In this paper, we consider a secondary link sharing the spectrum with a primary link in a fading cognitive radio (CR) network. Instead of applying the conventional interference power constraint at the primary user (PU) receiver for the secondary user (SU) to protect the primary transmission, we propose a new constraint on the maximum tolerable outage probability for the PU due to the SU transmission. Under the assumption that perfect instantaneous channel state information (CSI) on the SU channel, the channel from the SU transmitter to PU receiver, and the PU channel is available at the SU transmitter, we derive the optimal power allocation strategies to achieve the ergodic capacity of the SU fading channel. It is shown by simulations that the proposed power allocation strategies can achieve substantial capacity gain for the SU over that based on the conventional interference power constraint, for the same PU outage probability loss.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Unmanned aerial vehicle (UAV)-enabled communication is a promising technology to extend coverage and enhance throughput for traditional terrestrial wireless communication systems. In this paper, we consider a UAV-enabled wireless sensor network (WSN), where a multi-antenna UAV is dispatched to collect data from a group of sensor nodes (SNs). The objective is to maximize the minimum data collection rate from all SNs via jointly optimizing their transmission scheduling and power allocations as well as the trajectory of the UAV, subject to the practical constraints on the maximum transmit power of the SNs and the maximum speed of the UAV. The formulated optimization problem is challenging to solve as it involves non-convex constraints and discrete-value variables. To draw useful insight, we first consider the special case of the formulated problem by ignoring the UAV speed constraint and optimally solve it based on the Lagrange duality method. It is shown that for this relaxed problem, the UAV should hover above a finite number of optimal locations with different durations in general. Next, we address the general case of the formulated problem where the UAV speed constraint is considered and propose a traveling salesman problem (TSP)-based trajectory initialization, where the UAV sequentially visits the locations obtained in the relaxed problem with minimum flying time. Given this initial trajectory, we then find the corresponding transmission scheduling and power allocations of the SNs and further optimize the UAV trajectory by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Finally, numerical results are provided to illustrate the spectrum and energy efficiency gains of the proposed scheme for multi-antenna UAV data harvesting, as compared to benchmark schemes.
Intelligent reflecting/refracting surface (IRS) is envisioned as a promising technology to reconfigure wireless propagation environment for enhancing the communication performance, by smartly controlling the signal reflection/refraction with a large number of tunable passive elements. In particular, the application of IRS in high-mobility scenarios can convert wireless channels from fast fading to slow fading, thus achieving more reliable communications. In this paper, we first provide an overview of the new applications and opportunities of IRS in high-mobility communications. Next, we present two practical strategies for deploying IRS to aid high-mobility communications, namely, roadside IRS versus vehicle-side IRS, and compare their different channel characteristics, handover requirements, and deployment costs. Then, the main issues in designing IRS-aided high-mobility communications, including node discovery, mode switching, beam alignment/tracking, handover, and multiuser scheduling are discussed for both IRS deployment strategies. Moreover, numerical results are presented to demonstrate the potential performance gains of IRSs in vehicular communications. Finally, new research directions are pointed out for future work.
This paper proposes a power control strategy for wind and solar power generation systems based on hybrid energy storage. In order to improve energy utilization, reduce the number of charge and discharge of the energy storage device, and give full play to the advantages of the energy storage device. The hydrogen generating device is set to run at constant power, and the supercapacitor(SC) and energy storage battery(Bat) compensate for the lack of power and complement the energy storage. Each system unit coordinates work through control strategies to balance power fluctuations on the bus. Studies have shown that under the conditions of energy shock and load fluctuations, the DC voltage can be kept stable, effectively solving the problem of abandoning wind and light, and improving energy utilization.
This letter studies a new wireless information and power transfer coexisting system, where two uncoordinated energy and information sources simultaneously transmit to the same receiver for energy harvesting and information reception, respectively. In such a setup, the information decoder could suffer from severe signal distortion due to the simultaneously received energy signal that is usually orders-of-magnitude stronger than the desired information signal. To tackle this practical issue, we propose a novel lens antenna array-enabled receiver, which exploits the angle-of-arrival-dependent energy focusing capability of lens array for the spatial separation of energy and information signals. In addition, an optimal power splitting-based scheme is proposed by taking into account the signal distortion effect at the receiver, which is applicable for both lens and conventional antenna arrays. Numerical results show that the proposed lens array receiver significantly outperforms that with the conventional uniform planar array in terms of an achievable rate-energy tradeoff.
Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the expressions of the distribution metrics of the aggregate received power at a typical SN. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of the energy beams and the number of SNs to be charged. Depending on different SN task requirements, the optimal AD-WPT can maximize the average received power or the active probability of the SNs, respectively. It is shown that both the maximum average received power and the maximum sensor active probability increase with the increased deployment density and transmit power of the PBs, and decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared with the traditional omnidirectional WPT.
The necessity,advantages and resistance of offshore wind power in Zhejiang province are analyzed combined with its current situation of economic development and geographical and climatic characteristics.The key technologies in offshore wind power construction are analyzed.Meanwhile,the offshore wind power type selection,offshore substation access principles,access voltage grade selection and cable laying considerations for the actual situation in Zhejiang are provided.
This paper investigates the price-based resource allocation strategies for the uplink transmission of a spectrum-sharing femtocell network, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band as the macrocell. Assuming that the macrocell base station (MBS) protects itself by pricing the interference from the femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and the femtocells subject to a maximum tolerable interference power constraint at the MBS. Especially, two practical femtocell channel models: sparsely deployed scenario for rural areas and densely deployed scenario for urban areas, are investigated. For each scenario, two pricing schemes: uniform pricing and non-uniform pricing, are proposed. Then, the Stackelberg equilibriums for these proposed games are studied, and an effective distributed interference price bargaining algorithm with guaranteed convergence is proposed for the uniform-pricing case. Finally, numerical examples are presented to verify the proposed studies. It is shown that the proposed algorithms are effective in resource allocation and macrocell protection requiring minimal network overhead for spectrum-sharing-based two-tier femtocell networks.
The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active ("on") APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g. network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.
This paper studies the design of an estimation system where a remotely observed source sequence is to be communicated through a noisy channel to an estimator. The remote node is assumed to have the capability of harvesting, and, subject to a capacity limit, storing energy from its ambient environment. The focus is on various transmit power-allocation strategies that minimize the mean square error at the estimator for such an energy harvesting estimation system as the fluctuation of harvested energy presents a unique challenge compared with a traditional battery powered system. We first establish the optimality of uncoded transmission for such a system. Two types of side information (SI) at the transmitter are then considered in this paper: noncausal SI (energy harvested in the past, present, and future) and causal SI (energy harvested in the past). For the case where noncausal SI is available and battery storage is unlimited, it is shown that the optimal power allocation amounts to a simple "staircase-climbing" procedure, where the power level follows a nondecreasing staircase function. For the case where battery storage has a finite capacity, the optimal power-allocation policy can also be obtained via standard convex optimization techniques. Dynamic programming (DP) is used to optimize the allocation policy when only causal SI is available. The issue of unknown transmit power at the receiver is also addressed for both the causal and noncausal SI cases. Finally, to make the proposed solutions practically more meaningful, two heuristic schemes are proposed; these schemes are largely motivated by the structure of the solution to the DP formulation but with much reduced computational complexity. Numerical examples are provided to examine the complexity-performance tradeoff of various power-allocation strategies.
Massive multiple-input multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations (BSs) brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the enormous gain of massive MIMO and yet reduce its cost to an affordable level, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, while the location of the focal area varies with the angle of arrival (AoA) of the wave. Therefore, in practical scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled system provides two new benefits, namely energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper we analytically show that the average received signal-to-noise ratio (SNR) in both the single-user and multiuser uplink transmissions can be strictly improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency (RF) chains. Numerical results are provided to corroborate our analysis.
