Robust Spectrum Sharing via Worst Case Approach

This paper considers non-cooperative and fully-distributed power-allocation for secondary-users (SUs) in spectrum-sharing environments when normalized-interference to each secondary-user is uncertain. We model each uncertain parameter by the sum of its nominal (estimated) value and a bounded additive error in a convex set, and show that the allocated power always converges to its equilibrium, called robust Nash equilibrium (RNE). In the case of a bounded and symmetric uncertainty set, we show that the power allocation problem for each SU is simplified, and can be solved in a distributed manner. We derive the conditions for RNE's uniqueness and for convergence of the distributed algorithm; and show that the total throughput (social utility) is less than that at NE when RNE is unique. We also show that for multiple RNEs, the the social utility may be higher at a RNE as compared to that at the corresponding NE, and demonstrate that this is caused by SUs' orthogonal utilization of bandwidth for increasing the social utility. Simulations confirm our analysis

Worst-Case Robust Distributed Power Allocation in Shared Unlicensed Spectrum

This paper considers non-cooperative and fully-distributed power-allocation for selfish transmitter-receiver pairs in shared unlicensed spectrum when normalized-interference to each receiver is uncertain. We model each uncertain parameter by the sum of its nominal (estimated) value and a bounded additive error in a convex set, and show that the allocated power always converges to its equilibrium, called robust Nash equilibrium (RNE). In the case of a bounded and symmetric uncertainty region, we show that the power allocation problem for each user is simplified, and can be solved in a distributed manner. We derive the conditions for RNE's uniqueness and for convergence of the distributed algorithm; and show that the total throughput (social utility) is less than that at NE when RNE is unique. We also show that for multiple RNEs, the social utility may be higher at a RNE as compared to that at the corresponding NE, and demonstrate that this is caused by users' orthogonal utilization of bandwidth at RNE. Simulations confirm our analysis

Robustness of solutions to the capacitated facility location problem with uncertain demand

We investigate the properties of robust solutions of the Capacitated Facility Location Problem with uncertain demand. We show that the monotonic behavior of the price of robustness is not guaranteed, and therefore that one cannot discriminate among alternative robust solutions by simply relying on the trade-off price-vs-robustness. Furthermore, we report a computational study on benchmark instances from the literature and on instances derived from a real-world application, which demonstrates the validity in practice of our findings

Channel Capacity Maximization using NQHN Approach at Heterogeneous Network

In present scenario, the high speed data transmission services has pushed limits for wireless communication network capacity, at same time multimedia transmission in real-time needs provision of QoS, therefore the network capacity and small cell coverage has comes with lots of challenges. Improving the channel capacity and coverage area within the available bandwidth is necessary to provide better QoS to users, and improved channel capacity for the FCUs and MCUs in network. In this paper, we are proposing an NQHN approach that incorporate with efficient power allocation, improving the channel capacity by optimized traffic scheduling process in a small cell HetNets scenario. This work efficiently handle the interference with maintaining the user QoS and the implemented power controller uses HeNB power as per the real time based approach for macro-cell and femto-cell. Moreover, we consider the real traffic scenario to check the performance of our proposed approach with respect to existing algorith