Resource Allocation for Energy-Efficient Device-to-Device Communication in 4G Networks

Device-to-device (D2D) communications as an underlay of a LTE-A (4G) network can reduce the traffic load as well as power consumption in cellular networks by way of utilizing peer-to-peer links for users in proximity of each other. This would enable other cellular users to increment their traffic, and the aggregate traffic for all users can be significantly increased without requiring additional spectrum. However, D2D communications may increase interference to cellular users (CUs) and force CUs to increase their transmit power levels in order to maintain their required quality-of-service (QoS). This paper proposes an energy-efficient resource allocation scheme for D2D communications as an underlay of a fully loaded LTE-A (4G) cellular network. Simulations show that the proposed scheme allocates cellular uplink resources (transmit power and channel) to D2D pairs while maintaining the required QoS for D2D and cellular users and minimizing the total uplink transmit power for all users.Comment: 2014 7th International Symposium on Telecommunications (IST'2014

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

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

An upper bound on air interface blocking probability in multi-service CDMA networks

We propose a novel approach to derive an upper bound for the air interface blocking probability in a multiservice CDMA network with soft handoff (SHO) as a function of network load. This method requires only general assumptions made for network design and dimensioning. We obtain an approximated upper bound and compare it with the calculated values for the upper bound and with real network simulation results to show that our method with a reduced computational complexity is also accurate

Real-time Detection of Precursors to Epileptic Seizures: Non-Linear Analysis of System Dynamics

We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (iEEG), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent of the discrete wavelet packet transform (DWPT) of the segmented EEG signals is considered as the discriminating features. Such features are processed by a support vector machine (SVM) classifier to identify whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. When consecutive EEG segments contain such precursors, a decision is made that a precursor is in fact detected. The proposed scheme is applied to the Freiburg dataset, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with sensitivity of 100% and negligible false positive detection rates