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Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to accurately and efficiently identifying a large number of wireless devices blindly installed to known three-dimensional installation/fitting points, based on received signal strengths (RSSs) between the devices. The approach is non-trivial because of the factorial, mapping nature of the considered problem, the multiplicative ranging errors of the RSS measurements (with standard deviation of 5–7 dB), and the requirement of high mapping accuracy in many internet-of-things (IoT) applications, e.g., industrial IoT and aircraft. The consideration of a structured environment where the set of candidate node positions is known beforehand shifts the problem of interest from a pure position estimation problem to a position assignment problem. The key idea of the proposed approach is that we interpret the position mapping problem with a probabilistic graphical model, where the factorial nature of mapping (more specifically, the mutual exclusiveness of devices at every installation point) is fully captured. A max-product belief propagation is designed against the probabilistic graph, to estimate the max-marginal position distribution of each device. The Kuhn-Munkres algorithm is applied to preserve the mutual exclusiveness of devices throughout the belief propagation and to decide device locations based on the estimated position distributions. Large-scale simulations and field tests are carried out, showing that the new approach achieves close-to-100% accuracy in simulations with hundreds of blindfolded devices under RSS measurement errors with standard deviation of 5–7 dB. Our approach also achieves 100% accuracy in all field trials with 76 devices inside the cabin of a Fokker 100 airplane, dramatically outperforming baseline techniques. [ABSTRACT FROM AUTHOR]

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes. [ABSTRACT FROM AUTHOR]

This paper investigates a wireless powered mobile edge computing (MEC) network where a hybrid access point (H-AP) serves energy-constrained devices. For such a wireless powered MEC network, the H-AP first transmits wireless information and energy simultaneously in the downlink. Then, by utilizing the harvested energy, the device processes both its local data and the received data from the H-AP. In this system, we minimize the latency by jointly optimizing the power splitting ratio of rectifiers, the data offloading power, the local computing frequency, and the data offloading ratio at the device. By applying a polyblock outer approximation procedure, we propose an algorithm which guarantees the optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes. [ABSTRACT FROM AUTHOR]

The measurement of received signal strength (RSS) and channel state information (CSI) has been widely used in the research of device-free localization (DFL). However, DFL systems based on these systems usually require labour-intensive training to understand the impact of the target on the surrounding wireless signals to build the target location-related model or image. In addition, the fact that the RSS is susceptible to multipath interference and is not directly accessible to CSI directly limits its actual deployment. In this paper, we explore the feasibility of DFL based on time-of-flight measurements of ultra-wideband signals, and propose a fusion method for simultaneous localization of multiple targets based on the evaluation results. Specifically, we mesh the feasible region of the target and utilize the geometric filtering method to re-evaluate the filtered wireless links in combination with the real-time adaptive relaxation target model. Consequently, we introduce the clustering method and combine the category matching strategy to ensure that the algorithm has robust real-time multi-target perception ability. Extensive experiments conducted in the lobby, laboratory and office scenarios demonstrate that the proposed method plays an active role in more than 82% and can control the mean error within 0.6 m. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]