Abstract This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.

Human activity recognition (HAR) for boxing training typically requires specialized hardware or large labeled datasets to identify different types of punches and analyze their basic properties. However, measuring the unpredictability of boxers, a key characteristic to deceive opponents during combats, has remained an unexplored challenge. We tackled this challenge by combining a series of novel techniques and ideas. Unpredictability was computed as the entropy of the trajectories of the Markov chain characterizing the training session, estimated from the combinations (sequences of punches) thrown by the boxer to the training bag. Punch combinations were obtained by detecting punches as outliers in the raw acceleration sensor datastream coming from the bag, and separating them by type with an ensemble of PCA+GMM clusters. Punches were then divided into combinations using statistical analysis of separation intervals. The detection procedure works even with noisy data, in contrast to force-threshold methods commonly reported in the literature. As distinctive features of our system, it uses only unlabeled data from the current training session, and sensor positions and orientations inside the bag are unconstrained. However, punch clustering achieves up to 94% accuracy, comparable to state-of-the-art supervised approaches. These features are currently unseen in the literature and demonstrate the capacity for unsupervised learning techniques to address challenging problems in sports. Multiple validation experiments were consistent with boxers’ level and session performance. Overall, our approach provides significant contributions to the field of HAR and has the potential to improve the way boxing and other sports are trained and evaluated.

This paper focuses on interference coordination between the small cell and macro cell tiers of a wireless access network. We propose a new perspective based on a modelfree learning strategy, not requiring any previous knowledge about the network (e.g., topology, interference graph, scheduling algorithms). Our approach is based on a stochastic optimization algorithm known as Response Surface Methodology, that we use to learn the optimal parameter configuration during network operation (online learning) adapting to changes on network conditions (e.g., traffic, user positions). The result is a simple, effective and flexible mechanism that outperforms previous proposals. As a case study we apply our scheme to the dynamic adjustment of LTE-A eICIC parameters (CRE bias and ABS ratio).

This paper focuses on interference coordination between the small cell and macro cell tiers of a wireless access network. We present a self-optimization mechanism for LTE-A eICIC parameters (CRE bias and ABS ratio) following a novel approach based on a model-free learning strategy, not requiring any previous knowledge about the network (e.g., topology, interference graph, and scheduling algorithms). Our proposal is built upon a stochastic optimization algorithm known as response surface methodology (RSM), that we use to find efficient eICIC configurations during network operation (online learning), adapting to changing network conditions, such as traffic or user distribution. The objective consists of optimizing a performance metric for which, in general, mathematical expression is unavailable. In particular, we consider the fifth percentile throughput defined by the 3GPP. By means of RSM, our mechanism obtains local approximations of the objective function to perform steepest ascent iterations with an adjustable level of statistical accuracy. The algorithm can be extended to account for stochastic constraints, allowing the network to optimize one performance metric while maintaining other metrics above a desired level.

As cellular network technology evolves, the operators deploy new generation networks while maintaining their legacy networks, since not all users upgrade their terminals at the same pace. Therefore, the spectrum associated to these legacy networks becomes gradually underused. By means of cognitive radio techniques, the operator can allow its new generation terminals to reuse this spectrum. We propose a semi-decentralized scheme in which the operator guides the secondary access by broadcasting some operational parameters of the access strategy. The mechanism dynamically learns the optimal parameters by means of a response surface methodology (RSM), implying a very small signaling overhead. Our results show a notable capacity improvement compared to the classical approaches that exploit either spatial or temporal opportunities.

In Opportunistic Spectrum Access (OSA), conventional secondary users' (SU) radios can not transmit and detect primary user (PU) activity simultaneously, and therefore SU and PU transmissions may overlap, causing harmful interference at PU receivers. The extended approach of periodically interrupting SU transmission to perform spectrum sensing mitigates but does not completely solve the problem. Our proposal consists of a collision detection mechanism simultaneous to reception at the SU receiver (Background Detection), which aborts the ongoing secondary transmission if a collision is detected. While some previous works assumed the existence of a collision detection scheme, little work has been done in proposing a specific one for OSA systems, capable of operating during SU reception. Our proposal exploits local characterization of the PU activity, power levels and packet errors at each time slot. Based on these data, we develop a Maximum A Posteriori (MAP) estimator as the basis for the collision detection system. We describe the general methodology to build this estimator, and study the system's robustness against parameter misestimations. The proposed scheme can be incorporated to existing OSA protocols. Our results show a notable throughput improvement for the SUs while assuring a lower collision probability with PU transmissions in most cases, even under severe estimation inaccuracies.

This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a k-nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Católica San Antonio de Murcia volleyball team, which competes in the Spanish women's premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.

We consider a scenario in which a licensed wireless operator or primary network (PN) coexists with an ad-hoc, cognitive, secondary network. To minimize harmful interference on the primary users (PUs), secondary users (SUs) sense the PU activity on the licensed spectrum band before transmitting in spectrum opportunities. We study a bandwidth reservation (BR) scheme by which the PN keeps a set of adjacent channels free of PU transmissions. These reserved channels only accommodate PU traffic when all the non-reserved channels are used, and the SUs only occupy available channels within the reserved spectrum. Intuitively, this strategy reduces collision probability and simplifies the design of opportunistic spectrum access (OSA) mechanisms. However, from the point of view of the PN, BR entails a tradeoff between the benefits of an improved coexistence with SUs, and the capacity reduction associated to having fewer options for PU channel allocation. The main objective of this paper is to determine when BR improves the overall PN performance under SU activity. The SUs are characterized by a hardware limited radio, imperfect spectrum sensing, bayesian estimation of PU activity and multichannel access. Because PU capacity is the central issue, we assume a PN capable of exploiting all the available bandwidth at every moment. By means of a Markov-reward model, we compute the expected PU capacity with and without BR, considering propagation effects, interference and random locations. The results show that, in a non-congested PN with SU activity, the interference reduction capability of BR increases the overall capacity of the PN compared to not using BR.

This paper presents a novel system for effort control in team-sport training, where each component is associated with a particularized program. The system relies on a decision engine that analyzes training data and provides instructions to athletes, and on a sensing infrastructure which monitors athletes' biometrics and movements and gathers environmental data such as humidity or temperature. The prototype has been developed using IMOTE2 and MicaZ sensors under TinyOS and BioHarness biometric belts. The decision technique estimates the ratios of each effort regime, based on best-fit polynomial approximations using historical data from previous training sessions. The complete system results in a virtual coach, operating in real time and automatically. This methodology has been piloted in an experiment with the UCAM Volleyball Murcia team, top of the Spanish national women's volleyball league. Data obtained during training sessions have provided a knowledge base for the algorithms developed and allowed for validation of the results.

In dense radio frequency identification (RFID) systems, reader-to-reader interference may appear among readers operating nearby. When the anticollision algorithm is Frame Slotted Aloha (FSA), a usual solution is to multiplex the frames of interfering readers by means of a central controller. The scenario studied considers RFID tags attached to objects carried by conveyor belts or vehicles so that each tag crosses, in constant motion, one reader's interrogation region. The problem addressed consists of determining the length of the multiplexed FSA frames such that each object is successfully identified, with high probability, before exiting its interrogation region. In this paper, we develop a mathematical model, based on dynamic system theory, allowing us to obtain the configurations attaining the desired reliability objective, to show that some configurations present multiple equilibrium points and should be avoided, and to define the operating boundaries of the system. The model can also incorporate fading and capture effects.

This paper proposes a scheduling algorithm for non-real-time traffic in Vehicle to Infrastructure (V2I) networks. The scheduler operates at the link layer and uses the contention-free, poll-based access mode of the 802.11e standard. Our proposal exploits two specific features of V2I that are not present in other wireless systems. First, the fact that vehicles are constrained to a predefined area (the road segment under radio coverage) which can be characterized in terms of packet error ratio (PER). Second, that it is possible to know the vehicle speed, either accurately or approximately. These characteristics allow us to model the data transmission process in V2I as a dynamical system. However, the unpredictable arrivals of new vehicles make it difficult to directly apply dynamic programming to obtain the optimum scheduler. We overcome this limitation by developing a tractable formulation of the problem, similar to a classical linear quadratic regulator design problem but with the significant difference that constraints are included. We propose a novel algorithmic solution that can be efficiently computed and can easily incorporate quality of service differentiation. The scheduler is compared to three other feasible schemes, assessing the impact of the system parameters in the performance. Numerical results show that the proposed scheme is more beneficial when the average PER values have notable variations along the covered area.

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.