The aim of this study is to improve the performance of two-tier macro/femtocell networks using a power control approach. In wireless networks, power control plays an important role in improving a number of performance parameters such as co-channel interference and outage probability reduction, throughput increasing, and power saving. This study explores the evolution of centralised power control algorithm based on femtocell base station (FBS) clustering and predicted signal-to-interference-plus-noise ratio (SINR) of users. To reduce the computational complexity of centralised algorithm, dense deployed femtocells are considered in different clusters. In this case, femtocells inside one cluster make considerable interference to each other, while the interferences from femtocells of other clusters are negligible. Moreover, because of the non-linearity of SINR samples, non-linear logistic smooth transition autoregressive (LSTAR) model is used to model the SINR data, and then the next SINR samples are predicted from the previous samples. According to the clustered FBSs and predicted SINR, the proposed power control scheme is applied to femtocell network in the downlink. The results demonstrate that the introduced method improves the outage probability and throughput and outperforms previous methods significantly.

The problem of interacting multiple model (IMM) estimation for jump Markov linear systems with unknown measurement noise covariance is studied. The system state and the unknown covariance are jointly estimated, where the unknown covariance is modelled as a random matrix according to an inverse-Wishart distribution. For the IMM estimation with random matrices, one difficulty encountered is the combination of a set of weighted inverse-Wishart distributions. Instead of using the moment matching approach, this difficulty is overcome by minimising the weighted Kullback–Leibler divergence for inverse-Wishart distributions. It is shown that a closed-form solution can be derived for the optimisation problem and the resulting solution coincides with an inverse-Wishart distribution. Simulation results show that the proposed filter outperforms the previous work using the moment matching approach.

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

For solving the l ₁-l ₁ minimisation problem, the authors propose a reweighted fast iterative shrinkage thresholding algorithm. The proposed algorithm consists of two steps: in the first step, the authors apply the smoothing technique to l ₁-l ₁ minimisation; and in the second step the smoothed problem is solved by fast iterative shrinkage thresholding algorithm (FISTA). With the help of restarts technique, the authors further accelerate the reweighted FISTA algorithm. Compared with some provable and efficient existing methods, the methods proposed in this study enjoy faster speed, less parameters and that the convergent analysis does not need any assumption of A. On the computational level, numerical experiments on sparse signal recovery demonstrate the efficiency of the proposed methods.

In this study, the authors propose a novel component-wise variable step-size (CVSS) diffusion distributed algorithm for estimating a specific parameter over sensor networks. The novelty of the CVSS algorithm is that step-sizes vary from each other on different components at each iteration. They derive the steady-state value of global mean-square deviation (MSD) and relative MSD (RMSD). In the numerical simulations, they compare the proposed CVSS algorithm with several other least mean square (LMS) algorithms. Results show that, when compared with these other algorithms, the CVSS algorithm can effectively reduce steady-state value and speed up convergence rate of RMSD while not sacrificing the convergence rate of MSD. Results also reveal that the proposed CVSS algorithm can achieve reduced difference of steady-state values of relative estimation error on various components.

Polyphase codes including Frank, P1, P2, P3 and P4 codes are different from linear frequency modulation (LFM) and conventional phase coding, which have brought great challenges to current reconnaissance systems. Polyphase codes are derived from approximation to LFM waveforms. By theoretical derivation and simulation analysis, the authors can conclude that time–frequency rate (TFR) distribution is much more suitable for detection and estimation of polyphase coded signals than time–frequency distribution. Based on the transformation of TFR distribution, a hierarchical signal detection scheme and a peak slice-based parameter estimation method are proposed for polyphase coded signals. To improve the energy concentration, two higher order TFR distributions are presented as a supplement and comparison. The theoretical analysis and simulation experiments demonstrate the validity and efficiency of the proposed method.

The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalisation for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Their proposed detector exploits sparsity in its decision rule. They demonstrate the high efficiency of their method in the cases of voice activity detection in speech processing.

Electromagnetic radiation signal from computer display can be seen as a computer security risk if the radiation signal is intercepted and reconstructed. Electromagnetic radiation signal from computer display can also be called video leaking signal. Synchronising information extraction is the key problem of computer video leaking signal interception and reconstruction. To solve such problem, a novel synchronising information extraction algorithm based on spectral centroid has been developed. This study not only introduced spectral centroid into video leaking signal processing but also defined the concept of segmented spectral centroid. In addition, the uniformity degree of spectral centroid spacing distribution was defined to describe the harmonic characteristics of video leaking signal spectrum. The proposed algorithm can extract the electromagnetic radiation signal's synchronising information automatically and efficiently even with interference signal. Thus, the interception and reconstruction of electromagnetic radiation can be realised more effectively and the anti-interference performance can be improved.

This study presents a speech filtering method exploiting the combined effects of the empirical mode decomposition (EMD) and the local statistics of the speech signal using the adaptive centre weighted average (ACWA) filter. The novelty lies in incorporating the frame class (voiced/unvoiced) in the conventional filtering using the EMD and the ACWA filter. The speech signal is segmented into frames and each one is broken down by the EMD into a finite number of intrinsic mode functions (IMFs). The number of filtered IMFs depends on whether the frame is voiced or unvoiced. An energy criterion is used to identify voiced frames while a stationarity index distinguishes between unvoiced and transient sequences. Reported results obtained on signals corrupted by additive noise (white, F16, factory) show that the proposed filtering in line with the frame class is very effective in removing noise components from noisy speech signal. Compared with filtering results of the wavelet, the ACWA, and the EMD-ACWA methods, the proposed technique gives much better results in terms of average segmental signal-to-noise ratio and listening quality based on perceptual evaluation speech quality score.

In this study, parameter identifiability in array shape self-calibration in colocated multiple-input multiple-output radar is addressed under a deterministic framework. In contrast to the random model used in the previous analysis, some distinct identifiability conditions are established through deriving and then analysing the Cramér–Rao bound on self-calibration accuracy of antenna positions using far-field targets whose directions of arrival and scattering coefficients are initially unknown. It is proved that at least three non-collinear targets are needed to precisely self-calibrate the positions of antennas of arbitrary geometry when there exist a position reference and a direction reference. The sole exception is an actually linear array for which self-calibration is impossible.