The increasing demands of computationally intensive device applications are driving advancements in edge technologies and the need for improved computation offloading policies. This paper focuses on “adaptive” offloading and computation, i.e., adapting the amount of offload data and, consequently, the associated compute adaptive job's quality to the wireless channel quality, network congestion, and the compute adaptive job's computational options. For example, when the channel quality is poor and a job has a tight deadline, the amount of offloaded data can be reduced in exchange for a loss in the associated compute adaptive job's quality. In this paper, we show the substantial advantages of adapting the amount of offloaded data to channel quality and network congestion. We begin by defining a reward model for compute adaptive jobs based on the amount of offloaded data and resulting computation quality. We then develop an upper bound on the achievable revenue rate and propose/compare various offloading policies: Greedy, Predictive Abandonment (PA), Probabilistic Admission Control and Layer Assignment (PACLA), and combinations thereof. We evaluate our policies via simulation and observe that the combination of PACLA + PA provides the best offloading performance for homogeneous or heterogeneous compute adaptive jobs for devices with varied channel qualities.

Increasing demand for computationally intensive jobs on mobile devices is driving interest in computation offloading to the edge/cloud servers. This paper presents a comprehensive framework for managing offloading of stochastic and heterogeneous user(s)-generated jobs while considering job deadlines and congestion on wireless channels and edge/cloud servers. The goal of offloading is to maximize either the net computational work offloaded or power savings. We propose a class of policies called Predictive Abandonment (PA), where users opportunistically cut and offload jobs but abandon offloading if they predict that communication and computation delays will preclude on-time completion. Although these user-driven policies are desirable from an implementation perspective and achieve relatively good performance, they cannot coordinate tradeoffs amongst users with heterogeneous job types. To address this, we propose a complementary approach to coordinate offloading based on Probabilistic Admission Control and Cut Assignment (PACCA). When combined with PA, it delivers significant offloading benefits. We also develop an upper bound on the benefits of offloading, which can serve as a baseline for evaluating the additional gains of more complex offloading policies. We evaluate these policies via simulation for a range of loads and job profiles, demonstrating robust gains over a naive greedy offloading policy and near-optimal performance in some settings. Furthermore, we assess the robustness of PACCA + PA to imperfect knowledge of offered job rates.

The stochastic channel models typically abstract away the details of the paths that carry energy in the radio channel. While these have been universally acceptable for decades due to their ease of use and reasonable accuracy in most practical cases, the appearance of steerable, narrow-beam antennas in mmWave bands makes the exact path information very valuable, primarily for beam tracking algorithms. Currently, only deterministic channel modeling (e.g. ray tracing) provides the required level of details, but at prohibitive computing cost. This limits the study and design environments for such algorithms to the confines of existing ray tracing data, which is bulky and rarely available for free. In this paper, we consider an approach to stochastic channel modeling that allows to achieve the level of details equivalent to ray tracing, but at a fraction of the computing costs. The proposed approach may be immediately applied to any system operating at 20-100 GHz. It allows the researchers and engineers to perform quick testing of elaborate mmWave MAC and PHY algorithms with a system-level simulation, without having to obtain exhaustive measurement or ray tracing data.

BACKGROUND: Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another. RESULTS: We introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation. CONCLUSIONS: Modeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Unfortunately, the original version of this article [1] contained an error whereby the figures are completely out of order. The correct order of figures is as below. For example, Figure 1 should be the first figure, Figure 6 should be the second figure, Figure 2 should be the third figure and so forth as listed below. This has been corrected in the original article. Fig. 1 Fig. 6 Fig. 2 Fig. 3 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 Fig. 4 Fig. 5 Fig. 14

The emerging fifth-generation (5G) wireless technology will need to harness the massively unused millimeter-wave (mmWave) spectrum to meet the projected acceleration in mobile traffic demand. Today, the available range of mmWave-based solutions is already represented by IEEE 802.11ad (WiGig), IEEE 802.15.3c, WirelessHD, and ECMA-387 standards, with more to come in the following years. As the key performance-related aspects of these enabling technologies are rapidly taking shape, the primary research challenge shifts to characterizing network energy efficiency, among other system-level parameters. This is particularly important in scenarios that are not handled by current 4G communication networks, including congested public places, homes, and offices. In these dense deployments, wireless wearable devices are increasingly proliferating to assist in diverse user needs. However, mmWave operation in crowded environments, and especially for multiple neighboring personal networks, is not nearly well-understood. Bridging this gap, we conduct a full-fledged energy efficiency assessment of mmWave-based “high-end” wearables that employ advanced antenna beamforming techniques. Our rigorous analytical results shed light on the underlying scaling laws for the interacting mmWave-based networks based on IEEE 802.11ad and quantify the impact of beamforming quality on system energy efficiency under various conditions. Furthermore, we look at the system optimization potential subject to realistic hardware capabilities.

For the past years, the analysts have been predicting a tremendous and continuous increase in mobile traffic, causing much of industry and academia to seek out any and all methods to increase wireless network capacity. In this paper, we investigate one such method, cellular data offloading onto direct connections between proximate user devices, which has been shown to provide significant wireless capacity gains. To do so, we formulate a new system model that couples a cellular network in licensed bands and a device-to-device (D2D) network in unlicensed bands. We propose that devices be continually associated with the cellular base station and use this connectivity to help manage their direct connections in unlicensed spectrum. In particular, we demonstrate that assisted offloading of cellular user sessions onto the D2D links improves the degree of spatial reuse and reduces the impact of interference. In this study, a session is a real-time flow of data from one user to another, which adheres to a Poisson point process (PPP). By contrast to a throughput- or capacity-centric system view, the application of PPP enables formulations where entire user sessions, rather than singular data packets, are arriving at random and leaving the system after being served. The proposed methodology is flexible enough to accommodate practical offloading scenarios, network selection algorithms, quality of service measures, and advanced wireless technologies. In this study, we are primarily interested in evaluating the data session blocking probability in dynamically loaded cellular and D2D networks, but given the importance of energy efficiency for mobile devices, we are also interested in characterizing the energy expenditure of a typical data session in these different networks. First with our advanced analytical methodology and then with our detailed system-level simulator, we evaluate the performance of network-assisted data session offloading from cellular to D2D connections under a variety of conditions. This analysis represents a useful tool in the development of practical offloading schemes and ongoing standardization efforts.

In exploratory high-dimensional data analysis, local intrinsic dimension estimation can sometimes be used in order to discriminate between data sets sampled from different low-dimensional structures. Global intrinsic dimension estimators can in many cases be adapted to local estimation, but this leads to problems with high negative bias or high variance. We introduce a method that exploits the curse/blessing of dimensionality and produces local intrinsic dimension estimators that have very low bias, even in cases where the intrinsic dimension is higher than the number of data points, in combination with relatively low variance. We show that our estimators have a very good ability to classify local data sets by their dimension compared to other local intrinsic dimension estimators; furthermore we provide examples showing the usefulness of local intrinsic dimension estimation in general and our method in particular for stratification of real data sets.

Currently, cellular operators are struggling to relieve congestion on their networks in the face of rapidly growing mobile data traffic. While deploying an increasing number of base stations is expected to mitigate the disproportion between user demand and available radio resources, this solution is costly and plagued with many practical challenges. An attractive alternative is to enable cellular traffic offloading onto device-to-device (D2D) connections in the unlicensed bands, as current multi-radio user devices are already capable of establishing concurrent LTE and WiFi links. However, WiFi lacks a fast, efficient method of device/service discovery, and it is not equipped to efficiently manage numerous D2D connections. In our research, we have found that a limited amount of network assistance for D2D communications can overcome these limitations; and in this paper we describe our network-assisted D2D technology prototype. Specifically, we outline a complete standards-compliant solution that provides a seamless D2D connectivity experience to the end user. Our solution utilizes WiFi Direct as the link-layer technology for proximal D2D connections. However, the challenges faced during the design phase are universal to all D2D link-layer protocols, thus the proposed solutions are applicable to other potential D2D technologies.

Analysts predict explosive growth in traffic demand on mobile broadband systems over the coming years due to the popularity of streaming video, gaming, and other social media services. While 4G wireless technologies are making a significant effort to keep up with this demand, the expectation is that cellular deployments will fall short of the required capacity unless there is a dramatic shift towards smaller cells. There is already significant interest in femto- and pico-cell deployments for this reason. However, there is another method of creating small cells that the wireless industry has yet to capitalize on, namely direct connectivity between clients in close proximity. 3GPP is currently working to enable device-to-device (D2D) communications within Release 12 of LTE-Advanced. By comparison, IEEE has already defined a D2D communications protocol, termed WiFi Direct, which is based on the 802.11 standards. WiFi Direct not only serves to offload user data onto direct links, but does so using the unlicensed bands. To benefit users further, WiFi Direct can be enhanced by enabling the LTE network to assist during peer discovery and direct connection establishment. In this paper, we discuss the network/client requirements and performance benefits of network-assisted WiFi Direct. We assume that clients are continuously under management by the LTE network, which assists them with service/peer discovery and direct connection establishment. We show that network-assisted WiFi Direct can significantly improve the performance of proximal applications and reduce the power consumed by the clients involved, while also improving capacity of the LTE network.