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Short packet communications play key roles in the Internet-of-Things. Conventional Shannon’s coding theorem is not applicable for short packet communications, and the achievable rate in the finite blocklength regime is related to the blocklength and the decoding error probability. Contrary to conventional physical layer security, wireless information surveillance assumes that the communication users are suspicious users and the eavesdroppers are legitimate monitors, and proactive eavesdropping via jamming has been proposed to improve the eavesdropping performance. Existing works on proactive eavesdropping ignored the scenario when the suspicious users adopt the short packet communications. Thus, this paper tries to develop effective proactive eavesdropping schemes suitable for the short packet suspicious communications. Under the truncated channel inversion power allocation and the modified constant power allocation policies at the suspicious source, the problems of jamming power allocation for maximizing the monitoring success probability subject to the average transmit power constraint at the monitor are investigated. Based on the Dinkelbach-type algorithm, the Lagrange duality method and a novel tractable approximate expression for the decoding error probability, we derive suboptimal solutions to the problems. Simulation results demonstrate the effectiveness of the proposed solutions compared to various benchmark algorithms in existing literature. [ABSTRACT FROM AUTHOR]

In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better. [ABSTRACT FROM AUTHOR]

Recently, wireless powered cooperative cognitive radio networks (CRNs), which combine the technologies of radio frequency (RF) energy harvesting and CR, have drawn great attention. In such networks, energy cooperation between the cognitive users (CUs) and the wireless powered primary users (PUs) can be performed, where the CUs can charge the PUs wirelessly in exchange for the spectrum access. Specifically, energy cooperation and information transmission is executed in two phases, where the CUs transmit their data signals and the PUs harvest energy from these signals in the first phase, and the PUs transmit their data using the harvested energy in the second phase. In particular, we consider two multiple access schemes for the CUs, namely non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA). For both NOMA and TDMA, the PU sum-rate maximization problems under the minimum CU sum-rate constraint are first simplified by exploring particular problem structure, then are transformed to convex problems, and finally are solved optimally. The PU sum-rates of the two schemes are compared theoretically as well as numerically. It is revealed that the circuit power consumption at the CUs, the required minimum CU sum-rate, and the PU energy harvesting sensitivity and saturation thresholds play key roles in the PU performance comparison of the two schemes. [ABSTRACT FROM AUTHOR]

In this paper, spectrum sharing incentive for legitimate wireless information surveillance at the physical layer is investigated. The concept behind this is that authorized government agencies want to eavesdrop the communication between a suspicious source-destination pair, and stimulate the help of another source-destination pair (referred to as secondary users) looking for transmission opportunities. We thus propose to let the secondary receiver act as a monitor to eavesdrop the suspicious communication and let the secondary transmitter act as a friendly jammer to improve the eavesdropping performance, such that they are granted to share the spectrum of the suspicious users, as a reward. To guarantee the eavesdropping performance, the successful eavesdropping probability is required to be larger than that with passive eavesdropping. The problems of jointly optimizing the transmit power of the secondary transmitter and the successive interference cancellation (SIC) decoding order at the secondary receiver to maximize the average secondary transmission rate and minimize the secondary transmission outage probability are investigated. Two scenarios are considered for the availability of the channel state information (CSI). The first scenario assumes perfect CSI, and the optimal strategies for the transmit power control and the SIC decoding ordering are derived. The second scenario assumes partial CSI, where the instantaneous CSI of the secondary transmission link and the statistical CSI of the links related to the suspicious communication are available, and the optimal strategies for maximizing an approximate average secondary transmission rate and minimizing an approximate secondary transmission outage probability are proposed. Extensive simulation results validate the effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

This article presents a hybrid robust sequential fusion estimation (HRSFE) method for moving-target localization in mobile asynchronous wireless sensor networks (WSNs). The estimation error or inconsistency, caused by the uncertainties of sampling, communication, and sensor-node position, is considered fully and compensated by incorporating the sensor-node position with uncertainty into the state vector as well as introducing an adaptive fading factor into the cubature Kalman filter (CKF) to maintain the estimation consistency. In addition, a QR-decomposition-based square-root form of the cubature strong localization filter (SR-CSLF) is proposed to improve the accuracy and stability of the CSLF. Moreover, an HRSFE method is designed to estimate the augment state of the WSN-assisted moving-target-localization system, and the proposed estimation method combines the merits of both the SR-CSLF and SR-CKF effectively and is able to deal with the uncertainties of sampling, communication, and sensor-node position in a unified framework. Simulations and experiments of a WSN-assisted moving-target-localization system under various uncertainty settings are employed to demonstrate the effectiveness and superiorities of the proposed method. [ABSTRACT FROM AUTHOR]

A very-large-scale integration capable of extracting motion features from moving images in real time has been developed employing row-parallel and pixel-parallel architectures based on the digital pixel sensor technology. Directional edge filtering of input images is carried out in row-parallel processing to minimize the chip real estate. To achieve a real-time response of the system, a fully pixel-parallel architecture has been explored in adaptive binarization of filtered images for essential feature extraction as well as in their temporal integration and derivative operations. As a result, self-speed-adaptive motion feature extraction has been established. The chip was designed and fabricated in a 65-nm CMOS technology and used to build an object detection system. Motion-sensitive target image localization was demonstrated as an illustrative example. [ABSTRACT FROM AUTHOR]

We present a directional-edge-based object tracking system based on a field-programmable gate array (FPGA) that can process 640\,\times\,480 resolution video sequences and provide the location of a predefined object in real time. Inspired by biological principle, directional edge information is used to represent the object features. Multiple candidate regeneration, a statistical method, has been developed to realize the tracking function, and online learning is adopted to enhance the tracking performance. Thanks to the hardware-implementation friendliness of the algorithm, an object tracking system has been very efficiently built on an FPGA, in order to realize a real-time tracking capability. At the working frequency of 60 MHz, the main processing circuit can complete the processing of one frame of an image (640\,\times\,480 pixels) in 0.1 ms in high-speed mode and 0.8 ms in high-accuracy mode. The experimental results demonstrate that this system can deal with various complex situations, including scene illumination changes, object deformation, and partial occlusion. Based on the system built on the FPGA, we discuss the issue of very large-scale integrated chip implementation of the algorithm and self initialization of the system, i.e., the autonomous localization of the tracking object in the initial frame. Some potential solutions to the problems of multiple object tracking and full occlusion are also presented. [ABSTRACT FROM PUBLISHER]

The combination of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) has been recognized as a critical method to improve the sixth generation networks performance. In this paper, a distributed RIS-aided downlink multiple-input multiple-output (MIMO) NOMA systems with discrete phase shifts are studied, where the channel directions from base station to paired users can be manipulated with the assistance of RISs by employing the concept of signal alignment. In order to ensure base station can flexibly serve some users with NOMA and others users with spatial division multiple access, a unified precoder and decoder are provide to cancel inter-cluster interference. Subsequently, the channel statistics over Nakagami- $m$ fading channels are derived for near and far users. In particular, considering the cascade channel gain may exists two different cases, Beaulieu series is further adopted to characterize their corresponding cumulative distribution function. In what follows, the outage probability and ergodic rate for three situations within one cluster are derived by utilizing the obtained channel statistics, respectively. Based on the derived results, we also analyze the diversity order and high signal-to-noise ratio slope to provide essential insights into the considered systems. Finally, simulation results are presented to reveal that: 1) selecting the setting of 3-bits resolution can realize a near-aligned performance for our proposed systems; 2) the cascade channel statistic caused by RIS can be evaluated with any number of RIS element and any channel gain via Beaulieu series. [ABSTRACT FROM AUTHOR]

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician $\mathcal K$ -factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is promising in enabling large-scale model training by massive devices without exposing their local datasets. However, due to limited wireless resources, traditional cloud-based FL system suffers from the bottleneck of communication overhead in core network. Fueled by this issue, we consider a hierarchical FL system and formulate a joint problem of edge aggregation interval control and resource allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the training latency, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into an edge aggregation interval control problem and a resource allocation problem, and solved by an iterative optimization algorithm. Specifically, given the resource allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. The problem of resource allocation including training time allocation and bandwidth allocation is solved separately based on the convex optimization theory. Simulation results show that the proposed algorithm, compared to the benchmarks, can achieve higher learning performance with lower training latency, and is capable of adaptively adjusting the edge aggregation interval and the resource allocation strategy according to the training process. [ABSTRACT FROM AUTHOR]

In this paper, a generalized uplink cell-free massive multiple-input multiple-output (mMIMO) system with hardware imperfections is investigated. Specifically, the hardware impairments consist of multiplicative phase drifts, additive distortion, and amplified thermal noise. Leveraging on a generic hardware impairment model, closed-form expressions for the channel estimation and achievable rate are presented. Based on these tractable results, we explore the uplink achievable rate surrounding the key design parameters, i.e., the channel estimation error, the number of antenna arrays, the local oscillator (LO) setup, and the variance of phase drifts. Aside from this, we also design a power optimization algorithm to maximize the sum-rate under variant channel use. The weighted minimum mean square error (MMSE) methodology is introduced to iteratively determine the solution in closed form. Numerical simulations validate the analytical analyses and examine the effectiveness of the proposed power control algorithm. [ABSTRACT FROM AUTHOR]

The paper studies a wireless-powered full-duplex non-orthogonal multiple access (NOMA) system with one base station and two groups of near and far users, where the near users can be regarded as the self-energy recycling relays to assist the far users. In particular, the locations of the near and far users are modeled as the uniformly distributed and homogeneous Poisson Point Process respectively. From the beneficial perspective of the near users, we propose a novel power allocation scheme, which can utilize the partial channel state information to switch the transmission mode dynamically between cooperative NOMA and non-cooperative NOMA. Further, since the users’ locations have a significant influence on our proposed power allocation scheme, three different user selection strategies namely random near user and random far user (RNRF), optimal near user and nearest far user (ONNF), and optimal near user and farthest far user (ONFF) are investigated to carry out NOMA pairing. Firstly, the RNRF allows all of users to communicate with the base station by a fair opportunity compared to the others strategies, and then a two-stage user selection method is proposed by considering the actual states of near users to determine an optimal near user who can maximize the outage performance of far user for ONNF and ONFF strategies. In addition, outage probabilities, diversity orders, and system throughputs are derived to characterize the performance of three strategies. Finally, numerical results are shown to verify that not only our proposed power allocation scheme can improve the quality of service of near users but also user selection strategies can fully exploit multi-user diversity in comparison to the existing work. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is considered as a promising technology, which can effectively control the phase of signals with low-cost reflective elements. In this paper, we investigate the RIS-assisted D2D communication system under Nakagami- $m$ fading. Overlay mode and underlay mode are discussed respectively. To characterize the performance of proposed communication system, we propose novel closed-form analytical expressions for outage probability under two modes, respectively. Based on the analytical results, we derive the diversity order of outage probability. We show that under overlay D2D mode, the diversity order depends on the number of elements and fading parameters. For the underlay D2D case, 0 diversity order is obtained due to the existence of interference. Furthermore, we study the ergodic achievable rate for each mode and the corresponding tight bounds are derived in closed form. High SNR slopes achieved by users under two modes are also discussed. We observe a high SNR slope of order 1 for overlay D2D mode and 0 for underlay D2D mode. In addition, we also investigate the energy efficiency (EE) of the considered system. Numerical results show that the optimal phase shift proposed in our paper enhance system performance effectively comparing to the traditional D2D communication and random phase shift scheme. Significant EE gains can be obtained for low to middle SNR region by passive reflectors with reconfigurable phase. Unlike relay system, the nearer RIS to the D2D transmitter/receiver, the better performance is obtained. [ABSTRACT FROM AUTHOR]

Integrating millimeter Wave (mmWave) and long-term evolution (LTE) sub-6 GHz bands to provide wireless services for high-speed rails (HSRs) is a promising solution to balance the ever-rising service demand and operational cost. In this paper, we optimize the network interface selection of the mmWave and LTE interfaces for HSR communications, to minimize the operational cost while satisfying the service requests of passengers. The network interface selection problem is formulated as a finite-horizon discrete-time Markov decision process (MDP) problem. Based on the file requests and associated deadlines of a passenger and the train schedule, we solve the MDP problem ahead-of-time by using dynamic programming (DP) and achieve optimal interface selection for each waypoint along the railways. Further, by exploiting the subadditivity and superadditivity of the considered problem, we reveal that an optimal switch of interfaces is only triggered at the moment when the remaining undelivered data or the elapsed time reaches their specific thresholds. Based on the thresholds, the optimal interface selection can be directly achieved for every waypoint, hence eliminating the need for DP and substantially reducing the computational complexity. The proposed algorithms are evaluated with practical train schedules. Simulation results show that the proposed algorithms can substantially reduce the operational cost by 35% in our considered simulation setting, as compared to existing alternatives. [ABSTRACT FROM AUTHOR]

This paper investigates secure transmission in a wireless powered cooperative communication network (WPCCN) over Nakagami- $m$ fading channels, where multiple intermediate energy harvesting (EH) nodes with finite energy storage are deployed to assist the secure transmission between source and destination in the presence of an eavesdropper. Specifically, the intermediate EH nodes first harvest and accumulate energy from the source's signal and then forward the confidential signal or emit jamming signal to interfere the eavesdropper according to actual requirements. In order to exploit the intermediate EH nodes for secure improvement, considering both energy storage status and channel gains, three relay-and-jammer selection schemes at the intermediate nodes are proposed, namely: energy threshold based best-relay and random-jammer (ETBR), energy threshold based random-relay and best-jammer (ETRB), and energy threshold based best-relay and best-jammer (ETBB). For understanding the impact of critical parameters on the secrecy performance, we first capture the evolution of the EH nodes’ storages using finite-state Markov chain (FSMC) and then derive the analytical expressions for hybrid outage probability (HOP) and secrecy throughput (ST). Furthermore, numerical results demonstrate that our proposed selection schemes outperform the existing schemes (bR-rJ, rR-bJ, bR-bJ) due to the consideration of energy accumulation and storage status. In addition, there is a suitable value of the energy threshold for each proposed scheme to achieve the optimal secrecy performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner–Ziv coding. Different decoding strategies are also considered. This paper aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue, because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach’s algorithm is proposed to find the optimal solution to the approximate problem. In summary, this paper provides an economic solution to the challenge of constrained fronthaul capacity and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem. [ABSTRACT FROM AUTHOR]

This communication presents a new design of a quadri-polarization-agile aperture-coupled patch antenna array for 60 GHz applications. Four polarization states, namely, vertical linear polarization, horizontal linear polarization, left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP) can be flexibly reconfigured by dynamically switching the excitation of the corresponding port. More importantly, as the LP and CP radiations are achieved by using differential feeding and sequential-rotation feeding, respectively, the array exhibits wide impedance and axial ratio (AR) bandwidths, high polarization purity as well as symmetrical pattern with the main beam fixed at boresight. The −10 dB impedance bandwidths are better than 11.6% (57–64 GHz) for the four polarization states. The measured AR bandwidths are from 58 to more than 64 GHz for both the LHCP and RHCP. The good radiation performance together with the quadri-polarization-agile capability of this new array, as demonstrated in the communication, underlines its suitability for the future 5G communication systems, especially in a multipath propagation environment. [ABSTRACT FROM AUTHOR]

In this paper, we study the network average power consumption minimization problem in a two-tier heterogeneous network by optimally tuning the activation ratio of micro base stations (BSs) under the quality of service (QoS) constraints of the network mean queueing delay and the network signal-to-interference ratio (SIR) coverage. With the consideration of dynamic packets arrivals, each BS can either be busy or be idle depending on its queueing status. The network performance is thus critically determined by the traffic intensity of each BS. With the assumption of universal frequency reuse, the average traffic intensity of each tier is characterized by a set of fixed-point equations, which can be solved by a proposed iterative method. By using the approximation that BSs of the same tier have the same SIR coverage, the cumulative distribution function of the traffic intensity of each tier is further obtained. On that basis, the network average power consumption per area, the network mean queueing delay, and the network SIR coverage are characterized. Numerical results demonstrate that if the idle power coefficient is below a certain threshold, then the optimal activation ratio equals the one to minimize the network average power consumption per area; otherwise, the optimal activation ratio can be obtained according to the QoS constraints. It is further shown that universal frequency reuse outperforms spectrum partitioning in terms of both the network average power consumption and the network SIR coverage in the considered scenario. [ABSTRACT FROM AUTHOR]

We study a novel communication technique, ambient backscatter, that utilizes radio frequency signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing noncoherent schemes, we here design the semi-coherent detection, where channel-related parameters can be obtained from unknown data symbols and a few pilot symbols. In order to obtain a benchmark for overall detection, we first derive a maximum likelihood detector assuming a complex Gaussian ambient source, and the closed-form bit error rate (BER) is computed. To release the dependence on prior knowledge of the ambient source, we next derive a type of robust design, called an energy detector, with the ambient signal being either complex Gaussian or phase shift keying (PSK). The closed-form detection thresholds, analytical BERs, and outage probability are provided correspondingly. Interestingly, the complex Gaussian source would cause an error floor, while the PSK source does not, which brings nontrivial indication of constellation design as opposed to popular Gaussian-embedded literatures. We also propose an effective approach to estimate detection-required parameters rather than channels themselves. Numerical simulations are finally presented to verify theoretical results. [ABSTRACT FROM PUBLISHER]

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), load balancing among different tiers can be effectively achieved by a biased user association scheme with which each user chooses to associate with one base station (BS) based on the biased received power. In contrast to previous studies, where a BS always has packets to transmit, we assume in this paper that incoming packets intended for all the associated users form a queue in the BS. In order to find the delay limit of the network to support real-time service, we focus on the delay optimization problem by properly tuning the biasing factor of each tier. By adopting a thinned Poisson point process model to characterize the locations of BSs in the busy state, an explicit expression of the average traffic intensity of each tier is obtained. On that basis, an optimization problem is formulated to minimize a lower bound of the network mean queuing delay. By showing that the optimization problem is convex, the optimal biasing factor of each tier can be obtained numerically. When the mean packet arrival rate of each user is small, a closed-form solution is derived. The simulation results demonstrate that the network queuing performance can be significantly improved by properly tuning the biasing factor. It is further shown that the network mean queuing delay might be improved at the cost of a deterioration of the network signal-to-interference ratio coverage, which indicates a performance tradeoff between real-time and non-real-time traffic in HetNets. [ABSTRACT FROM AUTHOR]

This paper presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an $M$-antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies with quantized channel state information (CSI), as well as, perfect CSI. Then, novel lower and upper bounds are derived which apply for an arbitrary number of antennas and are shown to be sufficiently tight to the Monte-Carlo results. Based on these results, we examine in detail three important special cases including: high signal-to-noise ratio (SNR), weak interference between cellular link and D2D link, and BS equipped with a large number of antennas. We also derive asymptotic expressions for the ergodic achievable rate for these scenarios. Based on these results, we obtain valuable insights into the impact of the system parameters, such as the number of antennas, SNR and the interference for each link. In particular, we show that an irreducible saturation point exists in the high SNR regime, while the ergodic rate under IC strategy is verified to be always better than that under BF strategy. We also reveal that the ergodic achievable rate under perfect CSI scales as {\log _2}M, whilst it reaches a ceiling with quantized CSI. [ABSTRACT FROM AUTHOR]

In wireless device-to-device (D2D) networks, devices are reluctant to forward packets because of limited energy and possible delays for their own data. The incentive mechanisms that motivate devices to constitute direct communication for wireless multimedia quality optimality in D2D systems have been overlooked in the past. In this paper, we propose a new low-complexity distributed game-theoretic source selection and power control scheme that enhances the multimedia transmission quality with latency constraints. This approach has two major contributions. First, the proposed approach optimally selects the most beneficial source devices by analyzing the interactions between the base station's (BS's) rewarding strategies (denoted by price) and the devices' contributing behaviors (denoted by transmission power) using a Stackelberg game model. Second, optimal transmission power is adjusted for each selected source device in D2D networks by deriving Stackelberg equilibrium, wherein the BS and the device both achieve maximum utility. Computer simulations demonstrate that significant improvement in D2D multimedia transmission quality can be obtained by deploying the proposed scheme. [ABSTRACT FROM PUBLISHER]

In mobile communications, two potentially ground-breaking ideas have emerged in recent years: massive MIMO antenna technology, which delivers enormous information rates, and wireless energy transfer (WET), which makes remote charging of mobile users possible. This article realizes the huge potential of combining the two for high wireless information transfer (WIT) and WET. In particular, to maximize the synergy, the distributed version of massive MIMO (known as massive distributed antenna system, MDAS) is advocated to go with the joint wireless information and energy transfer (JWIET) system capable of both WIT and WET. We present the opportunities in MDAS-JWIET, and discuss research trends in MDAS with several architectures involving WET and WIT. [ABSTRACT FROM PUBLISHER]

Camera global motion estimation is critical to the success of video stabilization. This paper presents an effective and robust feature based motion estimation method. In the proposed approach, feature points are collected from input video sequences based on Speeded Up Robust Features (SURF). Random Samples Consensus (RANSAC) is used to remove local motion vectors and incorrect correspondences. In the global motion estimation, a particle filter is used to estimate the weight of feature points, solving the issue of Different Depth of Field (DDOF) for feature points. Then, the weighted least square (WLS) algorithm is applied to obtain the global motion estimation. Finally, a Kalman filter estimates the intentional motion, and the unintentional motion is compensated to obtain stable video sequences. Experimental results show that the proposed algorithm has the characteristics of high precision and good robustness. [ABSTRACT FROM PUBLISHER]