Applications where packetized video is streamed over the Internet, must be designed to achieve robustness to packet loss as well as compression efficiency. Whenever possible, the ideal solution to this problem is to jointly optimize the adaptation of the compression and error protection strategies to the network status, so as to minimize the expected end-to-end distortion of reconstructed video at the receiver. However, in the case of pre-compressed video streaming, compression is performed without knowledge of the network condition, and conversely the delivery is performed without access to the original signal. It is hence difficult for the transmitter to estimate and minimize the end-to-end distortion during delivery. This paper addresses this problem by deriving an algorithm which enables the transmitter, or other intermediate nodes, to estimate the overall end-to-end distortion while delivering pre-compressed video. This estimate fully accounts for the effects of (prior) quantization, packet loss and error propagation, as well as error concealment. The accuracy of the estimate is demonstrated by simulation results. The algorithm requires storage of minimal side-information that is computed during compression. The algorithm is of low complexity, and is applicable to virtually all coding techniques, including the standard (predictive) video coders. The paper also discusses the use of this estimate to adapt a variety of packet-loss resilience techniques for pre-compressed video streaming. The considerable potential gains of this approach are illustrated via the example of an FEC-based streaming video system.
This paper proposes a novel receiver mode switching scheme for simultaneous wireless information and power transfer (SWIPT) and the enabling transmitter design for a point-to-point multiple-input single-output (MISO) channel when the channel state information (CSI) is not available at the transmitter. The proposed scheme employs random beamforming at the transmitter to generate artificial channel fading to enable opportunistic energy harvesting at the receiver when the channel power exceeds a certain threshold. For the proposed scheme, we investigate the achievable average information rate and average harvested energy, as well as trade-offs between them in MISO fading channels. Compared to a reference scheme of periodic receiver mode switching without random transmit beamforming, the proposed scheme is shown to be able to achieve better rate-energy trade-off's when the harvested energy target is sufficiently large. Particularly, it is revealed that employing one single random beam for the proposed scheme is asymptotically optimal as the transmit power increases to infinity, and also performs the best with finite transmit power for high energy harvesting requirements of practical interests, thus leading to an appealing low-complexity implementation.
Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.
No abstract is provided for this article.
The past few years have witnessed a tremendous increase on the use of unmanned aerial vehicles (UAVs) in civilian applications, which call for high-performance communication between UAVs and their ground clients, especially when they are densely deployed. To achieve this goal, cellular base stations (BSs) can be leveraged to provide a new and promising solution to support massive UAV communications simultaneously in a cost-effective way. However, different from terrestrial communication channels, UAV-to-BS channels are usually dominated by the light-of-sight (LoS) link, which aggravates the co-channel interference and renders the spatial frequency reuse in existing cellular networks ineffective. In this paper, we consider the use of a directional antenna at each UAV to confine the interference to/from other UAV users within a limited region and hence improve the spatial reuse of the spectrum. Under this model, a UAV user may be temporarily blocked from communication if it cannot find any BS in its antenna main-lobe, or it finds that all BSs under its main-lobe are simultaneously covered by those of some other UAVs and hence suffer from strong co-channel interference. Assuming independent homogeneous Poisson point processes (HPPPs) for the UAVs' and ground BSs' locations respectively, we first analytically derive a closed-form upper bound for the UAV blocking probability and then characterize the achievable average spatial throughput of the cellular-enabled UAV communication network, in terms of various key parameters including the BS/UAV densities as well as the UAV's flying altitude and antenna beamwidth. Simulation results verify that the derived bound is practically tight, and further show that adaptively adjusting the UAV altitude and/or beamwidth with different BS/UAV densities can significantly reduce the UAV blocking probability and hence improve the network spatial throughput.
Local caching with device-to-device (D2D) communications has been recently introduced as an effective scheme for reducing the average download time of the mobile terminals (MTs). The MTs first cache the files in their local memories and then exchange the files with each other within the vicinity via D2D communications. Prior works have largely overlooked MTs' heterogeneity in file preferences and assume unselfish caching behaviors of the MTs. In this work, we practically divide the MTs into different groups according to their individual preferences over the files and propose optimal file caching strategies for self-interested MTs to reduce the average file download time. Assuming the knowledge of the social file preference for an intelligent group, we develop the optimal caching strategy for this group by formulating and solving a convex optimization problem. Closed-form solution for the problem is obtained, which is shown to follow a water-filling structure over the files. Finally, numerical examples are presented to show that the selfish caching of a group can be detrimental to both itself and the other intelligent groups.
A solution based on utility function is proposed for the load balancing of CDMA and WIFI wireless access networks.The first step is to acquire the key indicators of the network.Based on the network indicators,a game model will then be established.An optimal network resource allocation strategy will be developed through game model calculations,in order to balance the network load and achieve maximum benefits for the operators.The solution includes a C+W network load balancing diagram.Simulation results show the effectiveness of the algorithm,which can improve network utilization and achieve balanced C+W network loads.
When studying the gait of pheasants, an intermittent-flight bird, it is necessary to take into account changes in the gaits and hindlimb joint angles resulting from increases of speed. In this study, pheasant locomotion postures were recorded on a speed-variable treadmill with high-speed cameras. Firstly, kinematic analysis showed that the stride cycle of pheasants decreased and the stride length increased with increasing speed. The duty factor also decreased, but was less than 0.5 in only about 10% of measurements. Thus, pheasants are more inclined to choose the grounded running or walking gait in laboratory situations. Secondly, changes in the tarsometatarso–phalangeal joint angle and the intertarsal joint angle at touch-down, mid-stance and lift-off concomitant with speed variation were studied. Tarsometatarso–phalangeal joint angle was found not to be significantly affected by changes in speed, but changed over larger ranges than the intertarsal joint angle. Thirdly, the continuous changes in the joint angles were studied during a complete stride cycle. The curves shifted leftward with increasing speed. Finally, the changes at four main positions were analyzed with increasing speed.
Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the BS and its served users. In contrast, we propose in this paper a new integrated IRS BS architecture by deploying IRSs inside the BS antenna radome. Since the distance between the integrated IRSs and BS antenna array is practically small, the path loss among them is significantly reduced and the real time control of the IRS reflection by the BS becomes easier to implement. However, the resultant near field channel model also becomes drastically different. Thus, we propose an element wise channel model for IRS to characterize the channel vector between each single antenna user and the antenna array of the BS, which includes the direct (without any IRS reflection) as well as the single and double IRS-reflection channel components. Then, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum rate of the users. By considering two typical cases with/without perfect CSI at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS BS architecture over the conventional multi antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.
In this paper, we consider a challenging secure wireless sensing scenario where a legitimate radar station (LRS) intends to detect a target at unknown location in the presence of an unauthorized radar station (URS). We aim to enhance the sensing performance of the LRS and in the meanwhile prevent the detection of the same target by the URS. Under this setup, conventional stealth-based approaches such as wrapping the target with electromagnetic wave absorbing materials are not applicable, since they will disable the target detection by not only the URS, but the LRS as well. To tackle this challenge, we propose in this paper a new target-mounted IRS approach, where intelligent reflecting surface (IRS) is mounted on the outer/echo surface of the target and by tuning the IRS reflection, the strength of its reflected radar signal in any angle of departure (AoD) can be adjusted based on the signal's angle of arrival (AoA), thereby enhancing/suppressing the signal power towards the LRS/URS, respectively. To this end, we propose a practical protocol for the target-mounted IRS to estimate the LRS/URS channel and waveform parameters based on its sensed signals and control the IRS reflection for/against the LRS/URS accordingly. Specifically, we formulate new optimization problems to design the reflecting phase shifts at IRS for maximizing the received signal power at the LRS while keeping that at the URS below a certain level, for both the cases of short-term and long-term IRS operations with different dynamic reflection capabilities. To solve these non-convex problems, we apply the penalty dual decomposition method to obtain high-quality suboptimal solutions for them efficiently. Finally, simulation results are presented that verify the effectiveness of the proposed protocol and algorithms for the target-mounted IRS to achieve secure wireless sensing, as compared with various benchmark schemes.
In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is nonconvex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves a significant performance gain in terms of the number of SNs whose data are successfully collected, as compared with other benchmark schemes.
The method on the meteorological amendment of smoke-shading jamming is described.The process of the azimuth and horizontal amendment of the carrier is derived.The effect of the substance and surroundings on jamming is considered during analysis.The analysis on amending of passive jamming will enhance the integrated jamming effectiveness.
No abstract is provided for this article.
The ZigBee wireless sensor network has been already widely used in many areas of industrial control, home network and medical equipment. A wireless mesh data transmission network based on the chip CC2530 of TI and the ZigBee Protocol stack was established with the practical application, in which any two nodes can communicate directly. The experimental results show that the designed network has special features of the high speed of data transmission, low delay, fault tolerance and flexibility. Finally, application in the wireless intelligent light pen CMM measurement system was analyzed.
In order to improve the indoor positioning accuracy and reduce the impact of a reflected light on the positioning performance, based on the analysis of the existing RSS positioning algorithm, an improved positioning algorithm based on K-means clustering is proposed. According to the indoor visible light communication system model and optical power distribution, 10*10 receiving points are uniformly selected on the receiving surface of 5m*5m. Through the collection of the received optical power of each receiving point, the K-means clustering algorithm is used to classify 100 receiving points: the first cluster of points is less affected by a reflected light, and the second cluster of points is greatly affected by a reflected light. The second cluster of receiving points is weighted after classifying, and the simulation results show that the algorithm significantly reduces the room edge positioning error, and the positioning performance of the entire room is improved by 28%.
In intelligent reflecting surface (IRS) aided wireless communication systems, channel state information (CSI) is crucial to achieve its promising passive beamforming gains. However, CSI errors are inevitable in practice and generally correlated over the IRS reflecting elements due to the limited training with discrete phase shifts, which degrade the data transmission rate and reliability. In this paper, we focus on investigating the effect of CSI errors to the outage performance in an IRS-aided multiuser downlink communication system. Specifically, we aim to jointly optimize the active transmit precoding vectors at the access point (AP) and passive discrete phase shifts at the IRS to minimize the AP's transmit power, subject to the constraints on the maximum CSI-error induced outage probability for the users. First, we consider the single-user case and derive the user's outage probability in terms of the mean signal power (MSP) and variance of the received signal at the user. Since there is a trade-off in tuning these two parameters to minimize the outage probability, we propose to maximize their weighted sum with the optimal weight found by one-dimensional search. Then, for the general multiuser case, since the users' outage probabilities are difficult to obtain in closed-form due to the inter-user interference, we propose a novel constrained stochastic successive convex approximation (CSSCA) algorithm, which replaces the non-convex outage probability constraints with properly designed convex surrogate approximations. Simulation results verify the effectiveness of the proposed robust beamfoming algorithms and show their significant performance improvement over various benchmark schemes.
