Content Caching and Delivery over Heterogeneous Wireless Networks

Emerging heterogeneous wireless architectures consist of a dense deployment of local-coverage wireless access points (APs) with high data rates, along with sparsely-distributed, large-coverage macro-cell base stations (BS). We design a coded caching-and-delivery scheme for such architectures that equips APs with storage, enabling content pre-fetching prior to knowing user demands. Users requesting content are served by connecting to local APs with cached content, as well as by listening to a BS broadcast transmission. For any given content popularity profile, the goal is to design the caching-and-delivery scheme so as to optimally trade off the transmission cost at the BS against the storage cost at the APs and the user cost of connecting to multiple APs. We design a coded caching scheme for non-uniform content popularity that dynamically allocates user access to APs based on requested content. We demonstrate the approximate optimality of our scheme with respect to information-theoretic bounds. We numerically evaluate it on a YouTube dataset and quantify the trade-off between transmission rate, storage, and access cost. Our numerical results also suggest the intriguing possibility that, to gain most of the benefits of coded caching, it suffices to divide the content into a small number of popularity classes.Comment: A shorter version is to appear in IEEE INFOCOM 201

Learning to Generate Image Embeddings with User-level Differential Privacy

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of $\epsilon<2$ while controlling the utility drop within 5%, when millions of users can participate in training

Parallels in Communication and Navigation Technology and Natural Phenomenon

The premise is more than art imitates life, or technology imitates nature it is a nascent step to see how we might be unwittingly inspired and influenced. An example that might immediately come to mind is a starling murmuration (a phenomenon called scale-free correlation) and Intels recent Coachella music festival drone performance. Superconductivity is a macroscopic manifestation of a quantum phenomenon - choreographed electrons (i.e. an electron murmuration) that enable astonishing devices. There is indeed an intimate connectedness between biology and electromagnetism. Our brains are complex neural circuits generating magnetic fields with a magnitude around 100 femtoTesla (roughly one billion times weaker than a typical magnet used to tack notes to a refrigerator door). Migratory birds navigate by orienteering with respect to the Earth's magnetic field. Electromagnetic field therapy is used in orthopedics to aid in bone repair. The electric eel generates a large electric field for self-defense. Sharks apparently detect extremely weak electric fields for finding prey. And so on. There are similarities between the way a field of wheat responds to a breeze and the natural restoring forces of a semiconductor crystal. And waves in a slowly moving river can lap backwards against a peninsular shoreline mimicking a diffraction effect. Getting back to the introductory sentence and mysterious links over cosmic distances, in August 2016, China launched the Quantum Experiments at Space Scale (QUESS) satellite. The technology is based on a non-linear crystal that produces pairs of entangled photons whose attributes apparently remain entwined regardless of how far apart they are separated. This paper will, no doubt superficially, attempt to enumerate and examine these types of connections and parallelisms

Model Predictive Control Design for f Nonlinear Four-Tank System

In recent years, MPC has become a prominent advanced control technique, especially in large industrial processes. However, for enormous complexity, non-convexity and computational reasons, MPC practice and applications have been restricted to linear plants. During the last decade, many formulations have been developed for MPC formulation of linear and nonlinear, stable and unstable plants but still there remain some unsolved issues which depend on plant specifications. Instability, unfeasibility, non-convexity and lack of robustness are examples of unsolved issues. In this thesis a control system has been designed for a highly nonlinear and non minimum phase Four-Tank system. Then constrained optimization is employed in the MPC formulation to repair violation on boundaries. It also leads the system to work with the best performance. Additionally, the influence of most effective tuning parameters in MPC strategy has been investigated. In particular main part of the thesis has focused on performance criteria based on good reference tracking in model predictive control domain. Regarding to investigate the performance of this algorithm and due to application of “nonlinear Four-Tank system” in control theory and industry, this system is considered as a plant to be examined under this method. The most attractive aspect of this system is; the time-varying movement of a right half plane transmission zeros across the imaginary axis. This system’s configuration makes the process difficult to control under the previous controllers. This problem appears to be one of the most important and practical designs of nonlinear system in process control. In this thesis, besides good performance, the algorithm enjoys from relative simplicity and faster response in compare with the algorithms developed in other previous works. The problems of complexity of algorithm, non-convexity of the optimization, especially when working with nonlinear plants are the most common problems in the control design criteria. Since linear model predictive control is used instead of nonlinear model predictive control; these problems are avoided to be appeared in this work. All the results in this study show fast performance in controlling the Four-Tank system. Both of the weighting matrices are considered so that a system is fast enough smooth control signals and they are tuned till the desired performance is achieved.. Low value of prediction horizon and weighting matrices are more preferable to reduce number of free variable and avoid complexity of analysis

Spatial Throughput of Mobile Ad Hoc Networks Powered by Energy Harvesting

Designing mobiles to harvest ambient energy such as kinetic activities or electromagnetic radiation will enable wireless networks to be self sustaining besides alleviating global warming. In this paper, the spatial throughput of a mobile ad hoc network powered by energy harvesting is analyzed using a stochastic-geometry model. In this model, transmitters are distributed as a Poisson point process and energy arrives at each transmitter randomly with a uniform average rate called the energy arrival rate; upon harvesting sufficient energy, each transmitter transmits with fixed power to an intended receiver under an outage-probability constraint for a target signal-to-interference-and-noise ratio. It is assumed that transmitters store energy in batteries with infinite capacity. By applying the random-walk theory, the probability that a transmitter transmits, called the transmission probability, is proved to be equal to one if the energy-arrival rate exceeds transmission power or otherwise is equal to their ratio. This result and tools from stochastic geometry are applied to maximize the network throughput for a given energy-arrival rate by optimizing transmission power. The maximum network throughput is shown to be proportional to the optimal transmission probability, which is equal to one if the transmitter density is below a derived function of the energy-arrival rate or otherwise is smaller than one and solves a given polynomial equation. Last, the limits of the maximum network throughput are obtained for the extreme cases of high energy-arrival rates and dense networks.Comment: This paper has been presented in part at Asilomar Conf. on Signals, Systems, and Computers 2011 and at IEEE Intl. Conf. on Communications (ICC) 2013. The full version will appear in IEEE Transactions on Information Theor