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Integrating unmanned aerial vehicles (UAVs) into future wireless systems such as the fifth-generation (5G) cellular network is anticipated to bring significant benefits for both UAV and telecommunication industries. Generally speaking, UAVs can be used as new aerial platforms in the cellular network to provide communication services for terrestrial users, or become new aerial users of the cellular network served by the terrestrial base stations. Due to their high altitude, UAVs usually have dominant line-of-sight (LoS) channels with the ground nodes, which, however, pose new security challenges to future wireless networks with widely deployed UAVs. On one hand, UAV-ground communications are more prone than terrestrial communications to eavesdropping and jamming attacks by malicious nodes on the ground. On the other hand, compared to malicious ground nodes, malicious UAVs can launch more effective eavesdropping and jamming attacks to terrestrial communications. Motivated by the above, in this article, we aim to identify such new issues from a physical-layer security viewpoint and propose novel solutions to tackle them efficiently. Numerical results are provided to validate their effectiveness and promising directions for future research are also discussed.
We consider the channel estimation issues for two-way relay network (TWRN) that employs orthogonal frequency division multiplexing (OFDM) modulation. We propose a two-phase training protocol for channel estimation, which is compatible with two-phase data transmission scheme associated with TWRN. It will be seen that channel estimation in TWRN is quite different from that in the traditional point-to-point system or even that in the one-way relay network (OWRN). The identifiability issue of the channel estimation, which particularly exists for TWRN, is studied. Simulation results corroborate the effectiveness of the proposed method.
A composite generation and transmission system is such a power system which consists of generators, transformers, buses, circuit breakers, protective relaying, auto reclosure and transmission lines. In this paper the judgment of system failure mode and the definitions of risk indices, the mathematical model of sub systems and the implementation of algorithms are discussed. The results of example calculation show that this method is correct and feasible.
This letter studies the achievable rates of the two-path relay channel (TPRC), where two half-duplex relays successively in turn forward messages from a source to a destination. It is shown that the conventional analogue and digital relaying schemes incur non-negligible throughput losses due to a socalled "inter-relay-interference (IRI)" phenomenon. This letter presents a new digital relaying scheme at the source transmitter to mitigate the IRI by applying the "dirty-paper-coding (DPC)" technique. It is shown that the proposed scheme achieves the capacity of the symmetric TPRC.
Selling spectrum resources to mobile virtual network operators (MVNOs) is popular among primary network operators (POs) for increasing licensed spectrum utilization. An MVNO seeks transmission opportunities (TXOPs) on the PO’s licensed spectrum channel(s) by spectrum sensing. Since licensed primary users (PUs) often have higher transmission priority, TXOPs available to the MVNO are directly determined by PUs’ transmission behaviors. In this paper, we present the first study that uses a pricing scheme to regulate PUs’ transmission behaviors so that TXOPs for an MVNO are improved, meanwhile quality-of-service (QoS) of PUs can be guaranteed as well. Our idea is to design a non-uniform pricing scheme that regulates PUs to transmit based on a time-varying non-uniform transmission cost. We model the optimal pricing problem as a hierarchical game where the interaction between the PO and PUs is modeled as a Stackelberg game and the spectrum random access among PUs is modeled as a non-cooperative game. We first solve the non-cooperative game among PUs, as a building block, then we embed its outcome to a Markov Chain Monte Carlo (MCMC) framework to solve the whole optimal pricing problem. We strictly shows that there could be multiple optimal pricing schemes for a PO. Comprehensive simulations confirm the theoretical analysis, and valid the effectiveness of our proposed scheme.
Local-sky star recognition algorithm is a process of recognizing the extracted stars in image by making use of the prior rough attitude of star sensor in celestial sphere. In order to improve the detection and response performance of star sensor working in dynamic condition, ICCD is applied to imaging stars. However, image taken by ICCD has more non-Gaussian noise and the energy of imaging star is unstable. So a local-sky star recognition algorithm using spatial triangular relationship as matching features is supposed to deal with the difficulties. In the first place, an index array is designed according to Guide Triangles, which is applied to construct Guide Triangle Index List. In the second place, a general directing range of star sensor boresight is calculated according to FOV of star sensor and the output of inertial guidance system, and then, the candidate Guide Triangles set in above region is obtained rapidly. In the third place, construct image triangle patterns by applying position and energy of the extracted stars in the image, and then match the image triangle patterns with the above candidate Guide Triangles set for two stages, until N(N≥2) groups of successfully matched triangles pairs with smallest matching deviations sum are obtained. At the last, the recognized Guide Stars have to be matched posterior referring to the principle of simulated sky image, and the recognition results of image stars are all obtained. The proposed algorithm has compact Guide Database structure, rapid local-sky guide triangles obtaining, and good recognition correction percentage, even it has worse star location precision and more false stars. The simulation tests are performed to validate the relative efficiency and adaptation of the algorithm.
This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive radio network with wireless energy harvesting (EH). We consider that the secondary receiver is equipped with a FD radio and acts as a FD hybrid access point (HAP), which aims to collect information from its associated EH secondary transmitter (ST) and relay the signals. The ST is assumed to be equipped with an EH unit and a rechargeable battery such that it can harvest and accumulate energy from radio frequency (RF) signals transmitted by the primary transmitter (PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS) protocol for the considered system. In the proposed protocol, thanks to its FD capability, the HAP can receive the PT's signals and transmit energy-bearing signals to charge the ST simultaneously, or forward the PT's signals and receive the ST's signals at the same time. We derive analytical expressions for the achievable throughput of both primary and secondary links by characterizing the dynamic charging/discharging behaviors of the ST battery as a finite-state Markov chain. We present numerical results to validate our theoretical analysis and demonstrate the merits of the proposed protocol over its non-cooperative counterpart.
This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' rates over the conventional proper or circularly symmetric complex Gaussian signaling. In particular, for the pseudo-covariance optimization, the semidefinite relaxation (SDR) technique is applied to provide a high-quality approximate solution. For the special case of two-user MISO-IC, the SDR technique yields the optimal pseudo-covariance solution.
Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser system, where single-antenna users distributed in a coverage area are served by a base station (BS) equipped with multiple movable subarrays. We begin by presenting a spatially non-stationary channel model to capture the near-field effects, including positiondependent large-scale channel gains and line-of-sight visibility. To evaluate system performance, we further derive a closedform approximation of the expected weighted sum rate under maximum ratio combining (MRC), revealing that optimizing XLMA placement enhances user channel power gain to increase desired signal power and reduces channel correlation to decreases multiuser interference. Building upon this, we formulate an antenna placement optimization problem to maximize the expected weighted sum rate, leveraging statistical channel conditions and user distribution. To efficiently solve this challenging non-linear binary optimization problem, we propose a polynomial-time successive replacement algorithm. Simulation results demonstrate that the proposed XL-MA placement strategy achieves nearoptimal performance, significantly outperforming benchmark schemes based on conventional fixed-position antennas.
The performance of wireless communication is fundamentally constrained by the limited battery life of wireless devices, whose operations are frequently disrupted due to the need of manual battery replacement/recharging. The recent advance in radio frequency (RF) enabled wireless energy transfer (WET) technology provides an attractive solution named wireless powered communication (WPC), where the wireless devices are powered by dedicated wireless power transmitters to provide continuous and stable microwave energy over the air. As a key enabling technology for truly perpetual communications, WPC opens up the potential to build a network with larger throughput, higher robustness, and increased flexibility compared to its battery-powered counterpart. However, the combination of wireless energy and information transmissions also raises many new research problems and implementation issues to be addressed. In this article, we provide an overview of state-of-the-art RF-enabled WET technologies and their applications to wireless communications, with highlights on the key design challenges, solutions, and opportunities ahead.
Characterizing the fundamental tradeoffs for maximizing energy efficiency (EE) versus spectrum efficiency (SE) is a key problem in wireless communication. In this paper, we address this problem for a point-to-point additive white Gaussian noise (AWGN) channel with the transmitter powered solely via energy harvesting from the environment. In addition, we assume a practical on-off transmitter model with non-ideal circuit power, i.e., when the transmitter is on, its consumed power is the sum of the transmit power and a constant circuit power. Under this setup, we study the optimal transmit power allocation to maximize the average throughput over a finite horizon, subject to the time-varying energy constraint and the non-ideal circuit power consumption. First, we consider the off-line optimization under the assumption that the energy arrival time and amount are a priori known at the transmitter. Although this problem is non-convex due to the non-ideal circuit power, we show an efficient optimal solution that in general corresponds to a two-phase transmission: the first phase with an EE-maximizing on-off power allocation, and the second phase with a SE-maximizing power allocation that is non-decreasing over time, thus revealing an interesting result that both the EE and SE optimizations are unified in an energy harvesting communication system. We then extend the optimal off-line algorithm to the case with multiple parallel AWGN channels, based on the principle of nested optimization. Finally, inspired by the off-line optimal solution, we propose a new online algorithm under the practical setup with only the past and present energy state information (ESI) known at the transmitter.
In upstream vectored DSL systems using zero-forcing generalized decision feedback equalizers (ZF-GDFE), different decoding orders cause performance tradeoffs among the users. In this paper, these tradeoffs are characterized by formulating optimization problems with practical constraints. Lagrange dual decomposition and a two-step algorithm are used to solve the dual problems optimally with the computational complexity linear in the number of DMT tones. However, solving the tonal subproblem, which is shared by all the proposed optimization problems, associates with a high-complexity exhaustive search of K\ orderings, where K is the number of users. Thus, this paper proposes two low-complexity algorithms in order to find suboptimal orderings: successive ordering search (SOS) with complexity O(K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) and modified greedy search (MGA) with complexity O(K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). Numerical results show that MGA performs well enough in finding the achievable rate region. For problems related to feasibility check, SOS is suitable for closely approximating the optimal solution.
In order to optimize and manage the network resources,this document presents the core network and access network QoS several means to solve the problem.And approved the backbone layer,the convergence layer and the network access layer three different levels to the different technical means to achieve NGN bearer network QoS strategy that final adoption in the business side of access,network QoS strategy side of the core network business services quality assurance.
This paper studies a wireless communication system with a rotary-wing unmanned aerial vehicle (UAV) dispatched to communicate with multiple ground nodes (GNs). With limited on-board energy available at the UAV, we aim to maximize the weighted minimum of the communication throughput of the GNs, by jointly designing the UAV trajectory and communication resource allocation. The formulated problem is difficult to be directly solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first propose a simple fly-hover-communicate protocol, where the UAV successively visits a set of hovering locations and at each of them communicates with one corresponding GN. By leveraging the classic travelling salesman problem (TSP) and convex optimization techniques, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations. To further improve the performance, we consider the general scenario where the UAV also communicates while flying, and under a given UAV path, we find the optimal time allocation by solving a linear programming (LP) problem. Numerical results show the significant performance gains of the proposed designs over benchmark schemes.
