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This paper presents a concept of a routing algorithm for a network of sensors monitoring the condition of machinery and equipment operating in areas at risk of methane and/or coal dust explosion. It was assumed that the proposed algorithm would find application in sensor networks monitoring, among other things, oil pressure in powered roof supports and the position of powered roof support elements. The results of a literature analysis were presented, which included the simulation of sensor network routing algorithms, including, among others, reactive algorithms, proactive algorithms and algorithms based on so-called swarm intelligence (SI). As a result of the analyses, several algorithms were selected and implemented in a prototype sensor network. The characteristics of each of these algorithms are described. The article includes a description of the commissioning work of the network, which consisted of between 3 and 30 nodes. An analysis of the collected measurement data obtained for each criterion of the performance evaluation of the routing algorithms is presented. [ABSTRACT FROM AUTHOR]

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives. [ABSTRACT FROM AUTHOR]

This paper presents a wireless sensor network system (WSNS) for effective, reliable, and efficient monitoring of landslides. The system incorporates a network of wireless inertial measurement unit (IMU) sensor devices for collecting movement data, a local base station for data gathering, a capture server for data processing and storage, and a warning system. The major contributions of this paper are: 1) two approaches for defining movement thresholds; 2) landslide classification concept based on IMU sensor data patterns and magnitudes; and 3) a conceptual framework for building an intelligent and reliable wireless monitoring and warning system. The IMU sensor data collected by three-axis accelerometer and three-axis gyroscope were utilized to define the movement thresholds and classify landslides based on specially designed laboratory experiments. The performances of the IMU sensors and the base station for data collection and communication were tested through a rock-fall experiment conducted in the field conditions. The WSN-IMU system is capable of monitoring all types of slope movements independent of the triggering factors. The unique ability of the WSN-IMU system to determine landslide types allows designers and authorized personnel to predict subsequent movement pattern and duration so as to implement appropriate risk management and control measures to alleviate the socioeconomic losses. This paper outcome serves as the foundation for future studies and technological advancements that will facilitate landslide stabilization or mitigation actions as well as to predict the intensity of damages associated with those landslides. [ABSTRACT FROM AUTHOR]

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time task fault-tolerant scheduling algorithm for a dynamic monitoring platform of distribution network operation under overload of distribution transformers. The proposed algorithm is based on wireless communication and mobile edge computing to address the challenges faced by distribution networks in handling the increasing load demand. For the considered system, we evaluate the system performance by analyzing the communication and computing latency, from which we then derive an analytical expression of system outage probability to facilitate the performance evaluation. We further optimize the system design by allocating computing resources for multiple mobile users, where a greedybased optimization scheme is proposed. The proposed algorithm is evaluated through simulations, and the results demonstrate its effectiveness in reducing task completion time, improving resource utilization, and enhancing system reliability. The findings of this study can provide a basis for the development of practical solutions for the dynamic monitoring of distribution networks. [ABSTRACT FROM AUTHOR]

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]

The C-RAN architecture has been prevailing in mobile networks for 5G and beyond. Along with the significant increase in the amount of mobile data, network operators must consider the spatio-temporal pattern of traffi c demand due to human mobility and lifestyles. It has been an important problem to efficiently deploy mobile networks avoiding the underutilization of cell capacities in off -peak times. To address this problem, in this paper we propose a concept of an adaptive C-RAN architecture toward the beyond 5G era. The proposed architecture enables flexible reconfi guration of the radio unit (RU) states and movable DU locations considering the changes in demand distribution. The DUs and RUs compose wireless relay fronthaul networks to forward fronthaul streams satisfying the strict latency requirements. This paper also proposes the joint scheduling algorithm to activate RUs, relocate DUs, and determine forwarding paths of fronthaul streams. It was confi rmed with computer simulations that mobile networks can be efficiently reconstructed with the proposed idea by activating/deactivating RUs and relocating/ migrating DUs. [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]

In this paper, we investigate resource allocation for IRS-assisted green multiuser multiple-input single-output (MISO) systems. To minimize the total transmit power, both the beamforming vectors at the access point (AP) and the phase shifts at multiple IRSs are jointly optimized, while taking into account the minimum required quality-of-service (QoS) of multiple users. First, two novel algorithms, namely a penalty-based alternating minimization (AltMin) algorithm and an inner approximation (IA) algorithm, are developed to tackle the non-convexity of the formulated optimization problem when perfect channel state information (CSI) is available. Existing designs employ semidefinite relaxation in AltMin-based algorithms, which, however, cannot ensure convergence. In contrast, the proposed penalty-based AltMin and IA algorithms are guaranteed to converge to a stationary point and a Karush-Kuhn-Tucker (KKT) solution of the design problem, respectively. Second, the impact of imperfect knowledge of the CSI of the channels between the AP and the users is investigated. To this end, a non-convex robust optimization problem is formulated and the penalty-based AltMin algorithm is extended to obtain a stationary solution. Simulation results reveal a key trade-off between the speed of convergence and the achievable total transmit power for the two proposed algorithms. In addition, we show that the proposed algorithms can significantly reduce the total transmit power at the AP compared to various baseline schemes and that the optimal numbers of transmit antennas and IRS reflecting elements, which maximize the system energy efficiency of the considered system, are finite. [ABSTRACT FROM AUTHOR]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

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]

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks. [ABSTRACT FROM AUTHOR]

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a ${\textstyle {1 \over {2k }}}$ -approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes. [ABSTRACT FROM AUTHOR]

The broadcast nature of wireless communications makes transmission susceptible to eavesdropping. Recently, physical-layer security has been extensively studied to secure the wireless transmissions, but the security is severely compromised when the eavesdropper has the superiority in the channel quality. Toward this issue, we in this paper propose a joint ARQ and network coding method exploiting the characteristics of both the physical layer and the link layer. Specifically, we relate the message to be transmitted in the current slot with the successfully decoded messages in previous slots, where the ARQ is employed at the legitimate side to guarantee the successful transmission and decoding. Once one or more received messages fail to be decoded at the eavesdropper, it further prevents the eavesdropper from decoding the transmissions in later slots and thus the message transmitted currently and later is secured at the legitimate side. Moreover, for the more disadvantageous case that the eavesdropper is geographically closer to the transmitter, we further propose a destination-aided network coding scheme, which secures private information no matter where the eavesdropper is. The closed-form expressions of intercept probability and loss probability of the private information are analytically presented. The simulation results are provided to confirm our theoretical findings. [ABSTRACT FROM AUTHOR]

Modeling the variations in the local mean received power, the shadow fading is a relatively understudied effect in the literature. The inaccuracy of the universally accepted lognormal model is shown in many works. However, proposing other statistical distributions, such as gamma, which are not stemmed from the natural underlying physical process, cannot provide sufficient insights. Conceding the physical process of multiple reflections generating the lognormal distribution, in this paper, we propose a generalized mixture model that can address the modeling inaccuracies observed with a single lognormal distribution that may not correctly represent empirical data sets. To show that lognormal mixture model can be used under any shadow fading condition, we prove that an arbitrary probability density function can accurately be represented by a mixture of lognormal random variables (RVs). One of the main issues associated with mixture models is the determination of the mixture components. Here, we propose two solutions. Our first solution is a Dirichlet-process-mixture-based estimation technique that can provide the optimum number of components. Our second solution is an expectation–maximization (EM) algorithm-based technique for a more practical implementation. The proposed model and solution approaches are applied to our empirical data set, where the accuracy of the mixture model is verified by using both confidence-based and error-vector-norm-based techniques. Concluding this paper, we provide outage and cellular coverage probability expressions, where we show that more accurate shadow fading models yield more realistic performance estimates. [ABSTRACT FROM PUBLISHER]

With the rapid development of cloud computing, the user-centric networks with the baseband unit pool have attracted a great deal of attentions in academic and industrial fields. However, limited fronthaul capacity and computation resource have become the bottlenecks inevitably. Thus, this paper investigates the power-efficient transmission in user-centric networks by considering both fronthaul capacity and computation resource constraints, where multiple access points (APs) and user equipments (UEs) are distributed. Specifically, a joint optimization of the beamforming vectors, AP-UE association strategy and transmission time is proposed to minimize the total power consumption (TPC). The formulated mixed integer non-linear problem (MINLP) is NP-hard. To address this problem, the MINLP is first transformed into a convex one via the successive convex approximation and semidefinite relaxation methods. Then, an iterative but effective algorithm is designed by using the property of the solution and applying the Lagrangian dual method. Simulation results show that the proposed algorithm converges rapidly and outperforms benchmark algorithms in terms of TPC. [ABSTRACT FROM AUTHOR]

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul. [ABSTRACT FROM AUTHOR]

In the incoming communication system, especially for the battery constrained Internet of Things devices, consumption of power resources will be a critical performance metric. This point shows importance when throughput minimal requirement and interference limit have been carried out. This paper investigates such a power allocation problem in a multiple-parallel-channel wireless system to minimize the sum power consumed by the entire system, while meeting the sum power constrains for each group of channels and the whole system as well as meeting the throughput constraints for each of the groups and the system. Sum power minimization itself is also a key issue for margin-adaptive loading. Resorting to geometric concepts, an algorithm named as the group virtual bottom power water-filling (GVB-PWF) is proposed to solve the problem, including the large-scale problems, which computes the exact solution with a low degree of the polynomial computational complexity. Optimality of the proposed algorithm is also proved strictly. To the best of our knowledge, no prior algorithm in the open literature offered such an optimal solution to the proposed problem, with the merit of exactness and efficiency. Simulation results demonstrate that the proposed power allocation algorithm uses less power about 25%, compared with the popular primal-dual interior-point method with the same amount of computations. [ABSTRACT FROM AUTHOR]

The rapidly growing mobile traffic calls for higher quality of service (QoS) and lower on-grid energy cost. Integrating energy harvesting to small-cell networks (SCNs) with caching has been regarded as a promising solution. However, considering the limited resources of the small-cell base stations (SBSs), how to cache contents and serve the mobile users (MUs) is a crucial problem. In this paper, we jointly design a green content caching and mobile user–base station (MU-BS) association mechanism in the SCNs, where the number of MUs’ content requests served by the SBSs is maximized. This mechanism maximizes the number of requests handled by the SBSs under the constraints of the available energy and backhaul capacity of the SBSs, while concurrently ensures the QoS of the MUs. First, the joint optimization problem is formulated, which is NP-hard. To decrease the complexity of the problem, we divide it into two subproblems: MU-BS association and content caching. But when the number of SBSs is large, these two subproblems cannot be efficiently solved by the conventional centralized approaches. Thus, we construct a two-dimensional-action weakly acyclic game to optimize these two subproblems distributedly and iteratively, in which the existence of the two-dimensional-action Nash equilibrium has been proved. Finally, we propose a two-dimensional iteration algorithm to solve the problem. The simulation results show that the proposed algorithm can achieve better performance when compared with the other existing algorithms. [ABSTRACT FROM AUTHOR]

We present a framework for random access that is based on three elements: physical-layer network coding (PLNC), signature codes, and tree splitting. In presence of a collision, physical-layer network coding enables the receiver to decode, i.e., compute, the sum of the packets that were transmitted by the individual users. For each user, the packet consists of the user’s signature, as well as the data that the user wants to communicate. As long as no more than $K$ users collide, their identities can be recovered from the sum of their signatures. This framework for creating and transmitting packets can be used as a fundamental building block in random access algorithms, since it helps to deal efficiently with the uncertainty of the set of contending terminals. In this paper, we show how to apply the framework in conjunction with a tree-splitting algorithm, which is required to deal with the case that more than $K$ users collide. We demonstrate that our approach achieves throughput that tends to 1 rapidly as $K$ increases. We also present results on net data-rate of the system, showing the impact of the overheads of the constituent elements of the proposed protocol. We compare the performance of our scheme with an upper bound that is obtained under the assumption that the active users are a priori known. Also, we consider an upper bound on the net data-rate for any PLNC-based strategy in which one linear equation per slot is decoded. We show that already at modest packet lengths, the net data-rate of our scheme becomes close to the second upper bound, i.e., the overhead of the contention resolution algorithm and the signature codes vanishes. [ABSTRACT FROM AUTHOR]

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

Digital television (DTV) signal has been recognized as a promising signal for navigation and positioning. However, due to the single frequency network (SFN) transmission within the European standard digital video broadcasting terrestrial (DVB-T) system, emitter confusion problem occurs in navigation and positioning, resulting in that a receiver is unable to know from which emitter a received signal comes. In this paper, we consider the wireless positioning with emitter confusion problem in DVB-T SFN networks. A joint wireless positioning and emitter identification algorithm is proposed, which is based on the expectation maximization (EM) method. The proposed algorithm is tested in a scenario, where signals are received from three to five emitters. Simulation results show that, relying on more than three emitters used in the tests for 2-D positioning, the EM-assisted positioning algorithm is feasible to achieve accurate positioning results in the existence of the emitter confusion problem. Our studies show that the performance achieved by the proposed algorithm approaches the Cramér-Rao bound. Furthermore, the proposed algorithm is effective to identify the DTV emitters, and the positioning performance is robust to the emitter identification error. Additionally, our methodology is general, and can be employed for time of arrival-based positioning in any SFNs. [ABSTRACT FROM PUBLISHER]

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization. [ABSTRACT FROM AUTHOR]

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility. [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]

Charging time and power transfer efficiency are the main challenges of wireless power transfer for electric vehicles. It is proposed in this paper to resolve both issues using the transformer induction concept as well as adaptive robotic technology. To this end, an autonomous robotic arm equipped with several sensors is mounted to the vehicle’s undercarriage that carries a receiving coil. In case that the vehicle is parked in a parking spot equipped with wireless power transmitters, the robot will locate the receiving coil on the right place that is above the transmitting core when the vehicle is parked. When the vehicle is driven on a road equipped with the wireless power transmission system, the proposed mechanism provides the driver with signals that indicate the most efficient driving trajectory. To achieve the maximum efficiency, an advanced mathematical adaptive algorithm based on the extremum seeking method is employed. In addition, silicon carbide metal-oxide semiconductor field-effect transistor is used in power converter to retain the frequency as high as possible to decrease the charging time. Through this method, the above issues and the problem of large non-permeable air gap and accordingly electromagnetic interference are resolved; while, the charging time is decreased dramatically. [ABSTRACT FROM AUTHOR]

Concurrent dual-band receivers use fewer RF components to flexibly receive signals in the dual bands. To further reduce the number of components, a compact concurrent dual-band receiver architecture has been demonstrated in this paper. It employs only a single down-conversion channel to down-convert the dual-band signals simultaneously. The down-converted signal is a relatively narrowband signal without the wide gap between the two bands. Hence, slower analog-to-digital converters can be adopted in the subsequent circuits to digitize the received signal. Due to the nonlinearity of the low-noise amplifier, there might be noticeable nonlinearity distortion and memory effects in the received dual-band signals. To ensure high signal quality, a blind postdistortion algorithm is proposed to cancel out the nonlinearity distortion in the two received signals adaptively. Subsequently, the blind finite-impulse response equalizers for each band are built based on the modified constant modulus algorithm and the decision-directed equalization method. In addition, the nonideal wireless channel response is also able to be compensated using the proposed algorithm. Experimental results have shown perfect postcompensation performances of the overall algorithms in the dual bands. [ABSTRACT FROM PUBLISHER]

Recent advances in loop interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Meanwhile, wireless virtualization has attracted a lot of attention from both academia and industry because it can provide more flexibility, diversity, and other benefits in the process of wireless network design, construction, operation, and management. In this paper, we introduce the idea of wireless virtualization into FDR cellular networks. Then, the problem of energy-aware virtual resource management is formulated as a three-stage Stackelberg game. The subgame perfect equilibrium for each stage is analyzed. In addition, the interplays of the three-stage game are discussed, and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models. [ABSTRACT FROM AUTHOR]

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive. [ABSTRACT FROM AUTHOR]

We study a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such a channel, quantize-map-and-forward (QMF) [and its generalization of noisy network coding (NNC) and short message NNC] achieves the cut-set upper bound within a constant additive gap, where the gap grows linearly with the number of relay stages $K$. This gap, however, may not be acceptable for practical communication systems with multihop transmissions (e.g., a wireless backhaul operating at high frequencies). Recently, we improved the capacity scaling by using a forward sliding-window (SW) decoding and by optimizing the quantization level at each relay, obtaining the gap that grows logarithmically as $\log {K}$. Furthermore, the improved scheme has lower decoding complexity and delay than the general QMF and NNC approaches. In this paper, we further improve the performance by presenting a mixed scheme in which each relay can perform either decode-and-forward (DF) or the improved QMF (with SW decoding) and can choose to perform rate-splitting to enable partial interference cancellation. In general, the optimization of the relay DF/QMF configuration is combinatorial. Nevertheless, we provide that a simple greedy algorithm finds an optimal configuration under some practically reasonable assumptions. We derive an achievable rate that is easily computable and show that the proposed mixed scheme outperforms the QMF-only schemes. We demonstrate that the performance improvement increases with $K$ , which indicates that the mixed scheme is indeed beneficial for multihop transmission. [ABSTRACT FROM AUTHOR]

In this paper, the target tracking problem is investigated for a class of wireless localization systems with degraded measurements and quantization effects. The measurement degradations induced by unreliable channels are characterized by a random parameter matrix, and the quantization effects are modeled by sector-bounded uncertainties. A recursive filtering algorithm is proposed in order to track the plant states as accurately as possible. An upper bound on the filtering error covariance is first derived and such an upper bound is then minimized by properly designing the filter gain at each sampling instant. The desired filter parameters are obtained by solving two sets of Riccati-like difference equations that are in a recursive form suitable for online application. Finally, a simulation experiment is carried out for the scenario of personnel localization/tracking problems in a mine industrial site and the effectiveness of the proposed filter method is demonstrated. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of encoding data into repeat-free sequences in which sequences are imposed to contain any k-tuple at most once (for predefined k). First, the capacity of the repeat-free constraint are calculated. Then, an efficient algorithm, which uses two bits of redundancy, is presented to encode length-n sequences for k = 2 + 2 log (n). This algorithm is then improved to support any value of k of the form k = a log (n), for 1 < a, while its redundancy is o(n). We also calculate the capacity of repeat-free sequences when combined with local constraints which are given by a constrained system, and the capacity of multi-dimensional repeat-free codes. [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 investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cellular/infostation integrated network that supports on-demand data service delivery over a wireless link to users in a high-speed train. For the requests with different lifetimes and prices that they are willing to pay, we develop the optimal packet schedule that aims at earning the maximum revenue. To this end, we formulate the problem as an integer linear program (ILP). The problem cannot be solved efficiently by existing ILP solvers with guaranteed polynomial-time complexity in the worst case. By exploring the special structure of the formulated ILP, we propose a novel Checker algorithm and prove that this algorithm is guaranteed to find the offline optimal schedule in polynomial time. Based on the relevant insights, we further develop a class of online Checker algorithms that require only causal knowledge of service demands and wireless channel capacities. It is established that these online algorithms have a worst-case competitive ratio of 1/2, i.e., total revenue earned by them is at least half as much as the revenue earned by an offline optimal schedule that knows the complete a priori knowledge of future requests and channel conditions. Simulation results are provided to demonstrate the merit of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a self-directed adaptive phased array control method using the low-frequency part of the signal for a millimeter-wave wireless personal area network, which can reduce the hardware requirement and power consumption compared with those of the conventional digital beamforming method. The signal from each element antenna of the antenna array is phase shifted and down converted to the baseband and then divided into two paths. The first paths are low-pass filtered to extract the low-frequency part of the signals for beamforming control. The second paths from all antennas are combined in-phase in an analog domain and then sampled by two high-speed A/D converters for demodulation. A beamforming algorithm using the sampled low-frequency part of the signal is also proposed. The beamforming calculation time is established as a function of the signal-to-noise ratio, the bandwidth of the low-frequency part, and the required phase control accuracy. The calculated values match the measured results. Using the IEEE 802.15.3c specifications with an eight-element array antenna, the calculation time is less than \5~\mu\s for initial beam establishment and less than \30~\mu\s for beam tracking. Therefore, high-speed beamforming is possible while reducing the power consumption. [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]

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS. [ABSTRACT FROM AUTHOR]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

Many wireless vision sensor networks (WVSNs) applications are characterized to have a low duty cycling. An individual wireless vision senor node (VSN) in WVSN is required to complete the tasks as quickly as possible. The execution of the tasks can be speeded up by exploiting the inherited parallelism in the tasks by using a hardware platform such as field-programmable gate array (FPGA). Traditionally, SRAM FPGAs are considered to be inefficient for duty cycled applications. This paper presents a low-complexity, energy-efficient, and reconfigurable VSN architecture based on SRAM FPGA using a design matrix, which includes tasks’ partitioning, a low-complexity background subtraction, bilevel coding, and duty cycling. The proposed VSN, referred to as SENTIOF-CAM, has been implemented on a prototype board and energy values of different states are measured for three real applications. The comparison results with existing solutions show that the proposed architecture with SRAM FPGA can achieve energy reduction of up to a factor of 69 as compared with software VSN solutions and approximately similar energy values to that for the FLASH FPGA-based VSN solutions. The lifetime based on measured energy values shows that, for a sample period of 5 min, a 3.2-years lifetime can be achieved with a battery of 37.44-kJ energy. In addition, the proposed solution offers a generic architecture with a smaller design complexity on a hardware reconfigurable platform and offers easy adaptation for a number of applications. [ABSTRACT FROM AUTHOR]