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We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

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This letter deals with the identification of timevarying Rayleigh fading channels using a training sequence-based approach. When the fading channel is approximated by an autoregressive (AR) process, it can be estimated by means of Kalman filtering, for instance. However, this method requires the estimations of both the AR parameters and the noise variances in the state-space representation of the system. For this purpose, the existing noise compensated approaches could be considered, but they usually require a long observation window and do not necessarily provide reliable estimates when the signal-to-noise ratio is low. Therefore, we propose to view the channel identification as an errors-in-variables (EIV) issue. The method consists in searching the noise variances that enable specific noise compensated autocorrelation matrices of observations to be positive semidefinite. In addition, the AR parameters can be estimated from the null spaces of these matrices. Simulation results confirm the effectiveness of this approach, especially in presence of a high amount of noise.

This paper presents a case study about the evaluation and optimisation of GSM network in Jenien City, Palestine. Two approaches are used to evaluate the network performance, namely: key performance indicators (KPIs) and drive test. The initial evaluation of the network showed that about 0.76% of the initiated connections are dropped. In addition, about 7.76% of the collected samples from the drive test lie in level 4 which is the worst level in terms of signal quality and signal strength. Moreover, only 65.5% of the collected samples lie in level 1 which is the best level in terms of signal quality and strength. A new traffic channel (TCH) and broadcast control channel (BCCH) frequency plans are proposed to decrease TCH drop rate, reduce interference level and boost signal quality. The proposed plans are implemented into the network. Two kinds of optimisation procedures are adopted. The first one is manual optimisation while the second one is by using specific optimisation tools. According to the comparative study, we carried-out between the performance of the network before and after optimisation, the average TCH drop rate is reduced from 0.76% to 0.62%. In addition, the percentage of samples in level 1 is increased from 65.5% to 76.8% while the percentage of samples in level 4 is decreased from 7.76% to 5.16%. His research interests are in the field of signal processing for wireless communications with focus on mobile fading channel modelling and estimation, multicarrier techniques, cellular network planning and optimisation. He is a member of the IEEE Communications Society.