Wireless visual sensor networks (WVSN) have recently seen dramatic growth with technology development. These networks consist of a number of smart visual sensors (VSes) that can collect visual information, i.e., images and video captured in a network area. Optimizing the energy consumption and coverage are important contradictory challenges in WVSNs since increase in coverage leads to increase in energy consumption. Therefore, optimization of energy consumption by maintaining image quality defined by user or operator located in sink [quality of experience (QoE)] to be used in target tracking applications is addressed in this paper. The target coverage as well as the quality of the received image of the target are considered as the desired QoE. The novel two-dimensional target coverage model is also presented mathematically. This model is described as a function of the VS inherent parameters, the target position and the visual sensor position. Based on a convex optimization framework, a heuristic approach for the VS selection and the focal length adjustment is suggested to solve the optimization problem while maintaining high image quality. Simulation results are presented to verify the capability and efficiency of the proposed method in comparison with the optimal method (Exhaustive search method).

Nowadays, by emerging new technologies, demand for a wireless visual sensor networks (WVSN) has been significantly increased. By providing the vital visual data from such networks, they play a substantial role in the surveillance applications. The wireless visual sensors (VSes) are the main component of these networks, which are equipped with camera and transceiver module. Energy optimization and satisfying the quality of the captured visual data, are two main contradictable issues in this area of research, especially in the target tracking applications. Therefore, these two issues have been simultaneously investigated in this paper. The coverage of the tracked targets and the quality of the captured visual data are considered as the quality of experienced (QoE). The desired threshold of QoE is defined by user. To optimize energy consumption with regard to QoE constraints in a multi target tracking scenario, an effective VSes selection is proposed. This method has been developed based on the convex optimization framework. Besides, the focal length of the VSes is set optimally by executing the proposed algorithm. Simulation results show efficiency of the proposed method compared with the optimal method (exhaustive search).

In this study, the sum throughput maximisation problem for cognitive radio networks (CRNs) based on the multiple‐input multiple‐output orthogonal frequency division multiple access (MIMO‐OFDMA) structure has been investigated. Hence, the intention is to maximise the downlink sum throughput of the CRN in a way that the sum power of the cognitive base station (CBS) remains below the given power limit and the induced interference on each subcarrier guarantees the interference threshold level predefined by primary users. Here, due to combinatorial constraints, the complex problem should be resolved. Hence, the core purpose of this study is to prepare a novel resource assignment algorithm that tries to fulfil these combinatorial constraints simultaneously. Therefore, this study makes progress towards solving the problem theoretically based on convex optimisation framework. An optimal solution has been implemented by proposing the iterative algorithm in which an optimal level of Lagrangian coefficients is obtained. Then, because of the optimal algorithm intricacy, the two low complicated algorithms are further suggested based on the solutions of two simplified versions of the original problem. Numerical results demonstrate that the suggested algorithms improve sum throughput considerably in comparison with classical algorithm. The proposed simplified algorithms converge to the optimal solutions’ performance while their complexities are desirable for practical implementation.

The authors propose a two‐dimensional direction‐of‐arrival (DOA) estimation for multi‐path environments, in which there are uncorrelated, partially correlated and coherent signals. The inability to identify non‐coherent signals from coherent ones results in a considerable waste of sensors. In this work, an adaptive and automated threshold is considered for the efficient separation of noise, non‐coherent signals, and coherent groups. First, non‐coherent signals, coherent groups, and noise subspace are separated using k ‐medoids clustering. After determining the number of sources, non‐coherent signals and coherent groups, non‐coherent DOAs are estimated separately. The number of coherent signals in each coherence group is determined by the minimum descriptive length criterion. Finally, coherent DOAs are estimated in each group by constructing a coherent estimation matrix. The proposed method does not require any prior information such as knowing the number of signals or the covariance matrix of uncorrelated signals. The simulation results show that the proposed method is able to distinguish between the non‐coherent and coherent signals, even at low signal‐to‐noise ratios and a small number of snapshots. Also, in terms of detection probability and estimation accuracy, it shows an improvement of over 1.2 and 83%, respectively, compared with the conventional forward−backward spatial smoothing scheme.

This study investigates outage probability (OP) of a generalised decode‐and‐forward‐based multiuser spectrum sharing relay system. In practice, the secondary users are exposed to the primary transmitters ’ signals which can measurably affect the OP in the secondary system. In this context, analytical and asymptotic outage analysis are presented for two different relay selection techniques, namely partial relay selection (PRS) and opportunistic relay selection (ORS). In terms of the ORS, deriving an exact analytical expression for the end‐to‐end (e‐t‐e) OP is contingent upon solving a very complicated integral. To resolve this problem, they propose a simple modification under which the OP can be obtained with preferable accuracy. Numerical simulations are presented where the excellent agreement of the simulation results with the analytical results validates the mathematical derivations and confirms that the proposed simplification is accurate. Findings suggest that the ORS has three main advantages over the PRS. First, it is more robust against the primary interference. Second, it achieves the full diversity order, whereas the PRS limits the diversity order to the number of secondary destinations plus one. Third, the gain of enhancement achieved by increasing the number of relays is significantly larger.

This study addresses resource allocation problem with partial fairness, considering weighted sum throughput maximisation for an orthogonal frequency‐division multiple access system with half‐duplex decode‐and‐forward relaying. In the system formulation, multi‐cell co‐channel interference as one of the critical factors in multi‐cell processing has been taken into account and its effect on system performance is evaluated. It has been shown that the problem is in the form of combinatorial non‐convex optimisation problems (NP‐hard). Avoiding to solve NP‐hard problem, the authors have made suboptimal solutions according to iterative methods to satisfy the system requirements in the best way. In this way, the resulting non‐convex optimisation problem has been solved by dual Lagrangian decomposition, and iterative resource allocation algorithms are proposed to find solutions which satisfy Karush–Kuhn–Tucker conditions. Two resource allocation strategies based on centralised and distributed scenarios are derived to maximise the average system throughput (bit/s/Hz) considering partial fairness. Simulation results indicate that the authors’ proposed methods work better than interference threshold methods and other conventional algorithms such as single cell methods which use local information processing.

This study addresses subcarrier allocation and power distribution in a sectorised two‐cell downlink orthogonal frequency‐division multiple access system impaired by multicell interference. The objective is to maximise the sum spectral efficiency in a way that the signal‐to‐interference plus noise ratio (SINR) of the transmitted data substream on each subcarrier meets a target SINR. In cellular systems because of intercell interference, it may not be possible to guarantee the target SINR in all cells simultaneously; therefore the authors have suggested a cooperative resource allocation algorithm which tries to provide the target SINR per subcarrier in all cells through cooperation in the interference control. For a considered subcarrier, if achieving the target SINR is impossible by the interference reduction, one base station avoids transmission on the considered subcarrier so that another base station can meet the target SINR. For complexity reduction, a simplified resource allocation algorithm is then provided based on the separation of subcarrier allocation and power control. Simulation results show that the proposed algorithms not only guarantee the target SINR but achieve more sum spectral efficiency than benchmark algorithms in many practical situations.

This study addresses resource allocation for weighted sum throughput maximisation (WSTM) in a sectorised two-cell downlink orthogonal frequency-division multiple access systems impaired by multicell interference. It is well known that the optimisation problem for this scenario is NP-hard and combinational, which is here converted to a novel WSTM problem based on the intercell interference limitation. In the new problem, we use a lower bound of throughout as a cost function which is convex, and so it simplifies the power allocation algorithm and offers a better convergence. Then, two subclasses of this new problem are solved based on the dual Lagrange framework. By the first subclass, on the assumption that subcarrier allocation parameters are fixed, an algorithm for optimal power allocation (OPA) is obtained. However, the optimal resource allocation requires an exhaustive search, including the OPA which cannot be implemented in practice because of its high complexity. By the second subclass, the joint subcarrier allocation and power distribution for two adjacent cells as a general problem is solved. Here, the algorithm allocates simultaneously the subcarriers and the power of the considered two cells while the resource allocation parameters of both cells are coupled mutually. After that, we propose a new centralised algorithm which combines the proposed power allocation with the non-convex subcarrier allocation without increase in the complexity. Simulation results show that the proposed algorithms achieve efficient performance in many practical situations.