Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
The resource allocation of the federated learning(FL)for unmanned aerial vehicle(UAV)swarm systems are investigated. The UAV swarms based on FL realize the artificial intelligence (AI) applications by means of distributed training on the basis of ensuring the security of private data. However, the direct application of the FL in UAV swarms will incur high overhead. Therefore, in this article, we consider the resource allocation problem in FL for UAV swarms. To avoid the high communication overhead between UAVs and the central server, we proposed an FL framework for UAV swarms based on mobile edge computing(MEC)in which model aggregation is migrated to edge servers. In the proposed framework, the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption. In order to minimize the total cost of FL, we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning (DRL). The simulation result shows that: 1) compared with the benchmark algorithm,the proposed algorithm can effectively reduce the total cost of FL;2)the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes.
Anomaly detection is an essential part of any practical system in order to remedy any malfunction and accident early to create a secure and robust system. Malicious users and malfunctioning cognitive radio(CR) devices may cause severe interference to legitimate users. However,there are no effective methods to detect spontaneous and irregular anomaly behaviors in sub-sampling data stream from wideband compressive spectrum sensing as an important function of a CR device.In this article,to detect anomaly utilization of spectrum from sub-sampled data stream, a multiple layer perceptron/feed-forward neural network(FFNN)based solution is proposed. The proposed solution would learn the pattern of legitimate and anomalous usages autonomously without expert’s knowledge. The proposed neural network (NN) framework has also shown benefits such as more than 80% faster detection speed and lower detection error rate.
With the expansion of satellite constellation, routing techniques for small-scale satellite networks have problems in routing overhead and forwarding efficiency. This paper proposes a vector segment routing method for large-scale multi layer satellite networks. A vector forwarding path is built based on the location between the source and the destination. Data packets are forwarded along this vector path, shielding the influence of satellite motion on routing forwarding. Then, a dynamic route maintenance strategy is suggested. In a multi layer satellite network, the low-orbit satellites are in charge of computing the routing tables for one area,and the routing paths are dynamically adjusted in the area in accordance with the network. The medium-orbit satellites maintain the connectivity of vector paths in multiple segmented areas. The forwarding mode based on the source and destination location improves the forwarding efficiency, and the segmented route maintenance mode decreases the routing overhead. The simulation results indicate that vector segment routing has significant performance advantages in end-to-end delay, packet loss rate, and throughput in a multi layer satellite network. We also simulate the impact of routing table update mechanism on network performance and overhead and give the performance of segmented vector routing in multi layer low-orbit satellite networks.
Frequency spectrum sharing between radar and communication systems has recently attracted substantial attention. We consider the coexistence between a massive multiple-input multiple-output (MIMO) downlink system and MIMO radar to enable the operation of these two systems with minimal mutual interference. Through an asymptotic analysis, we show that by using more antennas at the base station (BS), we can improve the performance of massive MIMO, while keeping the interference to the radar system unchanged.Additionally, if we use a large number of antennas at the BS and make the transmit power inversely proportional to the number of antennas, we can avoid the interference from the massive MIMO system to the radar system, with no compromise in the performance of the massive MIMO system. Closed-form expressions for the probability of detection of the radar system and the downlink spectral efficiency of the massive MIMO system, are derived. Furthermore, we propose a power allocation scheme which selects the transmit powers at the MIMO radar and BS to maximize the probability of detection for the MIMO radar. Interestingly,the optimal power allocation can be determined in closed-form. These results provide valuable insights into the practical coexistence between massive MIMO and radar systems.
The reconfigurable intelligent surface(RIS), which is composed of multiple passive reflective components,is now considered as an effective mean to improve security performance in wireless communications, as it can enhance the signal of legitimate users and suppress the power leakage at eavesdroppers by adjusting signal phases. In this paper,we maximize the downlink ergodic secrecy sum rate of a RIS-aided multi-user system over Rician fading channels, where we assume that only imperfect channel state information(CSI)is available at the base station(BS).Firstly,we obtain the deterministic approximate expression for the ergodic secrecy sum rate by resorting to the large-system approximation theory. Then the problem is formulated to maximize the downlink ergodic secrecy sum rate by optimizing the regularization coefficient of regularized zero-forcing (RZF) precoding and the phase-shifting matrix of the RIS. By using the particle swarm optimization (PSO) method, we propose an alternate optimization (AO) algorithm to solve this non-convex problem. Finally, the numerical simulations illustrate the accuracy of our large-system approximate expression as well as the effectiveness of the proposed algorithm.
We propose a Λ-type superconducting three-energy-level device-based communication system for extremely weak microwave communication scenarios, for example, long-distance deep-space communication. We provide a system diagram and propose the frame synchronization and power estimation approaches based on pre-defined synchronization sequences. Based on the microwave response characteristics of the superconducting three-energy-level system, we further investigate the optimization of synchronization sequence and information symbol modulation. We show that three-energy-level systems with weak Markovianity can be approximated using independent identical distribution methods to obtain optimal synchronization sequences, and the optimal modulation is asymmetric. The proposed system design and optimization approaches are evaluated by numerical results. Moreover, we investigate the performance of the three-energy-level communication system in the presence of interference. Simulation results show that the three-energy-level communication system can tolerate more than 10 dB interference compared to long term evolution(LTE)systems and achieve the same communication rate for the same bandwidth and temperature.
In millimeter-wave multiple-input multipleoutput (MIMO) systems, transmit antenna selection (TAS) can be employed to reduce hardware complexity and energy consumption when the number of antennas becomes very large. However, the traditional exhaustive search TAS tries all possible antenna combinations which causes high computational complexity. It may limit its application in practice. The main advantage of machine learning(ML)lies in the capability of establishing underlying relations between system parameters and objective, hence being able to shift the computation burden of real-time processing to the offline training phase. Based on this advantage, introducing ML to TAS is a promising way to tackle the high computational complexity problem. Although the existing ML-based algorithms try to approach the optimal performance, there is still a large room for improvement. In this paper, considering the secure transmission of the system, we model the TAS problem as a multi-class classification problem and propose an efficient antenna selection algorithm based on gradient boosting decision tree (GBDT), in which we consider the system security capacity and computational complexity as the optimization objectives. On the one hand, the system security performance is improved because its achievable security capacity is close to the traditional exhaustive search algorithm. On the other hand,compared with the exhaustive search algorithm and existing ML-based algorithms, the training efficiency is significantly improved with the complexity O(N), where N is the number of transmitting antenna. In addition,the performance of the proposed algorithm is evaluated in mmWave MIMO system by employing New York University simulator (NYUSIM) model, which is based on the real channel measurement. Performance analysis show that the proposed GBDT-based scheme can effectively improve the system secrecy capacity and significantly reduce the computational complexity.
The rejuvenation of non-geostationary orbit (NGSO) satellite communication holds the promise of seamless and ubiquitous broadband access from the space. However,the NGSO constellations must share the scarce radio spectrum resources with geostationary orbit(GSO) satellite systems, which results in dynamically changing and unevenly distributed interference to GSO systems. In this context, the ultra-large-scale NGSO constellation incurs a more complicated interference environment with GSO systems, which raises urgent demands on inter-system interference evaluation. In this case, we investigate the inter-system downlink interference from a NGSO satellite mega-constellation to a GSO earth station. Specifically, we consider the scenario where the NGSO and GSO earth stations are co-located,and apply a novel visibility analysis method in the interference modeling to reduce computation redundancy. The interference evaluation is then performed through comprehensive simulations,in which the Starlink constellation with more than 4 000 satellites is examined for the first time. The simulation results demonstrate various states of interference on the GSO earth station at different deployment locations. It reveals that the number of visible satellites could influence the angle between the main lobe directions of NGSO satellites and the GSO earth station antenna, which further affects the interference level.
Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account, which is made more likely for an attacker to determine the user’s true sensitive location and trajectory. To solve this problem,this paper presents a privacy budget allocation method based on privacy security level(PSL).Firstly, in a custom map, the idea of P-series is contributed to allocate a given total privacy budget reasonably to the initially sensitive locations.Then, the size of privacy security level for sensitive locations is dynamically adjusted by comparing it with the customized initial level threshold parameter μ. Finally, the privacy budget of the initial sensitive location is allocated to its neighbors based on the relationship between distance and degree between nodes. By comparing the PSL algorithm with the traditional allocation methods, the results show that it is more flexible to allocate a privacy budget without compromising location privacy under the same preset conditions.
