Distributed Resource Allocation and Decision Making under Uncertainty: A Cooperation Perspective
Final Report Abstract
In this project, we first investigate the emerging ultra-dense small cell networks (UD-SCNs) with energy harvesting. Such networks need to combat a variety of challenges. On one hand, densely-deployed small base stations (SBSs) and massive number of devices share limited wireless resources, which renders centralized control mechanisms infeasible, as a result of the excessive cost of information acquisition and computation. On the other hand, to reduce the energy consumption from fixed power grid and/or battery, the network entities may need to rely on the energy harvested from the ambient environment. However, opportunistic energy harvesting introduces uncertainty in the network operation. We review the state-of-the-art research, and outline the major challenges that arise in presence of energy harvesting due to the uncertainty (e.g., limited knowledge on energy harvesting process or channel profile), as well as limited computational capacities. We also propose an approach based on the mean field multi-armed bandit games to solve the uplink user association problem for energy harvesting devices in an UD-SCN in presence of uncertainty. Then we consider a user association problem in the downlink of an SCN, where SBSs are powered solely through ambient energy harvesting. Since energy harvesting is opportunistic, the amount of harvested energy is a random variable, without a priori known statistical characteristics. Thus, at the time of user association, the amount of available energy is unknown. We model the network as a competitive market with uncertainty, where self-interested SBSs, modeled as consumers, are willing to maximize their utility scores by selecting users, represented as commodities. The utility scores of SBSs depend on the random amount of harvested energy, formulated as nature’s state. For this model we prove the existence and characterize general equilibrium under uncertainty. Moreover, by using the Walrasian auction and the static knapsack problem, we develop an efficient distributed user association scheme which converges to equilibrium. We investigate a distributed downlink user association problem in a dynamic SCN, where every SBS obtains its required energy through ambient energy harvesting. On one hand, as also mentioned before, energy harvesting is inherently opportunistic so that the amount of available energy is a random variable. On the other hand, users arrive at random and require different wireless services, rendering the energy consumption a random variable. We shortly describe a probabilistic framework to mathematically model and analyze the random behavior of energy harvesting and energy consumption. We further analyze the probability of QoS satisfaction (success probability), for each user with respect to every SBS. Since acquiring a (statistical) knowledge of existing random variables (e.g., network traffic and channel quality) is costly, we develop a bandit-theoretical formulation for distributed SBS selection when no prior information is available at users. We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible. This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this study, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize the influence across the whole network.
Publications
- ”Distributed User Association in Energy Harvesting Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach,” in IEEE Access, vol. 5, pp. 3513-3523, Feb. 2017
S. Maghsudi and E. Hossain
(See online at https://doi.org/10.1109/ACCESS.2017.2676166) - ”Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Bandit Model,” in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1549-1563, March 2017
S. Maghsudi and E. Hossain
(See online at https://doi.org/10.1109/TWC.2017.2647946) - ”Distributed User Association in Energy Harvesting Small Cell Networks: An Exchange Economy With Uncertainty,” in IEEE Transactions on Green Communications and Networking, vol. 1, no. 3, pp. 294-308, Sep. 2017
S. Maghsudi and E. Hossain
(See online at https://doi.org/10.1109/TGCN.2017.2715349) - ”Influence Maximization with Partial Observation,” in International Joint Conference on Artificial Intelligence-Workshop on Social Influence Maximization (IJCAI-SocInf), pp. 20-32, Aug. 2017
S. Stein, S. Eshghi, S. Maghsudi, R. Bellamy, L. Tassiulas and N. Jennings
