Energy efficiency (EE) is currently the key performance evaluation metric for wireless networks. This paper considers the maximization of EE of an uplink wireless network that uses single‐carrier frequency division multiple access. The EE metric considered is bits/Joule, and a joint power and subchannel allocation problem is formulated to maximize this EE metric under constraints on the minimum achieved data rate of each user, the maximum transmit power budget of each user, and the exclusive as well as the consecutive allocation of single‐carrier frequency division multiple access subchannels among the users. This problem is a nonlinear and combinatorial optimization problem, and its optimal solution is prohibitively difficult. To make the problem tractable and find its optimal solution, it is transformed into an equivalent binary integer programming problem, which is a standard set partitioning problem. This approach named as the optimal energy‐efficient (OPT‐EE) algorithm finds optimal solution with significantly reduced computational complexity compared to the solution of the original problem. In addition, a heuristic suboptimal energy‐efficient approach is also investigated, which has acceptable performance with much less computational complexity compared to the OPT‐EE algorithm. Simulation results demonstrate the performance of OPT‐EE and heuristic suboptimal energy‐efficient algorithms and their comparison with the available work in the literature.

No abstract

<p>Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The results show significant performance gain even for unseen feature maps with minimal loss in accuracy. Results show a significant reduction in training parameters, inference, and increased pattern in FPS and GFLOPs for \textit{MFNet} compared to YOLOv5s. \textit{MFNet-M} performance evaluation in terms of precision, recall, mean average-precision (mAP), and IOU increased around 1.8\%, 2.2\%, 0.9\%, 1.7\% compared to YOLOv5s. Furthermore, \textit{MFNet-M} achieves the best performance with 96.8\% precision, 88.4\% recall, 95.9\% mAP, and 51.1\% IoU for UAV detection. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.</p>

The next generation networks intend to have features like device to device (D2D) connectivity, energy efficiency and spectral efficiency. This paper presents problem formulation for maximization of energy efficiency of cognitive radio assisted D2D networks subject to compliance of transmit powers of cellular users and interference constraints of primary users of broadcast network. Cognitive radio network (CRN) users are cellular users comprising of cellular and D2D users. Cellular users can opt any of the cellular or D2D mode. These CRN users opportunistically utilize spectrum of television (TV) white spaces. The problem thus formulated is NP-complete. Mesh adaptive direct search (MADS) algorithm has been used to find ε-optimal solution. The results of MADS are compared with the global optimal solution obtained by exhaustive search algorithm (ESA). The simulation results reveal that MADS' performance is equally good as that of ESA in most of the cases. MADS outperforms ESA in terms of energy efficiency comparison. Simulation results compare results of utility value, spectral efficiency and admission of CRN users with two types of utilities, i.e., utility with maximization of energy efficiency and utility without maximization of energy efficiency. Results testify that utility with maximization of energy efficiency outperforms its counterpart utility. MADS is also ideal algorithm as it has low computational complexity as compared to ESA. Computational complexity of ESA increases exponentially as numbers of users increase making MADS obvious choice for real life networks comprising of large number of cellular users. KeywordsD2D • Cognitive radio • Energy efficiency • MADS B Muhammad Naeem

Detection of amateur drones (AmDrs) is mandatory requirement of various defence organizations and is also required to protect human life. In literature, various researchers contributed in this regard and developed different algorithms utilizing video, thermal, radio frequencies and acoustic signals. However, inefficiency of the existing techniques is reported in different atmospheric conditions. In this paper, acoustic signal processing is performed based on independent vector analysis (IVA) to detect AmDrs in the field. This technique is capable to detect more than one AmDrs in the sensing field at a time in the presence of strong interfering sources. The IVA is a relatively new and practically applicable technique of blind source separation and is more efficient than the independent component analysis technique. In the proposed technique, recorded mixed signals through multiple microphones are first un-mixed through using the IVA technique. Then various features of the separated signals are extracted. These features include Root Mean Square (RMS) values, Power Spectral Density (PSD) and Mel Frequency Cepstral coefficients (MFCC). Finally, signals classification is performed through Support Vector Machines (SVM)and K Nearest Neighbor (KNN) to detect AmDrs in the field. Performance evaluation of the proposed technique is carried out through simulations and observed the superior performance of the proposed technique.