Unmanned aerial vehicle (UAV) has been widely used in various fields, which mainly relies on global navigation satellite system (GNSS) to obtain position information. However, fast-moving jamming poses a big challenge for UAV navigation. This motivates us to develop new robust adaptive beamforming technique that is capable of enhancing the navigation signal and suppressing the jamming efficiently. Since the navigation signal and jamming present non-Gaussianity in statistics, the minimum dispersion distortionless response (MDDR) beamformer is utilized which exhibits better performance of non-Gaussian signal reception over the minimum variance (MV) -based beamformer. To suppress the fast-moving jamming, the minimum power (MP) constraint is added to the optimization problem regarding to the MDDR beamformer, leading to a resultant problem with a quadratic constraint. Furthermore, instead of the quadratic constraint, a linear version is derived under the power constraint to achieve a shape null sector towards the fast-moving jamming, and as a consequence, a linearly constrained MDDR beamforming is established. This allows us to develop two algorithms, i.e., quasi-MVDR algorithm and Newton's method, to accelerate the solution. Simulation results show that the proposed beamformer is able to suppress the fast-moving jamming efficiently for UAV navigation.

In order to deploy minimum number of unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) to service all given ground terminals, this paper proposes an MBS placement based on sparse recovery (MBS-PBSR) algorithm. By exploiting the sparsity inherent in the differences between any two dedicated MBSs, the problem of UAV-mounted MBS placement could be formulated as an ℓ0-norm constrained optimization problem, which is then be solved by the reweighted ℓ1-norm method. Subsequently, the resulted solutions to the MBS placement are adjusted by the iterative redundant circle deletion algorithm, eventually leading to the redundant MBSs removal as much as possible. Simulation results demonstrate that our proposed MBS-PBSR algorithm works well with affordable computational complexity, and is nearly optimum in the sense of the number of deployed UAV-mounted MBSs.

The traditional proportional fair scheduling algorithm can only provide users with limited fairness while the channel is changing. To solve this problem and enhance the fairness between users, an adaptive proportional fair scheduling algorithm for LTE (Long term Evolution) system is proposed. It adjusts the scheduling priority according to individual user's channel condition. The proposed algorithm provides a good trade-off between capacity and fairness, and the simulation results show the validity.

In uplink multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems, inter-user interference includes co-channel interference (CCI) due to spatial multiplexing and inter-carrier interference (ICI) due to carrier-frequency offset (CFO). In this letter, a space-frequency joint processing scheme is proposed to suppress the two-dimensional interference alternatively and iteratively. Moreover, by the studies of convergence behavior of iterations, a sufficient condition for convergence is presented. The theoretical analysis and simulation results both show that this iterative scheme can effectively suppress inter-user interference with low complexity.

This paper aims at addressing estimation of carrier frequency offset (CFO) for the uplink orthogonal frequencydivision multiple access systems. Since the CFOs of the signals from different active users are sparsely distributed in the frequency domain, a sparse Bayesian learning is tailored to determine the CFO in this work, ending up with the sparse Bayesian learning assisted CFO (SBL-CFO) estimator. In particular, the CFO estimation problem is firstly formulated as a sparse nonnegative least squares (S-NNLS) problem. Meanwhile, background noise and sampling errors are mitigated utilizing a selection matrix and a whitening filter, respectively. This enables us to exploit the SBL with nonnegative Laplace prior (SBLNLP) to solve the S-NNLS problem. Furthermore, in order to make the convergence of the SBL-NLP algorithm faster and its estimation more accurate, the hyperprior inherent in the SBL-NLP algorithm is initialized by the traditional SBL with nonnegative Gaussian prior. Simulation results show that our proposed SBL-CFO estimator significantly outperforms the stateof- the-art estimators in terms of estimation accuracy, especially when the CFOs and number of active users are large.

This paper deals with an efficient method for channel estimation in Orthogonal frequency-division multiplexing (OFDM) systems. The channels are assumed to be Time-varying (TV) and approximated by a Secondorder polynomial (SOP) model. Increasing the polynomial order reduces the model error, but requires more polynomial estimation time. In order to circumvent the problem, we construct two SOP models by using a repeated-pattern preamble and pilots. As the channel information can be obtained from the second SOP model which polynomial estimation period is shorter than that of general SOP model, the proposed scheme is superior to the existing methods. Simulation results are presented to illustrate the superiority of our proposed approach.