A Complexity Adjustable Scheduling Algorithm for Throughput Maximization in Clusterized TDMA Networks

We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA) to access a wireless channel that is shared among several clusters. Earlier work has demonstrated that a significant increase in network throughput can be achieved if all the schedules are optimized jointly. However, a drawback of this approach is the prohibitive level of computational complexity is required when a network with a large number of clusters and time-slots is to be scheduled. In this paper, we propose a modification to our previously proposed algorithm which allows for the complexity to be adjusted to the available processing power, provided some minimum processing power is available. This is achieved by carefully reducing the number of interfering clusters considered when scheduling a cluster. In addition, we propose and evaluate two heuristic methods of discarding the less significant clusters. While the optimality of the obtained schedule is not proven, our results demonstrate that large gains are consistently attainable

On Models, Bounds, and Estimation Algorithms for Time-Varying Phase Noise

In this paper, first, a new discrete-time model of phase noise for digital communication systems, which is a more accurate model compared to the classical Wiener model, is proposed based on a comprehensive continuous-time representation of time-varying phase noise, and statistical characteristics of this model are derived. Next, the non-data-aided (NDA) and decision-directed (DD) maximum-likelihood (ML) estimators of time-varying phase noise, using the proposed discrete-time model are derived. To evaluate the performance of the proposed estimators, the Cramer-Rao lower bound (CRLB) for each estimation approach is derived and by using Monte-Carlo simulations it is shown that the mean-square error (MSE) of the proposed estimators converges to the CRLB at moderate signal-to-noise ratios (SNR). Finally, simulation results show that the proposed estimators outperform existing estimation methods as the variance of the phase noise process increases