods in cellular networks are mainly driven by radio criteria since it is assumed that the only limiting resource factor is on the air interface. However, as enhanced air interfaces have been deployed, and mobile data and multimedia traffic increases, a growing concern is that the backhaul of the cellular network can become the bottleneck in certain deployment scenarios. In this paper, we extend the BS assignment problem to cope with possible backhaul congestion situations. A backhaul-aware BS assignment problem is modeled as an optimization problem using a utility-based framework, imposing constraints on both radio and backhaul resources, and mapped into a Multiple-Choice Multidimensional Knapsack Problem (MMKP). A novel heuristic BS assignment algorithm with polynomial time is formulated, evaluated and compared to classical schemes based exclusively on radio conditions. Simulation results demonstrate that the proposed algorithm can provide the same system capacity with less backhaul resources so that, under backhaul bottleneck situations, a better overall network performance is effectively achieved.

Design and Evaluation of a Backhaul-Aware Base Station Assignment Algorithm for OFDMA-Based Cellular Networks

Power management techniques in smartphone-based mobility sensing systems: A survey

Mobile backhaul has moved to the forefront of wireless industry hot topics. The progressive deployment of spectrally efficient radio access technologies (e.g. HSPA, LTE, WiMAX) in mobile broadband is turning into stringent capacity requirements on the backhaul network. This makes backhaul capacity to become a potential network bottleneck in some deployment scenarios. While more cost-efficient transmission technologies are needed to increase the capacity of mobile backhaul, optimization solutions to get the most out of the backhaul capacity are also necessary. This paper analyses the possibility to exploit load balancing among base stations to improve backhaul capacity utilization. Load balancing is realized through cell selection algorithms accounting for both radio interface and backhaul conditions. An analytical model aimed at evaluating the performance of cell selection strategies for mobile networks with backhaul capacity constraints is developed. The analytical model is used to evaluate the performance of a novel backhaul-aware cell selection algorithm and compare it with classical schemes based exclusively on radio information. Obtained results show that the proposed algorithm can achieve a utilization of backhaul resources higher than the traditional cell selection schemes while providing the same radio interface performance. Analytical results have been verified by means of simulation.

Analytical Modeling and Performance Evaluation of Cell Selection Algorithms for Mobile Networks with Backhaul Capacity Constraints

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone's battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications.

The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem’s features belong to a numerical space. However, many problems often include data where numerical and categorical data coexist, which represents a challenge to manage them. To transform categorical data into a numeric form, preprocessing tasks are compulsory. Methods such as one-hot and feature-hashing have been the most widely used encoding approaches at the expense of a significant increase in the dimensionality of the dataset. This effect introduces unexpected challenges to deal with the overabundance of variables and/or noisy data. In this regard, in this paper we propose a novel encoding approach that maps mixed-type data into an information space using Shannon’s Theory to model the amount of information contained in the original data. We evaluated our proposal with ten mixed-type datasets from the UCI repository and two datasets representing real-world problems obtaining promising results. For demonstrating the performance of our proposal, this was applied for preparing these datasets for classification, regression, and clustering tasks. We demonstrate that our encoding proposal is remarkably superior to one-hot and feature-hashing encoding in terms of memory efficiency. Our proposal can preserve the information conveyed by the original data.

https://www.mdpi.com/1099-4300/22/12/1391/pdf

Hiram Galeana-Zapién

Papers

Design and Evaluation of a Backhaul-Aware Base Station Assignment Algorithm for OFDMA-Based Cellular Networks

Power management techniques in smartphone-based mobility sensing systems: A survey

Analytical Modeling and Performance Evaluation of Cell Selection Algorithms for Mobile Networks with Backhaul Capacity Constraints

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications.

A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning.