Learning-based content caching with time-varying popularity profiles

Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load called the "offloading loss", which measures the fraction of the requested files that are not available in the sBS caches. Previous approaches minimize this offloading loss assuming that the popularity profile of the content is time-invariant and perfectly known. However, in many practical applications, the popularity profile is unknown and time-varying. Therefore, the analysis of caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied in this paper from a learning-theoretic perspective. A probably approximately correct (PAC) result is derived, in which a high probability bound on the offloading loss difference, i.e., the error between the estimated (outdated) and the optimal offloading loss, is investigated. The difference is a function of the Rademacher complexity of the set of all probability measures on the set of cached content items, the β-mixing coefficient, 1/√t (t is the number of time slots), and a measure of discrepancy between the estimated and true popularity profiles

An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the realtime hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration

An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the realtime hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration

Three-User Cognitive Channels with Cumulative Message Sharing: An Achievable Rate Region

In this paper, an achievable rate region for the three-user discrete memoryless interference channel with asymmetric transmitter cooperation is derived. The three-user channel facilitates different ways of message sharing between the transmitters. We introduce a manner of noncausal (genie aided) unidirectional message-sharing, which we term cumulative message sharing. We consider receivers with predetermined decoding capabilities, and define a cognitive interference channel. We then derive an achievable rate region for this channel by employing a coding scheme which is a combination of superposition and Gel'fand-Pinsker coding techniques

Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors

The subspace intersection method (SIM) provides unbiased bearing estimates of multiple acoustic sources in a range-independent shallow ocean using a one-dimensional search without prior knowledge of source ranges and depths. The original formulation of this method is based on deployment of a horizontal linear array of hydrophones which measure acoustic pressure. In this paper, we extend SIM to an array of acoustic vector sensors which measure pressure as well as all components of particle velocity. Use of vector sensors reduces the minimum number of sensors required by a factor of 4, and also eliminates the constraint that the intersensor spacing should not exceed half wavelength. The additional information provided by the vector sensors leads to performance enhancement in the form of lower estimation error and higher resolution