We present a statistical model for the characterization of ultra-wideband (UWB) channels for the outdoor office environment. The bandwidth is from 3-6 GHz and measurements are done for the line-of-sight (LOS) and non-line-of-sight (NLOS) cases. Small scale effects are modeled by the Saleh-Valenzuela (S-V) model with modifications on the ray arrival times and amplitude statistics to fit the empirical data. The ray arrival times are described by a mixture of Poisson processes while the amplitude statistics are follow a Nakagami distribution with the m parameter following a lognormal distribution. The parameters for the cluster and ray power-decay time constants, cluster and ray arrival times, mean excess delay and RMS excess delay are also extracted to define the channel model completely.
In this paper, we study the anti-jamming problem in device-to-device (D2D) communication. D2D communication will be interfered by cellular users because of multiplexing uplink. When faced with malicious interference from outside, it is necessary to carry out reasonable interference control while ensuring the communication between cellular users and D2D users. Aiming at this problem, a power control communication anti-jamming algorithm based on Soft Actor-Critic (SAC) is proposed. By modeling the transmitter of D2D users as an agent, the overall throughput of the system is improved and the influence of external malicious interference is reduced while ensuring the signal-to-noise ratio of D2D users and cellular users. Compared with DQN (Deep Q-network) and random methods, the proposed algorithm can obtain higher system throughput and faster convergence speed in the face of sweep interference, and can show better anti-jamming effect.
In order to satisfy compatibility between multi-band orthogonal frequency division multiplexing (MB-OFDM) Ultra-Wideband cognitive radio systems and narrowband system, polynomial interference cancellation coding (PCC) is introduced. Simulation indicates that based on this method, the side-lobe signal of MB-OFDM decreases quickly, and then the frequency notch is deep enough. In addition, relationship between the number of sub-carrier turned off and width of frequency notch is given.
An innovative routing protocol named AODV-COG is proposed in this paper. Our protocol not only assign an appropriate interface to a route to efficiently make use of the spectrum in cognitive wireless mesh networks but also select an appropriate path which brings high-throughput to route packets. The proposed routing protocol is based on the classic AODV protocol and requires some changes to the NS-2 simulator to simulate the cognitive wireless mesh network conditions. Based on simulation results, AODV-COG successfully exploits the dynamic spectrum to improve the throughput of the network and does well in finding a path with higher throughput among the paths with the same hop counts.
Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.
In Cognitive Radio Networks, cognitive radio users have to sense and occupy the free channel, and continuously check the channel status. Once primary user needs to utilize this channel, the transmission must stop as not to cause interference, which significantly decreases the performance of TCP. In this paper, we propose an improved transport protocol, called TCP-CReno. This new protocol can obtain the current channel status by interacting with Mac layer and modify the transmission status at source. Simulations in NS2 indicate that TCP-CReno increases the sending rate, reduces the retransmission ratio at the source, and improves the whole performance.
research-article Power Control for Cell-free Massive MIMO Based on AFSA Share on Authors: Yulin Lai Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile , Xuebin Sun Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile , Dianjun Chen Beijing University of Posts and Telecommunications, China Beijing University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 130Pages 1–3https://doi.org/10.1145/3469213.3470333Online:18 August 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
This paper provides a exploration of the application of low earth orbit satellite networks (LSN) in the context of sixth-generation(6G) mobile communication technology. The primary focus of this research is to address issues related to congestion control and routing within satellite networks. We introduces ECSAC-DRO, a novel algorithm that leverages deep reinforcement learning (DRL) to optimize routing and congestion control. Experimental results demonstrate that the ECSAC-DRO algorithm, taking into consideration the high-dimensional characteristics of LSN and incorporating elements from the DRL-based Soft Actor-Critic (SAC) algorithm, excels in providing flexible control over LSN congestion levels and bandwidth utilization. It outperforms traditional methods like Dijkstra, making it a promising and forward-looking solution that lends support to the future development of 6G communication technology.
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