This paper studies the capacity of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channels with multiple eavesdroppers having single/multiple antennas. It is known that the MIMO secrecy capacity is achievable with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The MIMO secrecy capacity computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by standard convex optimization techniques. To handle this difficulty, we explore a relationship between the MIMO secrecy channel and the recently developed MIMO cognitive radio (CR) channel, in which the multi-antenna secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called ``interference temperature (IT)'' constraints. By constructing an auxiliary CR MIMO channel that has the same channel responses as the MIMO secrecy channel, we prove that the optimal transmit covariance matrix to achieve the secrecy capacity is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Based on this relationship, several algorithms are proposed to solve the non-convex secrecy capacity computation problem by transforming it into a sequence of CR spectrum sharing capacity computation problems that are convex. For the case with single-antenna eavesdroppers, the proposed algorithms obtain the exact capacity of the MIMO secrecy channel, while for the case with multi-antenna eavesdroppers, the proposed algorithms obtain both upper and lower bounds on the MIMO secrecy capacity.
Currently, ice melting devices for distribution networks are not convenient for large-scale use in mountainous areas with harsh terrain due to their large size. For some small devices, they cannot cover all common power distribution wire types due to their limited ice-melting length. In this paper, the key components of the device are compared and analyzed to achieve the continuous adjustment of output current and voltage in a wide range to meet the ice melting needs of different types of wires and different lengths of lines. Affected by the short-circuit impedance and rated capacity of the station distribution, the allowable values of harmonic currents of different distribution are inconsistent. This paper analyzes the filter from the aspects of design and load selection to enable the device to meet the harmonic requirements under different distribution capacities. Finally, detailed harmonic analysis and calculation are carried out through the pscad modeling and a Skyworth Dewe-5000 power quality analyzer test.
In this letter, we study efficient channel estimation for an intelligent reflecting surface (IRS)-assisted orthogonal frequency division multiplexing (OFDM) system to achieve minimum training time. First, a fast channel estimation scheme with reduced OFDM symbol duration is proposed for arbitrary frequency-selective fading channels. Next, under the typical condition that the IRS-user channel is line-of-sight (LoS) dominant, another fast channel estimation scheme based on the novel concept of sampling-wise IRS reflection variation is proposed. Moreover, the pilot signal and IRS training reflection pattern are jointly optimized for both proposed schemes. Finally, the proposed schemes are compared in terms of training time and channel estimation performance via simulations, as well as against benchmark schemes.
In the emerging hybrid electricity market, mobile network operators (MNOs) of cellular networks can make day-ahead energy purchase commitments at low prices and real-time flexible energy purchase at high prices. To minimize electricity bills, it is essential for MNOs to jointly optimize the day-ahead and real-time energy purchase based on their time-varying wireless traffic load. In this paper, we consider two different MNOs coexisting in the same area, and exploit their collaboration in both energy purchase and wireless load sharing for energy cost saving. Specifically, we propose a new approach named energy group buying with load sharing, in which the two MNOs are aggregated as a single group to make the day-ahead and real-time energy purchase, and their base stations (BSs) share the wireless traffic to maximally turn lightly-loaded BSs into sleep mode. When the two MNOs belong to the same entity and aim to minimize their total energy cost, we use the two-stage stochastic programming to obtain the optimal day-ahead and real-time energy group buying jointly with wireless load sharing. When the two MNOs belong to different entities and are self-interested in minimizing their individual energy costs, we propose a novel repeated Nash bargaining scheme for them to negotiate and share their energy costs under energy group buying and load sharing. Our proposed repeated Nash bargaining scheme is shown to achieve Pareto-optimal and fair energy cost reductions for both MNOs.
The paper presents an numerical study of the hydraulic and cavitating characteristics of a slot hydrofoil at the angle of incidence of 6°. Results indicate that the performance of this slot hydrofoil is better than the original hydrofoil at non-cavitation condition, but deteriorates sharply once cavitation occurred. To improve the performance, a new splitting scheme was put forward and optimization research was carried out at the same indicent angle, numerical results show that the optimized slot hydrofoil achieves better hydraulic and cavitation performances.
In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to IRS in each candidate site and from this IRS to any grid in the target area in terms of IRS deployment parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, assuming an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branch-and-bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves near-optimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off than other baseline IRS deployment strategies.
Multi-antenna or multiple-input multiple-output (MIMO) technique can significantly improve the efficiency of radio frequency (RF) signal enabled wireless energy transfer (WET). To fully exploit the energy beamforming gain at the energy transmitter (ET), the knowledge of channel state information (CSI) is essential, which, however, is difficult to be obtained in practice due to the hardware limitation of the energy receiver (ER). To overcome this difficulty, under a point-to-point MIMO WET setup, this paper proposes a general design framework for a new type of channel learning method based on the ER's energy measurement and feedback. Specifically, the ER measures and encodes the harvested energy levels over different training intervals into bits, and sends them to the ET via a feedback link of limited rate. Based on the energy-level feedback, the ET adjusts transmit beamforming in subsequent training intervals and obtains refined estimates of the MIMO channel by leveraging the technique of analytic center cutting plane method (ACCPM) in convex optimization. Under this general design framework, we further propose two specific feedback schemes termed energy quantization and energy comparison, where the feedback bits at each interval are generated at the ER by quantizing the measured energy level at the current interval and comparing it with those in the previous intervals, respectively. Numerical results are provided to compare the performance of the two feedback schemes. It is shown that energy quantization performs better when the number of feedback bits per interval is large, while energy comparison is more effective with small number of feedback bits.
For standard AODV routing protocol choose the smallest end-to-end path for a target node hops as the source node of the path to the destination,but the use of minimum-hop routing path routing protocol selected is not,in some cases,the optimal path.In this paper,based on the standard AODV,choice of a maximum data transfer rate of the path as the choice of routing parameter,referred to as HDR_AODV.Designed the RREQ and RREP message format of HDR_AODV,achieved the function of HDR_AODV in the OPNET simulation platform,and through experiments,from the delay,throughput,data dropped such as network performance verified HDR_AODV better than standard AODV.
This paper studies a new mobile edge computing (MEC) setup where an unmanned aerial vehicle (UAV) is served by cellular ground base stations (GBSs) for computation offloading. The UAV flies between a give pair of initial and final locations, during which it needs to accomplish certain computation tasks by offloading them to some selected GBSs along its trajectory for parallel execution. Under this setup, we aim to minimize the UAV's mission completion time by optimizing its trajectory jointly with the computation offloading scheduling, subject to the maximum speed constraint of the UAV, and the computation capacity constraints at GBSs. The joint UAV trajectory and computation offloading optimization problem is, however, non-convex and thus difficult to be solved optimally. To tackle this problem, we propose an efficient algorithm to obtain a high-quality suboptimal solution. Numerical results show that the proposed design significantly reduces the UAV's mission completion time, as compared to benchmark schemes.
No abstract is provided for this article.
This study intended to investigate the impact of substitution rule change on the physical performance in World Cup tournaments. Running data from the 2022 and 2023 FIFA World Cups were used for analysis. The results show that substitutes have much more sprinting, high-speed running and moderate speed running per minute than starters (p < 0.05), with similar magnitudes of differences for the men's (28.4–47.6%) and women's World Cup (23.4–59.5%). Substitutes in winning vs losing teams had higher sprint distance (3.7 vs 2.8 m· min−1) and number of high-speed running (1.70 vs 1.58 times· min−1) in men's World Cup and higher sprint distance (3.3 vs 2.5 m· min−1) in women's World Cup. In the men's World Cup, substituted midfielders had a higher frequency and greater distance covered with high-speed running (1.85 vs 1.57 and 1.44 times· min−1; 10.1 vs 8.9 and 7.8 m· min−1) and higher sprint frequency (0.74 vs 0.71 and 0.62 times· min−1) compared to forwards and defenders, which was not the case for the Women's World Cup. Together, this study underlines the importance of substitutions for the team running performance in modern elite football under the new substitution rules, and practitioners need to pay attention to the use of substitute players under the new rules.
According to the wideband channel with the sparse characteristics of time frequency doubly selective fading, and the sparse energy mainly concentrating on a few clusters, this paper presents the compressed sensing algorithm, that is cluster-regularized adaptive matching pursuit (CRAMP) based on cluster characteristics. Through analyzing the time frequency doubly selective channel in orthogonal frequency division multiplexing (OFDM) system, and using the time domain and frequency domain correlation in channel, the algorithm uses the 2D-DFT as the observation matrix in compressed sensing channel. And then utilizing clustering characteristics, it estimates for each cluster rather than estimates for each element in channel. The simulation results show that the CRAMP algorithm in channel reconstruction accuracy and bit error ratio (BER) respects are superior to conventional compressed sensing ones.
The recent advance in radio-frequency (RF) wireless energy transfer (WET) has motivated the study of wireless powered communication network (WPCN), in which distributed wireless devices are powered via dedicated WET by the hybrid access-point (H-AP) in the downlink (DL) for uplink (UL) wireless information transmission (WIT). In this paper, by exploiting the cognitive radio (CR) technique, we study a new type of CR enabled secondary WPCN, called cognitive WPCN, under spectrum sharing with the primary wireless communication system. In particular, we consider a cognitive WPCN, consisting of one single H-AP with constant power supply and distributed users, shares the same spectrum for its DL WET and UL WIT with an existing primary communication link, where the WPCN's WET/WIT and the primary link's WIT may interfere with each other. Under this new setup, we propose two coexisting models for spectrum sharing of the two systems, namely underlay and overlay based cognitive WPCNs, depending on different types of knowledge on the primary user transmission available at the cognitive WPCN. For each model, we maximize the sum-throughput of the cognitive WPCN by optimizing its transmission under different constraints applied to protect the primary user transmission. Analysis and simulation results are provided to compare the sum-throughput of the cognitive WPCN versus the achievable rate of the primary user in two coexisting models. It is shown that the overlay based cognitive WPCN outperforms the underlay based counterpart, thanks to its fully cooperative WET/WIT design with the primary WIT, while it also requires higher complexity for implementation.
