Power allocations in an interference-limited wireless network for global maximization of the weighted sum throughput or global optimization of the minimum weighted rate among network links are not ...only important but also very hard optimization problems due to their nonconvexity nature. Recently developed methods are either unable to locate the global optimal solutions or prohibitively complex for practical applications. This paper exploits the d.c. (difference of two convex functions/sets) structure of either the objective function or constraints of these global optimization problems to develop efficient iterative algorithms with very low complexity. Numerical results demonstrate that the developed algorithms are able to locate the global optimal solutions by only a few iterations and they are superior to the previously-proposed methods in both performance and computation complexity.
We present the fundamental concepts of SPH with particular emphasis on its state-of-the-art applications in geomechanics and geotechnical engineering. In the first part of the paper, we focus on ...establishing fundamental SPH equations and discussing how they are used to solve partial differential equations (PDEs) in geomechanics. Through this process, we expect to provide readers with a better understanding of SPH formulations to avoid misuse or misinterpretation of its capacity and limitation. Discussions on several outstanding issues and recommendations for further developments are also be presented. Of particular interest through this revisit of the key SPH concepts is a new and robust SPH approximation formulation for the Laplacian, which involves the second-order derivatives of a field quantity. This new formulation is proven to outperform existing SPH formulations and achieve high accuracy. The second part of the paper focuses on demonstrating the applications of SPH in the fields of geomechanics and geotechnical engineering through various examples, ranging from the most fundamental to more complex applications involving multi-phase flows. We hope that this paper will become a useful resource to provide readers with a better understanding of SPH and its potential in solving complex problems in geomechanics and geotechnical engineering.
Alternative polyadenylation (APA) affects most mammalian genes. The genome-wide investigation of APA has been hampered by an inability to reliably profile it using conventional RNA-seq. We describe ...'Quantification of APA' (QAPA), a method that infers APA from conventional RNA-seq data. QAPA is faster and more sensitive than other methods. Application of QAPA reveals discrete, temporally coordinated APA programs during neurogenesis and that there is little overlap between genes regulated by alternative splicing and those by APA. Modeling of these data uncovers an APA sequence code. QAPA thus enables the discovery and characterization of programs of regulated APA using conventional RNA-seq.
This paper presents an overview of admittance control as a method of physical interaction control between machines and humans. We present an admittance controller framework and elaborate control ...scheme that can be used for controller design and development. Within this framework, we analyze the influence of feed-forward control, post-sensor inertia compensation, force signal filtering, additional phase lead on the motion reference, internal robot flexibility, which also relates to series elastic control, motion loop bandwidth, and the addition of virtual damping on the stability, passivity, and performance of minimal inertia rendering admittance control. We present seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.
Unmanned aerial vehicles (UAVs) have emerged as a promising candidate solution for data collection of large-scale wireless sensor networks (WSNs). In this paper, we investigate a UAV-aided WSN, where ...cluster heads (CHs) receive data from their member nodes, and a UAV is dispatched to collect data from CHs. We aim to minimize the total energy consumption of the UAV-WSN system in a complete round of data collection. Toward this end, we formulate the energy consumption minimization problem as a constrained combinatorial optimization problem by jointly selecting CHs from clusters and planning the UAV's visiting order to the selected CHs. The formulated energy consumption minimization problem is NP-hard, and hence, hard to solve optimally. To tackle this challenge, we propose a novel deep reinforcement learning (DRL) technique, pointer network-A* (Ptr-A*), which can efficiently learn the UAV trajectory policy for minimizing the energy consumption. The UAV's start point and the WSN with a set of pre-determined clusters are fed into the Ptr-A*, and the Ptr-A* outputs a group of CHs and the visiting order of CHs, i.e., the UAV's trajectory. The parameters of the Ptr-A* are trained on small-scale clusters problem instances for faster training by using the actor-critic algorithm in an unsupervised manner. Simulation results show that the trained models based on 20-clusters and 40-clusters have a good generalization ability to solve the UAV's trajectory planning problem in WSNs with different numbers of clusters, without retraining the models. Furthermore, the results show that our proposed DRL algorithm outperforms two baseline techniques.
This paper considers joint optimization of cooperative beamforming and relay assignment for multi-user multi-relay wireless networks to maximize the minimum of the received ...signal-to-interference-plus-noise ratios (SINR). Separated continuous optimization of beamforming and binary optimization of relay assignment already pose very challenging programs. Certainly, their joint optimization, which involves nonconvex objectives and coupled constraints in continuous and binary variables, is among the most challenging optimization problems. Even the conventional relaxation of binary constraints by continuous box constraints is still computationally intractable because the relaxed program is still highly nonconvex. However, it is shown in this paper that the joint programs fit well in the d.c. (difference of two convex functions/sets) optimization framework. Efficient optimization algorithms are then developed for both cases of orthogonal and nonorthogonal transmission by multiple users. Simulation results show that the jointly optimized beamforming and relay assignment not only save transmission bandwidth but can also maintain well the network SINRs.
Long-range (LoRa) modulation is an orthogonal modulation scheme that uses linearly-modulated up chirps to represent information bits. Its constant envelope and good bit-error-rate performance make it ...one of the key players in establishing low-power wide-area networks for the Internet of things applications. However, LoRa modulation has low data rates. In this letter, we propose a new constant-envelope modulation scheme, named slope-shift-keying and interleaved-chirp spreading (SSK-ICS) LoRa modulation, that can deliver higher data rates than the conventional LoRa modulation scheme. Succinctly, the proposed SSK-ICS-LoRa modulation uses up chirps, down chirps, interleaved up chirps and interleaved down chirps to expand the signal set and hence can carry more bits per transmission symbol. For the same spreading factor and bandwidth consumption, the proposed scheme is able to improve the data rate of the conventional LoRa scheme up to 28.6%. We also present the optimal maximum-likelihood detectors for both coherent and non-coherent demodulators for the proposed scheme. Simulation results show that the proposed scheme outperforms LoRa modulation in both data rate and bit-error rate.
Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a ...base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.
Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory ...planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm's decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms.
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