We propose a cross-layer strategy for resource allocation between spatially correlated sources in the uplink of multi-cell FDMA networks. Our objective is to find the optimum power and channel allocation to the different sources, in order to minimize the maximum distortion achieved in decoding any source data in the network. This problem is NP-hard and finding the optimal solution is not computationally feasible. We propose a three-step algorithm to be performed separately in each cell, which finds cross-layer resource allocation in simple steps. This method separates the problem into inter-cell resource management, grouping of sources for joint decoding, and intracell channel assignment. For each of these steps we propose methods that satisfy different design constraints and analyze them by simulations. We show that, while using correlation in compression and joint decoding can achieve 25% distortion reduction over independent decoding, the improvement grows to 37% when correlation is also utilized in resource allocation. This significant distortion reduction motivates further work in correlation-aware resource allocation. Overall, our solution is able to achieve a 60% decrease in 5 percentile distortion compared to independent allocation methods.

Abstract-In this work we propose a novel inter-cell interference coordination (ICIC) and resource allocation method. Our aim is to maximize the rate of the worst performing user in all cells. We solve the problem in two phases, inter-cell and intracell resource management: first we define an ICIC scheme called interference concentration (ICon) in order to manage resources across cells, then each cell independently performs resource allocation to its users while meeting the constraints imposed by ICon. Finally, we adapt the ICIC method in order to balance the performance achieved in neighboring cells. Differently than Soft Frequency Reuse (SFR), ICon assigns received interference power limits, or the Interference Power Profiles (IPPs) rather than transmit power limits. The IPP determines the interference level the cell tolerates on each band. The intuition behind this change is that the interference to a given cell can be concentrated on a small band, resulting in more efficient use of bandwidth. In the intracell resource management phase, each cell allocates power and sub-bands to its users given their location in the cell, maximizing the minimum rate such that the IPP of none of its neighboring cells is violated. In order to balance the performance across all cells we use gradient-like updates to IPPs of cells. Finally we simulate an LTE-like system and compare the performance of our method with reuse 1, static FFR and SFR with proportionally fair scheduling of users in each cell. Static ICon achieves 18% higher 5 percentile rate than reuse 1 which was the best of these methods. Adaptive ICon is found to converge almost immediately, and adds an additional 11% to this gain.

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In this work we study adaptive resource allocation for uplink transmission of correlated video sources. We consider a framework where multiple wireless sources transmit correlated information via a common base station. We introduce an optimization problem for sources to maximize the weighted sum of received quality of all videos, when source correlation can be used at the decoder in case of missing data. Each source finds its respective Multiple Access (MAC) parameters and performs packet selection. This is done with minimal information exchange with the base station. We model the quality of a decoded video as a piecewise linear function of qualities of the most correlated views, and verify the validity of the piecewise linear quality model for a two source case. We use this model to simplify the resource allocation method. We then compare performance of our correlated resource allocation to optimal resource allocation for independent sources, as well as to a baseline method. The simulations show that our proposed method results in higher average Y-PSNR than the optimal independent resource allocation in most channel conditions, without significative complexity increase in the base station or the source nodes.

Di erent users can use a given Internet application in many different ways. The ability to record detailed event logs of user inapplication activity allows us to discover ways in which the application is being used. This enables personalization and also leads to important insights with actionable business and product outcomes.Here we study the problem of user session categorization, where the goal is to automatically discover categories/classes of user insession behavior using event logs, and then consistently categorize each user session into the discovered classes. We develop a three stage approach which uses clustering to discover categories of sessions, then builds classi ers to classify new sessions into the discovered categories, and nally performs daily classi cation in a distributed pipeline. An important innovation of our approach is selecting a set of events as long-tail features, and replacing them with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identi cation of session types even though the underlying application is constantly changing. We deploy the approach to Pinterest and demonstrate its e ectiveness. We discover insights that have consequences for product monetization, growth, and design. Our solution classi es millions of user sessions daily and leads to actionable insights.