Database-assisted Distributed Spectrum Sharing

According to FCC's ruling for white-space spectrum access, white-space devices are required to query a database to determine the spectrum availability. In this paper, we study the database-assisted distributed white-space access point (AP) network design. We first model the cooperative and non-cooperative channel selection problems among the APs as the system-wide throughput optimization and non-cooperative AP channel selection games, respectively, and design distributed AP channel selection algorithms that achieve system optimal point and Nash equilibrium, respectively. We then propose a state-based game formulation for the distributed AP association problem of the secondary users by taking the cost of mobility into account. We show that the state-based distributed AP association game has the finite improvement property, and design a distributed AP association algorithm that can converge to a state-based Nash equilibrium. Numerical results show that the algorithm is robust to the perturbation by secondary users' dynamical leaving and entering the system

Polarization effect of zinc on the region 1-16 of amyloid-beta peptide: a molecular dynamics study

Zinc is found saturated in the deposited Amyloid-beta (AB) peptide plaques in brains of patients subjected to Alzheimer disease (AD). Zinc binding to AB promotes aggregations, including the toxic soluble AB species. Up to now, only the region 1-16 of AB complexed with Zinc (AB16-Zn) is defined structurally in experiment, requiring an efficient theoretical method to present the interaction between zinc and AB peptide. In order to explore the induced polarization effect on the global conformation fluctuations and the experimentally observed coordination mode of AB16-Zn, in this work we consider an all-atom molecular dynamics (MD) of AB16-Zn solvated in implicit water. In our model the polarization effect affects the whole peptide is applied. The induced dipoles are divided into three distinct scales according to their distances from zinc. Besides, the atomistic polarizability on the coordinating sidechains is rescaled to describe the electron redistribution effect. As a comparison, another model which exactly follows the method of Sakharov and Lim (J. Am. Chem. Soc., 127, 13, 2005) has been discussed also. We show that, associated with proper van der Waals (vdW) parameters, our model not only obtains the reasonable coordinating configuration of zinc binding site, but also retains the global stabilization, especially the N-terminal region, of the AB16-Zn. We suggest that it is the induced polarization effect that promotes reasonable solvent exposures of hydrophobic/hydrophilic residues regarding zinc-induced AB aggregation

Price Differentiation for Communication Networks

We study the optimal usage-based pricing problem in a resource-constrained network with one profit-maximizing service provider and multiple groups of surplus-maximizing users. With the assumption that the service provider knows the utility function of each user (thus complete information), we find that the complete price differentiation scheme can achieve a large revenue gain (e.g., 50%) compared to no price differentiation, when the total network resource is comparably limited and the high willingness to pay users are minorities. However, the complete price differentiation scheme may lead to a high implementational complexity. To trade off the revenue against the implementational complexity, we further study the partial price differentiation scheme, and design a polynomial-time algorithm that can compute the optimal partial differentiation prices. We also consider the incomplete information case where the service provider does not know which group each user belongs to. We show that it is still possible to realize price differentiation under this scenario, and provide the sufficient and necessary condition under which an incentive compatible differentiation scheme can achieve the same revenue as under complete information.Comment: Technical report for the paper of the same name to appear in IEEE/ACM Transactions on Networkin

Spatial Spectrum Access Game

A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient spectrum sharing mechanisms. In this paper, we propose a framework of spatial spectrum access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial spectrum access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial spectrum access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial spectrum access game. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the spectrum access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial spectrum access games. Numerical results demonstrate that the distributed learning algorithm achieves up to superior performance improvement over a random access algorithm.Comment: The paper has been accepted by IEEE Transactions on Mobile Computin

Evolutionarily Stable Spectrum Access

In this paper, we design distributed spectrum access mechanisms with both complete and incomplete network information. We propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms are robust to the perturbations of users' channel selections.Comment: arXiv admin note: substantial text overlap with arXiv:1103.102

Cooperative Planning of Renewable Generations for Interconnected Microgrids

We study the renewable energy generations in Hong Kong based on realistic meteorological data, and find that different renewable sources exhibit diverse time-varying and location-dependent profiles. To efficiently explore and utilize the diverse renewable energy generations, we propose a theoretical framework for the cooperative planning of renewable generations in a system of interconnected microgrids. The cooperative framework considers the self-interested behaviors of microgrids, and incorporates both their long-term investment costs and short-term operational costs over the planning horizon. Specifically, interconnected microgrids jointly decide where and how much to deploy renewable energy generations, and how to split the associated investment cost. We show that the cooperative framework minimizes the overall system cost. We also design a fair cost sharing method based on Nash bargaining to incentivize cooperative planning, such that all microgrids will benefit from cooperative planning. Using realistic data obtained from the Hong Kong observatory, we validate the cooperative planning framework, and demonstrate that all microgrids benefit through the cooperation, and the overall system cost is reduced by 35.9% compared to the noncooperative planning benchmark.Comment: To appear in IEEE Transactions on Smart Gri

Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs

Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory in off-the-shelf devices is always finite, which limits the image resolutions and batch sizes that could be used for better DNN performance. Existing approaches to alleviate memory issue include better GPUs, distributed computation and gradient checkpointing. Among them, gradient checkpointing is a favorable approach as it focuses on trading computation for memory and does not require any upgrades on hardware. In gradient checkpointing, during forward, only a subset of intermediate tensors are stored, which are called Gradient Checkpoints (GCPs). Then during backward, extra local forwards are conducted to compute the missing tensors. The total training memory cost becomes the sum of (1) the memory cost of the gradient checkpoints and (2) the maximum memory cost of local forwards. To achieve maximal memory cut-offs, one needs optimal algorithms to select GCPs. Existing gradient checkpointing approaches rely on either manual input of GCPs or heuristics-based GCP search on linear computation graphs (LCGs), and cannot apply to arbitrary computation graphs(ACGs). In this paper, we present theories and optimal algorithms on GCP selection that, for the first time, apply to ACGs and achieve maximal memory cut-offs. Extensive experiments show that our approach constantly outperforms existing approaches on LCGs, and can cut off up-to 80% of training memory with a moderate time overhead (around 40%) on LCG and ACG DNNs, such as Alexnet, VGG, Resnet, Densenet and Inception Net

Imitation-based Social Spectrum Sharing

Dynamic spectrum sharing is a promising technology for improving the spectrum utilization. In this paper, we study how secondary users can share the spectrum in a distributed fashion based on social imitations. The imitation-based mechanism leverages the social intelligence of the secondary user crowd and only requires a low computational power for each individual user. We introduce the information sharing graph to model the social information sharing relationship among the secondary users. We propose an imitative spectrum access mechanism on a general information sharing graph such that each secondary user first estimates its expected throughput based on local observations, and then imitates the channel selection of another neighboring user who achieves a higher throughput. We show that the imitative spectrum access mechanism converges to an imitation equilibrium, where no beneficial imitation can be further carried out on the time average. Numerical results show that the imitative spectrum access mechanism can achieve efficient spectrum utilization and meanwhile provide good fairness across secondary users

Achieving an Efficient and Fair Equilibrium Through Taxation

It is well known that a game equilibrium can be far from efficient or fair, due to the misalignment between individual and social objectives. The focus of this paper is to design a new mechanism framework that induces an efficient and fair equilibrium in a general class of games. To achieve this goal, we propose a taxation framework, which first imposes a tax on each player based on the perceived payoff (income), and then redistributes the collected tax to other players properly. By turning the tax rate, this framework spans the continuum space between strategic interactions (of selfish players) and altruistic interactions (of unselfish players), hence provides rich modeling possibilities. The key challenge in the design of this framework is the proper taxing rule (i.e., the tax exemption and tax rate) that induces the desired equilibrium in a wide range of games. First, we propose a flat tax rate (i.e., a single tax rate for all players), which is necessary and sufficient for achieving an efficient equilibrium in any static strategic game with common knowledge. Then, we provide several tax exemption rules that achieve some typical fairness criterions (such as the Max-min fairness) at the equilibrium. We further illustrate the implementation of the framework in the game of Prisoners' Dilemma.Comment: This manuscript serves as the technical report for the paper with the same title published in APCC 201

Distributed Spectrum Access with Spatial Reuse

Efficient distributed spectrum sharing mechanism is crucial for improving the spectrum utilization. The spatial aspect of spectrum sharing, however, is less understood than many other aspects. In this paper, we generalize a recently proposed spatial congestion game framework to design efficient distributed spectrum access mechanisms with spatial reuse. We first propose a spatial channel selection game to model the distributed channel selection problem with fixed user locations. We show that the game is a potential game, and develop a distributed learning mechanism that converges to a Nash equilibrium only based on users' local observations. We then formulate the joint channel and location selection problem as a spatial channel selection and mobility game, and show that it is also a potential game. We next propose a distributed strategic mobility algorithm, jointly with the distributed learning mechanism, that can converge to a Nash equilibrium