Jose Lopez Vicario

Opportunistic relay selection with outdated CSI: outage probability and diversity analysis

In this paper, we analyze the outage probability and diversity order of opportunistic relay selection in a scenario based on decode and forward and where the available channel state information (CSI) is outdated. The study is conducted analytically by obtaining a closed-form expression for the outage probability, which is defined as the probability that the instantaneous capacity is below a target value. We derive high-SNR approximations for the outage probability. By doing so, we demonstrate that the diversity order of the system is reduced to 1 when CSI is outdated, being this behavior independent of the level of CSI accuracy. A physical explanation for this extreme loss of diversity is provided along with numerical results to support the analytical study.

Analytical assessment of multi-user vs. spatial diversity trade-offs with delayed channel state information

In this letter, we explore the combined use of spatial and multi-user diversity in a cellular system where channel state information is subject to delays in the feedback channel. First, we analytically derive the probability and cumulative density functions of the post-scheduling signal-to-noise ratio (SNR) for both a single-input single-output (SISO) and an orthogonal space-time block coding (OSTBC) transmission schemes. Then, we obtain the closed-form expressions of the corresponding average system capacities. By evaluating those expressions, we analytically show that the OSTBC scheme is far less sensitive to delays in the feedback channel

A reduced complexity approach to IAA beamforming for efficient DOA estimation of coherent sources

We address the 2D direction-of-arrival (DOA) estimation problem in scenarios with coherent sources. More specifically, we adopt beamforming solutions based on the iterative adaptive approach (IAA) recently proposed in the literature. The motivation of such adoption mainly comes from the excellent behavior these beamformers provide in scenarios with coherent sources. Nonetheless, these strategies suffer from a prohibitive computational complexity, especially in 2D scenarios. In order to alleviate the, we propose two reduced-complexity (RC) versions of the IAA and IAA based on maximum likelihood (IAA-ML) algorithms. The proposed beamformers are referred to as IAA-RC and IAA-ML-RC and provide similar results to those obtained with their original counterparts. Computational complexity, however, is further reduced. Numerical results presented in the paper show that the computational burden can be decreased by a 52% with our proposed solutions in the considered scenarios.

Label switching over IEEE802.15.4e networks

An open issue still to be addressed in low-power lossy networks (LLNs) is how the application requirements, the available transport services, the network layer routes, and the data link-layer resources are mapped efficiently. This can be explained by the fact that, in most LLNs, link-layer resources cannot be easily managed; this results in a best effort IP layer, and traffic engineering performed solely through flow control at the transport layer. The new IEEE802.15.4e standard defines a link-layer mechanism by which motes in the network synchronise and communicate by following a schedule. Each slot in that schedule can be seen as an atomic link-layer resource, which can be allocated to any arbitrary link in the network. The schedule can be built to match the bandwidth, latency and power requirements of each mote in the network. Managing that schedule is performed centrally in IEEE802.15.4e networks today. This paper explores a solution to achieve the same goal in a distributed manner. Specifically, we argue that this problem is very similar to traffic engineering on todays Internet. We show how multiprotocol label switching can be mapped to LLNs to manage the networks schedule. By using the completely fair distributed scheduler, we show by simulation how this novel link-layer resource allocation scheme yields a proper distribution of end-to-end delays among the motes and an average throughput that achieves the 70% of the maximum possible throughput in the worst conditions tested. Copyright

Beam Selection Strategies for Orthogonal Random Beamforming in Sparse Networks

Orthogonal random beamforming (ORB) constitutes a mean to exploit spatial multiplexing and multi-user diversity (MUD) gains in multi-antenna broadcast channels. To do so, as many random beamformers as transmit antennas (M) are generated and on each beam the user experiencing the most favorable channel conditions is scheduled. Whereas for a large number of users the sum-rate of ORB exhibits an identical growth rate as that of dirty paper coding, performance in sparse networks (or in networks with an uneven spatial distribution of users) is known to be severely impaired. To circumvent that, in this paper we modify the scheduling process in ORB in order to select a subset out of the M available beams. We propose several beam selection algorithms and assess their performance in terms of sum-rate and aggregated throughput (i.e., rate achieved with practical modulation and coding schemes), along with an analysis of their computational complexity. Since ORB schemes require partial channel state information (CSI) to be fed back to the transmitter, we finally investigate the impact of CSI quantization on system performance. More specifically, we prove that most of the MUD can be still exploited with very few quantization bits and we derive a beam selection approach trading-off system performance vs. feedback channel requirements.

A cross-layer approach to transmit antenna selection

In this paper, we investigate a cross-layer approach to transmit antenna selection capable of adapting the number of active antennas to varying channel conditions. We address a cross-layer methodology in the sense that the criterion for the selection of antenna subsets is the maximization of link layer throughput which takes into account characteristics both at the physical and link layers. In order to enhance system performance, adaptive modulation is included to jointly perform antenna selection and rate adaptation. Performance assessment is conducted in terms of link layer throughput and transmission delay

Enhanced Inertial-Aided Indoor Tracking System for Wireless Sensor Networks: A Review

In recent years, there has been a growing interest in localization algorithms for indoor environments. In this paper, we have developed an enhanced filtering method for indoor positioning and tracking applications using a wireless sensor network. The method combines position, speed, and heading measurements with the aim of achieving more accurate position estimates both in the short and the long term. Using as a base, the well-known extended Kalman filter, we have incorporated two novel measurement covariance matrix tuning methods. The power threshold covariance matrix tuning method and the distance statistics covariance matrix tuning method, both based on the statistical characteristics of the distance estimations. In addition, we take into account the inertial measurements obtained from a nine-degrees of freedom inertial measurement unit. The system has been validated in real scenarios and results show that it provides long-term accuracy, that is, the accuracy remains below 1 m during a 20-min test. In summary, our methods benefit from the reduced observation error of the inertial sensors in the short term and extend it over a long period of time.

Outage probability versus fairness trade-off in opportunistic relay selection with outdated CSI

We analyze the existing trade-offs in terms of system performance versus fairness of a cooperative system based on opportunistic relay selection (ORS) and with outdated channel state information (CSI). In particular, system performance is analytically evaluated in terms of outage probability, and the fairness behavior is assessed based on the power consumption at the different relays. In order to improve the fairness behavior of ORS while keeping the selection diversity gain, we propose a relay selection mechanism where the relay with the highest normalized signal-to-noise ratio (SNR) is selected for relaying the sources information. The proposed strategy is compared with existing relay selection strategies by adopting a novel graphical representation inspired by expected profit versus risk plots used in modern portfolio theory. As shown in the paper, this strategy allows operating the system in more favorable points of the outage versus fairness region.

A Throughput Analysis for Opportunistic Beamforming with Quantized Feedback

Opportunistic schemes employing multiple beams have recently attracted significant interest due to the capability to achieve both multiuser diversity and spatial multiplexing gain at limited feedback load. In this paper we explore the effect of the feedback quantization on the performance of these schemes. We derive a closed-form expression of the system throughput with and without feedback quantization. By doing so, we analytically assess the impact of the number of terminals and the restriction in the feedback bandwidth. It is shown that most of the throughput is preserved by using a few quantization bits

IoT-Cloud Service Optimization in Next Generation Smart Environments

The impact of the Internet of Things (IoT) on the evolution toward next generation smart environments (e.g., smart homes, buildings, and cities) will largely depend on the efficient integration of IoT and cloud computing technologies. With the predicted explosion in the number of connected devices and IoT services, current centralized cloud architectures, which tend to consolidate computing and storage resources into a few large data centers, will inevitably lead to excessive network load, end-to-end service latencies, and overall power consumption. Thanks to recent advances in network virtualization and programmability, highly distributed cloud networking architectures are a promising solution to efficiently host, manage, and optimize next generation IoT services in smart environments. In this paper, we mathematically formulate the service distribution problem (SDP) in IoT-Cloud networks, referred to as the IoT-CSDP, as a minimum cost mixed-cast flow problem that can be efficiently solved via linear programming. We focus on energy consumption as the major driver of todays network and cloud operational costs and characterize the heterogeneous set of IoT-Cloud network resources according to their associated sensing, computing, and transport capacity and energy efficiency. Our results show that, when properly optimized, the flexibility of IoT-Cloud networks can be efficiently exploited to deliver a wide range of IoT services in the context of next generation smart environments, while significantly reducing overall power consumption.