最新刊期
卷 8 ， 期 2 ， 2024
- 摘要：Device-to-device (D2D) communication is a short-range communication technology that can effectively improve the spectral efficiency of cellular networks. A two-stage subchannel and power joint allocation scheme was proposed to address the complex scenario of "many-to-many" communication in cellular networks (one subchannel could be assigned to multiple pairs of D2D user equipment (DUE), and one pair of DUE could also use multiple subchannels at the same time), considering the full frequency domain resource reuse in both uplink and downlink subchannels. In the first stage, a weighted bipartite graph matching-based resource allocation (WBGM-RA) algorithm was introduced. This algorithm allocated all subchannels to all cellular user equipment (CUE) to maximize CUE sum rate. In the second stage, an interference clustering-based resource allocation (IC-RA) algorithm was proposed, and the interference matrix was constructed according to the interference relations among UE sharing the same subchannel. Resources allocated to CUE were reallocated to DUE. Moreover, the transmit power of DUE was optimized to maximize the system sum rate while ensured that DUE did not cause serious interference to CUE. This study established a novel joint resource allocation for uplink and downlink subchannels, coupled with a mechanism for "many-to-many" channel reuse. This led to a substantial increase in spectrum access opportunities for DUE and overall spectrum efficiency in the network. Simulation results show that compared with the existing typical algorithm, this algorithm can effectively improve the system sum rate, increase the number of communication links in the system and increase the DUE access rate.关键词：cellular network;device-to-device communication;uplink-downlink resource sharing;channel allocation;power allocation348|766|0更新时间：2024-08-16
- 摘要：The industrial internet of things (IIoT) has great potential in achieving automation and intelligent production. However, existing networks struggle to meet the low-latency and high-reliability communication requirements in industrial control scenarios. Motivated by this fact, the power allocation problem of superimposed pilot (SP) for downlink short-packet transmission in IIoT was studied and a lower bound for achievable transmission rates with imperfect channel state information and maximum ratio transmission was derived. Furthermore, the downlink weighted sumrate maximization problem was formulated and transformed into a geometric programming problem aiming to optimize pilot and data power allocation via successive convex approximation method. Simulation results demonstrate the superiority of the proposed power optimization scheme of SP in short-packet transmission.关键词：IIoT;superimposed pilot;short-packet transmission;power allocation267|202|0更新时间：2024-08-16
- 摘要：With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.关键词：reinforcement learning;internet of things;random access;multi-base station network;slotted Aloha233|183|0更新时间：2024-08-16
- 摘要：In space communications, the signal faces long-distance transmission and a high-dynamic relative movement. The long-distance transmission results in a very low signal-to-noise ratio (SNR) and the high-dynamic relative movement causes a high-dynamic Doppler-shift on the carrier. To address the low SNR, the traditional acquisition method requires the long-time accumulation of many received signals. However, during the long-time accumulation, the high-dynamic Doppler-shift causes the serious energy dispersion problem. To solve the problem, a two-stage-sparse (TSS) algorithm was proposed to acquire the Doppler-shift. The proposed TSS algorithm firstly used the coarse acquisitions to construct a coarse sparse-search-range, then selected some large elements to construct a fine sparse-search-range, and finally searched the largest element as the acquisition result. Because the sparse-search-range only covers a narrow frequency range, the TSS algorithm excludes more noise elements, thus allowing the signal element to become the largest element. The theoretical analysis and simulation results show that the proposed TSS algorithm significantly increases the acquisition probability.关键词：space communication;low SNR;high-dynamic Doppler-shift;acquisition;energy dispersion140|130|0更新时间：2024-08-16
- 摘要：For the intelligent reflecting surface (IRS)-assisted multiple input single output (MISO) simultaneous wireless information and power transfer (SWIPT) system, the beam forming vector at the base station and the reflected beam forming vector of the IRS were jointly optimized, by considering the maximum transmit power of the base station, the unit modulus constraint of the IRS reflection phase shift matrix, and the minimum energy constraint of the energy receiver. The object was to maximize the spectrum efficiency. To solve the non-convex optimization problem, a deep deterministic policy gradient (DDPG) algorithm based on deep reinforcement learning was proposed. Simulation results show that the average reward of the DDPG algorithm is related to the learning rate. Under the condition of selecting the appropriate learning rate, the DDPG algorithm can obtain an average mutual information similar to that of the traditional optimization algorithm, but the running time is significantly lower than that of the traditional non-convex optimization algorithm. Even if the number of antennas and the number of reflective units are increased, the DDPG algorithm can still converge in a short period of time. This indicates that the DDPG algorithm can effectively improve the computational efficiency and is suitable for communication services with high real-time requirements.关键词：multiple input single output;simultaneous wireless information and power transfer;intelligent reflecting surface;beam forming;deep deterministic policy gradient213|162|0更新时间：2024-08-16
- 摘要：In recent years, the demand of express industry has increased rapidly, and the express industry is under increasing pressure. The unmanned aerial vehicle (UAV) delivery has become an effective supplement to vehicle delivery due to its low human cost, flexibility and convenience. However, UAVs are often limited by factors such as endurance and load capacity, requiring a low-cost and energy self-sustaining scheduling scheme for delivery and charging to support collaborative delivery of multiple UAVs. A two-stage self-sustaining multiple UAV cooperative delivery and charging scheduling scheme was proposed. The first stage aims at finding the delivery routes of UAVs to complete all delivery tasks in the region such that the number of UAVs was minimized under the energy and load capacity constraints of UAVs. The UAV delivery scheduling algorithm (UDSA) was proposed, and the approximation of UDSA was proved theoretically. The second stage aims to schedule the charging of UAVs with different arrival times to minimize the maximum charging completion time of all UAVs. An approximate UAV delivery scheduling algorithm (UCSA) was proposed to solve the problem. The simulation results show that, compared with the benchmark algorithm, UDSA can reduce the number of UAVs by 44.17% at most, and UCSA can reduce the maximum charging completion time by 18.87% at most.关键词：UAV;delivery scheduling;vehicle routing problem;wireless charging scheduling153|215|0更新时间：2024-08-16
- 摘要：In R16, 3GPP proposed that user devices could utilize the 4G/5G cellular networks to report the received signal strength indicator (RSSI) of Wi-Fi signal based on the existing minimization of drive test (MDT) technology, making it possible to measure the coverage probability of indoor Wi-Fi networks. However, existing measurement methods of network coverage probability based on the MDT require the spatial coordinates provided by the GPS. As GPS has poor indoor localization accuracy, it is not able to be applied to indoor Wi-Fi networks. A measurement method for network coverage probability based on clustering without spatial coordinates was proposed. The proposed method could distinguish RSSI measurement reported on different locations, with the fact that their statistics were similar at similar locations. The coverage probability was accurately measured by utilizing the clustering results without knowing the spatial coordinates. Experimental results show that the coverage probability measured by the proposed method is very close to the probability measured by the known spatial coordinates, and the accuracy is much higher than existing methods.关键词：coverage probability;Wi-Fi network;minimization of drive test;clustering algorithm;received signal strength indicator121|138|0更新时间：2024-08-16
- 摘要：The existing multi-satellite autonomous task planning methods lack of considering the integrity of area targets observation, resulting in the waste of a large number of observation resources. To solve this problem, an onboard coordinated task planning for multi-autonomous satellite to observe area targets was proposed. Firstly, a multi-satellite coordinated observation task planning model for area targets was established, and a multi-satellite autonomous coordinated planning framework based on contract network. Subsequently, based on this coordinated framework, the dynamic pricing-based bidding, tendering, and bid evaluation mechanisms were designed to realize the multi-satellite autonomous coordinated planning for the tasks of observing area targets. Finally, the simulation results show that, compared to the traditional multi-satellite coordinated planning method based on single-round bidding contract network and fixed price bidding contract network, the task revenue of the proposed method is increased by 60.40% and 29.07%, respectively.关键词：area target task;multi-satellite autonomous coordination;contract network protocol;task planning235|178|0更新时间：2024-08-16
- 摘要：In recent years, the energy requirements for devices in internet of things (IoT) applications have increased, making energy harvesting (EH) technology an important way to alleviate the energy shortage problem in edge computing and extend the battery life of devices. However, when there was insufficient renewable energy in the environment, the depletion of device power can cause task interruption and affect the performance of IoT. To solve this problem, a task offloading framework that combined energy harvesting and device-to-device (D2D) communication technology was proposed, using a deep reinforcement learning (DRL)-based edge collaborative offloading computing scheme to make autonomous decisions and solve resource allocation problems using simulated annealing algorithms to minimize the total cost of system operation. Simulation results on stable and extreme energy environments show that the proposed scheme can run stably and cost-effectively in single-user multiple-device scenarios.关键词：edge computing;energy harvesting;device-to-device communication;deep reinforcement learning105|180|0更新时间：2024-08-16
- 摘要：A wireless multi-channel hybrid data acquisition system was presented, consisting of two distinct components: a hardware system and a PC data acquisition analysis and processing software system. The hardware system was set with the number of surface electromyography (sEMG) signal acquisition channels and the number of inertial measurement unit (IMU) according to the target requirements by pressing a key, and sEMG signal transmission tasks and kinematics data acquisition tasks corresponding to the number of IMUs were created based on the FreeRTOS operating system, and the mutually exclusive signal volume technique was used to ensure that the data acquired by each task were transmitted to the PC software system via Wi-Fi. The data acquisition tasks were created based on the FreeRTOS operating system, and the kinematics tasks corresponding to the number of IMU sensors were created, and the mutually exclusive signal volume technique was used to ensure that the data acquired by each task was transmitted to the PC software system via Wi-Fi. The designed hybrid data acquisition system is more practical and scalable in the field of motor function rehabilitation for paralyzed limbs compared to the current commercial Trigno wireless based mixed signal acquisition system and other self-developed systems.关键词：hybrid data acquisition;sEMG signal;IMU sensor;wireless multichannel;muscle fatigue monitoring86|147|0更新时间：2024-08-16
- 摘要：In the context of the deep integration of new generation information technology and smart education, the research and application of smart classroom systems have received widespread attention. However, the current application of smart classroom systems is still in its early stages, lacking real-time feedback and real-time positive intervention functions for students. To solve this problem, a smart classroom system based on emotional learning was built, and edge computing was introduced to improve the real-time and intelligent level of the classroom, so as to achieve real-time feedback of students' emotional state during learning. Secondly, to improve the performance of the smart classroom system, convex optimization theory was utilized to optimize the allocation of system resources. Finally, through verification, the multiple resources joint optimization method of the smart classroom system can effectively reduce the maximum delay of device data collection and processing, greatly improve the real-time performance of emotional computing in the smart classroom system, and avoid blindly pursuing the minimum average delay, effectively avoiding the situation where a single user experienced poorly due to poor real-time performance. Overall, the system and algorithm achievements have reference and significance for the construction of future smart classrooms.关键词：emotional computing;smart classroom system;edge computing;multiple resources joint optimization;convex optimization188|264|0更新时间：2024-08-16
- 摘要：In large-scale Ad Hoc network, state sensing is a prerequisite for realizing global network view, which provides data support for network troubleshooting, routing decision and dynamic network topology planning. However, the existing single sensing mechanism cannot guarantee the timeliness of state information sensing, and will generate extra network overhead and reduce the network performance. To solve the above problems, an adaptive fast sensing strategy for large-scale Ad Hoc network states was proposed based on four-quadrant classification. Firstly, according to the difference of the delay sensitivity of network state information and network request frequency, it was classified based on the idea of four-quadrant graph. Secondly, fast sensing strategy was designed for network state information in different quadrants, and network state information was encapsulated into management frames to achieve embedded transmission and reduce network overhead. Finally, the simulation experiments show that the adaptive fast sensing method is superior to the single active reporting and request response strategy in terms of timeliness and information validity.关键词：large-scale Ad Hoc network;four-quadrant classification;adaptive fast sensing;embedded transmission109|163|0更新时间：2024-08-16
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